Date: 2021-07-27 12:23:27 CEST, cola version: 1.9.4
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First the variable is renamed to res_rh.
res_rh = rh
The partition hierarchy and all available functions which can be applied to res_rh object.
res_rh
#> A 'HierarchicalPartition' object with 'ATC:skmeans' method.
#> On a matrix with 10389 rows and 2881 columns.
#> Performed in total 11100 partitions.
#> There are 48 groups under the following parameters:
#> - min_samples: 29
#> - mean_silhouette_cutoff: 0.9
#> - min_n_signatures: 335 (signatures are selected based on:)
#> - fdr_cutoff: 0.05
#> - group_diff (scaled values): 0.5
#>
#> Hierarchy of the partition:
#> 0, 2881 cols
#> |-- 01, 1273 cols, 2797 signatures
#> | |-- 011, 485 cols, 890 signatures
#> | | |-- 0111, 198 cols, 64 signatures (c)
#> | | |-- 0112, 168 cols, 218 signatures (c)
#> | | `-- 0113, 119 cols, 755 signatures
#> | | |-- 01131, 45 cols (b)
#> | | |-- 01132, 39 cols (b)
#> | | `-- 01133, 35 cols (b)
#> | |-- 012, 448 cols, 960 signatures
#> | | |-- 0121, 177 cols, 577 signatures
#> | | | |-- 01211, 102 cols, 19 signatures (c)
#> | | | `-- 01212, 75 cols, 172 signatures (c)
#> | | |-- 0122, 131 cols, 1000 signatures
#> | | | |-- 01221, 64 cols, 8 signatures (c)
#> | | | |-- 01222, 41 cols (b)
#> | | | `-- 01223, 26 cols (b)
#> | | `-- 0123, 140 cols, 1387 signatures
#> | | |-- 01231, 36 cols (b)
#> | | |-- 01232, 35 cols (b)
#> | | |-- 01233, 33 cols (b)
#> | | `-- 01234, 36 cols (b)
#> | |-- 013, 201 cols, 628 signatures
#> | | |-- 0131, 114 cols, 99 signatures (c)
#> | | `-- 0132, 87 cols, 104 signatures (c)
#> | `-- 014, 139 cols, 1908 signatures
#> | |-- 0141, 63 cols, 242 signatures (c)
#> | |-- 0142, 53 cols (b)
#> | `-- 0143, 23 cols (b)
#> |-- 02, 960 cols, 5466 signatures
#> | |-- 021, 386 cols, 2878 signatures
#> | | |-- 0211, 181 cols, 3324 signatures
#> | | | |-- 02111, 65 cols, 677 signatures
#> | | | | |-- 021111, 34 cols (b)
#> | | | | `-- 021112, 31 cols (b)
#> | | | |-- 02112, 71 cols, 438 signatures
#> | | | | |-- 021121, 38 cols (b)
#> | | | | `-- 021122, 33 cols (b)
#> | | | `-- 02113, 45 cols (b)
#> | | `-- 0212, 205 cols, 1348 signatures
#> | | |-- 02121, 87 cols, 146 signatures (c)
#> | | |-- 02122, 85 cols, 155 signatures (c)
#> | | `-- 02123, 33 cols (b)
#> | |-- 022, 433 cols, 1390 signatures
#> | | |-- 0221, 188 cols, 178 signatures (c)
#> | | |-- 0222, 109 cols, 634 signatures
#> | | | |-- 02221, 60 cols, 98 signatures (c)
#> | | | `-- 02222, 49 cols (b)
#> | | `-- 0223, 136 cols, 175 signatures (c)
#> | `-- 023, 141 cols, 3262 signatures
#> | |-- 0231, 54 cols (b)
#> | |-- 0232, 36 cols (b)
#> | |-- 0233, 29 cols (b)
#> | `-- 0234, 22 cols (b)
#> `-- 03, 648 cols, 4249 signatures
#> |-- 031, 402 cols, 3301 signatures
#> | |-- 0311, 153 cols, 347 signatures
#> | | |-- 03111, 88 cols (a)
#> | | `-- 03112, 65 cols, 230 signatures (c)
#> | |-- 0312, 131 cols, 979 signatures
#> | | |-- 03121, 71 cols, 304 signatures (c)
#> | | `-- 03122, 60 cols, 508 signatures
#> | | |-- 031221, 32 cols (b)
#> | | `-- 031222, 28 cols (b)
#> | `-- 0313, 118 cols, 236 signatures (c)
#> |-- 032, 185 cols, 3273 signatures
#> | |-- 0321, 77 cols, 58 signatures (c)
#> | |-- 0322, 44 cols (b)
#> | |-- 0323, 38 cols (b)
#> | `-- 0324, 26 cols (b)
#> `-- 033, 61 cols, 2292 signatures
#> |-- 0331, 25 cols (b)
#> |-- 0332, 21 cols (b)
#> `-- 0333, 15 cols (b)
#> Stop reason:
#> a) Mean silhouette score was too small
#> b) Subgroup had too few columns.
#> c) There were too few signatures.
#>
#> Following methods can be applied to this 'HierarchicalPartition' object:
#> [1] "all_leaves" "all_nodes" "cola_report" "collect_classes"
#> [5] "colnames" "compare_signatures" "dimension_reduction" "functional_enrichment"
#> [9] "get_anno_col" "get_anno" "get_children_nodes" "get_classes"
#> [13] "get_matrix" "get_signatures" "is_leaf_node" "max_depth"
#> [17] "merge_node" "ncol" "node_info" "node_level"
#> [21] "nrow" "rownames" "show" "split_node"
#> [25] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single node by e.g. object["01"]
The call of hierarchical_partition() was:
#> hierarchical_partition(data = lt$mat, anno = lt$anno, subset = 500, cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_rh)
dim(mat)
#> [1] 10389 2881
All the methods that were tried:
res_rh@param$combination_method
#> [[1]]
#> [1] "ATC" "skmeans"
The density distribution for each sample is visualized as one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_rh),
col = get_anno_col(res_rh)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 1)
Some values about the hierarchy:
all_nodes(res_rh)
#> [1] "0" "01" "011" "0111" "0112" "0113" "01131" "01132" "01133" "012"
#> [11] "0121" "01211" "01212" "0122" "01221" "01222" "01223" "0123" "01231" "01232"
#> [21] "01233" "01234" "013" "0131" "0132" "014" "0141" "0142" "0143" "02"
#> [31] "021" "0211" "02111" "021111" "021112" "02112" "021121" "021122" "02113" "0212"
#> [41] "02121" "02122" "02123" "022" "0221" "0222" "02221" "02222" "0223" "023"
#> [51] "0231" "0232" "0233" "0234" "03" "031" "0311" "03111" "03112" "0312"
#> [61] "03121" "03122" "031221" "031222" "0313" "032" "0321" "0322" "0323" "0324"
#> [71] "033" "0331" "0332" "0333"
all_leaves(res_rh)
#> [1] "0111" "0112" "01131" "01132" "01133" "01211" "01212" "01221" "01222" "01223"
#> [11] "01231" "01232" "01233" "01234" "0131" "0132" "0141" "0142" "0143" "021111"
#> [21] "021112" "021121" "021122" "02113" "02121" "02122" "02123" "0221" "02221" "02222"
#> [31] "0223" "0231" "0232" "0233" "0234" "03111" "03112" "03121" "031221" "031222"
#> [41] "0313" "0321" "0322" "0323" "0324" "0331" "0332" "0333"
node_info(res_rh)
#> id best_method depth best_k n_columns n_signatures p_signatures is_leaf
#> 1 0 ATC:skmeans 1 3 2881 6708 0.64568 FALSE
#> 2 01 ATC:skmeans 2 4 1273 2797 0.26923 FALSE
#> 3 011 ATC:skmeans 3 3 485 890 0.08567 FALSE
#> 4 0111 ATC:skmeans 4 2 198 64 0.00616 TRUE
#> 5 0112 ATC:skmeans 4 2 168 218 0.02098 TRUE
#> 6 0113 ATC:skmeans 4 3 119 755 0.07267 FALSE
#> 7 01131 not applied 5 NA 45 NA NA TRUE
#> 8 01132 not applied 5 NA 39 NA NA TRUE
#> 9 01133 not applied 5 NA 35 NA NA TRUE
#> 10 012 ATC:skmeans 3 3 448 960 0.09241 FALSE
#> 11 0121 ATC:skmeans 4 2 177 577 0.05554 FALSE
#> 12 01211 ATC:skmeans 5 2 102 19 0.00183 TRUE
#> 13 01212 ATC:skmeans 5 2 75 172 0.01656 TRUE
#> 14 0122 ATC:skmeans 4 3 131 1000 0.09626 FALSE
#> 15 01221 ATC:skmeans 5 2 64 8 0.00077 TRUE
#> 16 01222 not applied 5 NA 41 NA NA TRUE
#> 17 01223 not applied 5 NA 26 NA NA TRUE
#> 18 0123 ATC:skmeans 4 4 140 1387 0.13351 FALSE
#> 19 01231 not applied 5 NA 36 NA NA TRUE
#> 20 01232 not applied 5 NA 35 NA NA TRUE
#> 21 01233 not applied 5 NA 33 NA NA TRUE
#> 22 01234 not applied 5 NA 36 NA NA TRUE
#> 23 013 ATC:skmeans 3 2 201 628 0.06045 FALSE
#> 24 0131 ATC:skmeans 4 2 114 99 0.00953 TRUE
#> 25 0132 ATC:skmeans 4 2 87 104 0.01001 TRUE
#> 26 014 ATC:skmeans 3 3 139 1908 0.18366 FALSE
#> 27 0141 ATC:skmeans 4 2 63 242 0.02329 TRUE
#> 28 0142 not applied 4 NA 53 NA NA TRUE
#> 29 0143 not applied 4 NA 23 NA NA TRUE
#> 30 02 ATC:skmeans 2 3 960 5466 0.52613 FALSE
#> 31 021 ATC:skmeans 3 2 386 2878 0.27702 FALSE
#> 32 0211 ATC:skmeans 4 3 181 3324 0.31995 FALSE
#> 33 02111 ATC:skmeans 5 2 65 677 0.06517 FALSE
#> 34 021111 not applied 6 NA 34 NA NA TRUE
#> 35 021112 not applied 6 NA 31 NA NA TRUE
#> 36 02112 ATC:skmeans 5 2 71 438 0.04216 FALSE
#> 37 021121 not applied 6 NA 38 NA NA TRUE
#> 38 021122 not applied 6 NA 33 NA NA TRUE
#> 39 02113 not applied 5 NA 45 NA NA TRUE
#> 40 0212 ATC:skmeans 4 3 205 1348 0.12975 FALSE
#> 41 02121 ATC:skmeans 5 2 87 146 0.01405 TRUE
#> 42 02122 ATC:skmeans 5 2 85 155 0.01492 TRUE
#> 43 02123 not applied 5 NA 33 NA NA TRUE
#> 44 022 ATC:skmeans 3 3 433 1390 0.13380 FALSE
#> 45 0221 ATC:skmeans 4 2 188 178 0.01713 TRUE
#> 46 0222 ATC:skmeans 4 2 109 634 0.06103 FALSE
#> 47 02221 ATC:skmeans 5 2 60 98 0.00943 TRUE
#> 48 02222 not applied 5 NA 49 NA NA TRUE
#> 49 0223 ATC:skmeans 4 2 136 175 0.01684 TRUE
#> 50 023 ATC:skmeans 3 4 141 3262 0.31399 FALSE
#> 51 0231 not applied 4 NA 54 NA NA TRUE
#> 52 0232 not applied 4 NA 36 NA NA TRUE
#> 53 0233 not applied 4 NA 29 NA NA TRUE
#> 54 0234 not applied 4 NA 22 NA NA TRUE
#> 55 03 ATC:skmeans 2 3 648 4249 0.40899 FALSE
#> 56 031 ATC:skmeans 3 3 402 3301 0.31774 FALSE
#> 57 0311 ATC:skmeans 4 2 153 347 0.03340 FALSE
#> 58 03111 ATC:skmeans 5 3 88 NA NA TRUE
#> 59 03112 ATC:skmeans 5 2 65 230 0.02214 TRUE
#> 60 0312 ATC:skmeans 4 2 131 979 0.09423 FALSE
#> 61 03121 ATC:skmeans 5 2 71 304 0.02926 TRUE
#> 62 03122 ATC:skmeans 5 2 60 508 0.04890 FALSE
#> 63 031221 not applied 6 NA 32 NA NA TRUE
#> 64 031222 not applied 6 NA 28 NA NA TRUE
#> 65 0313 ATC:skmeans 4 2 118 236 0.02272 TRUE
#> 66 032 ATC:skmeans 3 4 185 3273 0.31504 FALSE
#> 67 0321 ATC:skmeans 4 2 77 58 0.00558 TRUE
#> 68 0322 not applied 4 NA 44 NA NA TRUE
#> 69 0323 not applied 4 NA 38 NA NA TRUE
#> 70 0324 not applied 4 NA 26 NA NA TRUE
#> 71 033 ATC:skmeans 3 3 61 2292 0.22062 FALSE
#> 72 0331 not applied 4 NA 25 NA NA TRUE
#> 73 0332 not applied 4 NA 21 NA NA TRUE
#> 74 0333 not applied 4 NA 15 NA NA TRUE
In the output from node_info(), there are the following columns:
id: The node id.best_method: The best method selected.depth: Depth of the node in the hierarchy.best_k: Best number of groups of the partition on that node.n_columns: Number of columns in the submatrix.n_signatures: Number of signatures with the best_k.p_signatures: Proportion of hte signatures in total number of rows in the matrix.is_leaf: Whether the node is a leaf.Labels of nodes are encoded in a special way. The number of digits correspond to the depth of the node in the hierarchy and the value of the digits correspond to the index of the subgroup in the current node, E.g. a label of “012” means the node is the second subgroup of the partition which is the first subgroup of the root node.
Following table shows the best k (number of partitions) for each node in the
partition hierarchy. Clicking on the node name in the table goes to the
corresponding section for the partitioning on that node.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_rh)
| Node | Best method | Is leaf | Best k | 1-PAC | Mean silhouette | Concordance | #samples | |
|---|---|---|---|---|---|---|---|---|
| Node0 | ATC:skmeans | 4 | 1.00 | 0.95 | 0.98 | 2881 | ** | |
| Node01 | ATC:skmeans | 4 | 1.00 | 0.97 | 0.99 | 1273 | ** | |
| Node011 | ATC:skmeans | 3 | 0.97 | 0.96 | 0.98 | 485 | ** | |
| Node0111-leaf | ATC:skmeans | ✓ (c) | 2 | 0.92 | 0.95 | 0.98 | 198 | * |
| Node0112-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.97 | 0.99 | 168 | ** |
| Node0113 | ATC:skmeans | 3 | 1.00 | 0.96 | 0.98 | 119 | ** | |
| Node01131-leaf | not applied | ✓ (b) | 45 | |||||
| Node01132-leaf | not applied | ✓ (b) | 39 | |||||
| Node01133-leaf | not applied | ✓ (b) | 35 | |||||
| Node012 | ATC:skmeans | 3 | 1.00 | 0.99 | 0.99 | 448 | ** | |
| Node0121 | ATC:skmeans | 2 | 1.00 | 0.97 | 0.99 | 177 | ** | |
| Node01211-leaf | ATC:skmeans | ✓ (c) | 2 | 0.82 | 0.91 | 0.96 | 102 | |
| Node01212-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.99 | 1.00 | 75 | ** |
| Node0122 | ATC:skmeans | 3 | 0.98 | 0.96 | 0.98 | 131 | ** | |
| Node01221-leaf | ATC:skmeans | ✓ (c) | 2 | 0.93 | 0.92 | 0.97 | 64 | * |
| Node01222-leaf | not applied | ✓ (b) | 41 | |||||
| Node01223-leaf | not applied | ✓ (b) | 26 | |||||
| Node0123 | ATC:skmeans | 4 | 0.99 | 0.97 | 0.98 | 140 | ** | |
| Node01231-leaf | not applied | ✓ (b) | 36 | |||||
| Node01232-leaf | not applied | ✓ (b) | 35 | |||||
| Node01233-leaf | not applied | ✓ (b) | 33 | |||||
| Node01234-leaf | not applied | ✓ (b) | 36 | |||||
| Node013 | ATC:skmeans | 2 | 0.95 | 0.95 | 0.98 | 201 | * | |
| Node0131-leaf | ATC:skmeans | ✓ (c) | 2 | 0.90 | 0.93 | 0.97 | 114 | * |
| Node0132-leaf | ATC:skmeans | ✓ (c) | 2 | 0.78 | 0.91 | 0.96 | 87 | |
| Node014 | ATC:skmeans | 3 | 1.00 | 0.96 | 0.99 | 139 | ** | |
| Node0141-leaf | ATC:skmeans | ✓ (c) | 3 | 0.92 | 0.94 | 0.97 | 63 | * |
| Node0142-leaf | not applied | ✓ (b) | 53 | |||||
| Node0143-leaf | not applied | ✓ (b) | 23 | |||||
| Node02 | ATC:skmeans | 4 | 0.97 | 0.94 | 0.97 | 960 | ** | |
| Node021 | ATC:skmeans | 3 | 0.94 | 0.94 | 0.98 | 386 | * | |
| Node0211 | ATC:skmeans | 3 | 1.00 | 0.98 | 0.99 | 181 | ** | |
| Node02111 | ATC:skmeans | 3 | 0.93 | 0.91 | 0.96 | 65 | * | |
| Node021111-leaf | not applied | ✓ (b) | 34 | |||||
| Node021112-leaf | not applied | ✓ (b) | 31 | |||||
| Node02112 | ATC:skmeans | 3 | 0.91 | 0.91 | 0.96 | 71 | * | |
| Node021121-leaf | not applied | ✓ (b) | 38 | |||||
| Node021122-leaf | not applied | ✓ (b) | 33 | |||||
| Node02113-leaf | not applied | ✓ (b) | 45 | |||||
| Node0212 | ATC:skmeans | 3 | 0.99 | 0.96 | 0.98 | 205 | ** | |
| Node02121-leaf | ATC:skmeans | ✓ (c) | 3 | 0.94 | 0.93 | 0.97 | 87 | * |
| Node02122-leaf | ATC:skmeans | ✓ (c) | 2 | 0.97 | 0.96 | 0.98 | 85 | ** |
| Node02123-leaf | not applied | ✓ (b) | 33 | |||||
| Node022 | ATC:skmeans | 4 | 0.94 | 0.91 | 0.97 | 433 | * | |
| Node0221-leaf | ATC:skmeans | ✓ (c) | 3 | 0.95 | 0.94 | 0.97 | 188 | ** |
| Node0222 | ATC:skmeans | 2 | 1.00 | 0.98 | 0.99 | 109 | ** | |
| Node02221-leaf | ATC:skmeans | ✓ (c) | 3 | 0.91 | 0.92 | 0.97 | 60 | * |
| Node02222-leaf | not applied | ✓ (b) | 49 | |||||
| Node0223-leaf | ATC:skmeans | ✓ (c) | 2 | 0.89 | 0.93 | 0.97 | 136 | |
| Node023 | ATC:skmeans | 4 | 1.00 | 0.97 | 0.99 | 141 | ** | |
| Node0231-leaf | not applied | ✓ (b) | 54 | |||||
| Node0232-leaf | not applied | ✓ (b) | 36 | |||||
| Node0233-leaf | not applied | ✓ (b) | 29 | |||||
| Node0234-leaf | not applied | ✓ (b) | 22 | |||||
| Node03 | ATC:skmeans | 4 | 0.97 | 0.94 | 0.97 | 648 | ** | |
| Node031 | ATC:skmeans | 4 | 0.94 | 0.92 | 0.97 | 402 | * | |
| Node0311 | ATC:skmeans | 3 | 0.96 | 0.94 | 0.98 | 153 | ** | |
| Node03111-leaf | ATC:skmeans | ✓ (a) | 3 | 0.83 | 0.87 | 0.94 | 88 | |
| Node03112-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.98 | 0.99 | 65 | ** |
| Node0312 | ATC:skmeans | 4 | 0.92 | 0.91 | 0.96 | 131 | * | |
| Node03121-leaf | ATC:skmeans | ✓ (c) | 4 | 0.94 | 0.93 | 0.97 | 71 | * |
| Node03122 | ATC:skmeans | 2 | 1.00 | 0.98 | 0.99 | 60 | ** | |
| Node031221-leaf | not applied | ✓ (b) | 32 | |||||
| Node031222-leaf | not applied | ✓ (b) | 28 | |||||
| Node0313-leaf | ATC:skmeans | ✓ (c) | 3 | 0.94 | 0.94 | 0.97 | 118 | * |
| Node032 | ATC:skmeans | 4 | 1.00 | 0.98 | 0.99 | 185 | ** | |
| Node0321-leaf | ATC:skmeans | ✓ (c) | 2 | 0.97 | 0.95 | 0.98 | 77 | ** |
| Node0322-leaf | not applied | ✓ (b) | 44 | |||||
| Node0323-leaf | not applied | ✓ (b) | 38 | |||||
| Node0324-leaf | not applied | ✓ (b) | 26 | |||||
| Node033 | ATC:skmeans | 4 | 1.00 | 0.98 | 0.99 | 61 | ** | |
| Node0331-leaf | not applied | ✓ (b) | 25 | |||||
| Node0332-leaf | not applied | ✓ (b) | 21 | |||||
| Node0333-leaf | not applied | ✓ (b) | 15 |
Stop reason: a) Mean silhouette score was too small b) Subgroup had too few columns. c) There were too few signatures.
**: 1-PAC > 0.95, *: 1-PAC > 0.9
The nodes of the hierarchy can be merged by setting the merge_node parameters. Here we
control the hierarchy with the min_n_signatures parameter. The value of min_n_signatures is
from node_info().
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 347))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 438))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 508))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 577))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 628))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 634))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 677))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 755))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 890))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 960))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 979))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1000))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1348))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1387))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1390))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1908))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2292))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2797))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2878))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3262))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3273))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3301))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3324))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 4249))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 5466))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 6708))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
Following shows the table of the partitions (You need to click the show/hide code output link to see it).
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 347))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232"
#> [11] "01232" "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131"
#> [21] "0322" "01232" "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121"
#> [31] "0322" "01232" "0322" "0322" "0322" "01232" "01232" "01232" "03121" "03112"
#> [41] "01131" "02222" "01131" "03112" "01232" "01232" "03112" "03121" "0322" "01131"
#> [51] "03121" "01131" "01131" "01232" "01232" "02123" "02123" "0143" "01133" "0313"
#> [61] "0322" "01131" "01131" "02221" "03111" "01132" "01232" "01232" "0322" "01232"
#> [71] "0322" "03111" "0322" "01131" "01131" "0143" "0111" "0112" "02221" "01131"
#> [81] "0143" "0322" "01131" "0143" "01133" "02221" "01131" "01131" "01131" "01231"
#> [91] "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143" "0313"
#> [101] "01131" "01131" "0111" "01133" "0111" "0322" "02221" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "02221" "02221" "0322" "01132" "0321"
#> [121] "0313" "0322" "02222" "02221" "02222" "0234" "01231" "0111" "01133" "01133"
#> [131] "01231" "01131" "01133" "0324" "0111" "02222" "01131" "01131" "0322" "0111"
#> [141] "01131" "0111" "01232" "01231" "01231" "02222" "01131" "02123" "01131" "0324"
#> [151] "0313" "0313" "01131" "0313" "0322" "01131" "0313" "0234" "0322" "0322"
#> [161] "0322" "01131" "0313" "0313" "02222" "01131" "0322" "0313" "01131" "01131"
#> [171] "0322" "0313" "0313" "02222" "02222" "0313" "0313" "01131" "0313" "0313"
#> [181] "03121" "0313" "0322" "0313" "0322" "0313" "0313" "0313" "03121" "02222"
#> [191] "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121" "03121" "031221"
#> [201] "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322" "02222"
#> [211] "0313" "0234" "0313" "03121" "0313" "0313" "0322" "02222" "03121" "01133"
#> [221] "03121" "0313" "031221" "0313" "03121" "0313" "03121" "03121" "01131" "02113"
#> [231] "0313" "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313"
#> [241] "0313" "0313" "03121" "01133" "03121" "03121" "03121" "02222" "03121" "0313"
#> [251] "01133" "0313" "03121" "03121" "0313" "0313" "01133" "03121" "0313" "0313"
#> [261] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "01133" "0313"
#> [271] "01133" "01133" "0313" "0313" "01133" "0313" "0313" "0313" "0313" "0322"
#> [281] "02123" "01133" "0313" "0313" "0313" "02222" "0313" "0313" "03121" "03121"
#> [291] "03121" "031221" "03121" "03121" "03121" "03121" "03121" "031221" "02113" "03121"
#> [301] "02113" "0313" "0313" "0234" "0313" "02113" "02222" "031221" "02222" "03121"
#> [311] "03121" "0313" "02222" "0313" "0313" "03121" "01133" "0313" "0313" "01133"
#> [321] "0313" "01133" "03121" "0313" "03111" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "02222" "02222" "01132" "0313" "0112"
#> [341] "0313" "0313" "02222" "0313" "0313" "0313" "02222" "03111" "03111" "02222"
#> [351] "0313" "01133" "0313" "0313" "0313" "0313" "031221" "03121" "031221" "031221"
#> [361] "03121" "0313" "0313" "0313" "0313" "0313" "0313" "03121" "03121" "03121"
#> [371] "0313" "031221" "031221" "03121" "03121" "03121" "03121" "031221" "031221" "0313"
#> [381] "0111" "0112" "02222" "03111" "0112" "0111" "0112" "03111" "0324" "03111"
#> [391] "0112" "03111" "0112" "03111" "02222" "03111" "0313" "03111" "0112" "0111"
#> [401] "03111" "0112" "0143" "03111" "0112" "02222" "0111" "03111" "0112" "0112"
#> [411] "03111" "03111" "02123" "0112" "0112" "0112" "0111" "01133" "03111" "0111"
#> [421] "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112" "0112"
#> [431] "0112" "0234" "0112" "0234" "0111" "02221" "0112" "02123" "0112" "0234"
#> [441] "0234" "03111" "03111" "03111" "03111" "0112" "0112" "031221" "031221" "03121"
#> [451] "03111" "0112" "03112" "0112" "0112" "03121" "0112" "0112" "031222" "031222"
#> [461] "03111" "031221" "03111" "03111" "031221" "031221" "031222" "03111" "031221" "031221"
#> [471] "031222" "03111" "031222" "031221" "031221" "03111" "03111" "03121" "03111" "03111"
#> [481] "03111" "03111" "03111" "0112" "02123" "031222" "03112" "03111" "02222" "02222"
#> [491] "02123" "03121" "031222" "02222" "031222" "0112" "02123" "02113" "0112" "031222"
#> [501] "02113" "0112" "03111" "031221" "03111" "02113" "0112" "03111" "03111" "03111"
#> [511] "03111" "02222" "03111" "03111" "0112" "0112" "02222" "03111" "03121" "03111"
#> [521] "0112" "0112" "0112" "031221" "03121" "0313" "03121" "0112" "0112" "02221"
#> [531] "02123" "02123" "0112" "02222" "0111" "0111" "0111" "02123" "0111" "03111"
#> [541] "0112" "02222" "0111" "0112" "02222" "0111" "0111" "0112" "03111" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "03111" "03111" "0143"
#> [561] "03112" "01132" "0324" "0324" "01132" "0112" "0111" "02221" "03111" "0112"
#> [571] "0112" "02221" "0324" "03111" "0112" "03121" "0111" "0112" "0112" "02221"
#> [581] "0112" "0112" "0111" "0112" "03111" "0112" "03112" "0112" "0111" "01132"
#> [591] "0111" "0313" "0112" "031222" "0313" "0324" "0112" "0313" "0313" "0111"
#> [601] "0111" "01132" "0111" "0313" "0111" "0112" "02222" "0111" "0111" "0111"
#> [611] "0111" "0111" "0112" "0111" "0111" "0234" "03111" "03111" "0112" "03111"
#> [621] "03111" "0313" "0112" "03111" "0112" "03112" "03112" "03112" "03111" "03111"
#> [631] "0112" "0112" "0313" "0112" "03111" "02113" "03111" "0112" "0112" "0112"
#> [641] "0112" "03111" "03111" "0112" "03111" "03121" "0112" "0112" "02222" "0112"
#> [651] "0112" "0112" "0112" "0112" "03111" "0112" "0112" "0112" "03111" "03112"
#> [661] "03111" "0112" "0234" "0112" "0112" "031222" "03112" "03111" "03111" "03112"
#> [671] "02222" "0112" "02222" "03111" "0313" "0234" "03111" "03112" "02222" "0112"
#> [681] "03111" "03111" "031222" "031222" "03112" "031222" "031222" "031222" "03112" "03112"
#> [691] "03112" "0112" "031222" "03112" "02222" "03112" "031221" "0112" "031222" "0143"
#> [701] "031221" "0112" "0111" "03112" "03112" "02222" "02222" "0112" "0324" "0112"
#> [711] "0324" "02123" "0111" "0112" "0111" "0112" "0111" "0111" "02221" "03112"
#> [721] "03112" "02221" "0234" "0112" "02221" "03112" "03112" "03112" "0112" "0112"
#> [731] "03112" "0112" "0111" "03112" "0112" "0112" "0111" "0111" "0111" "03112"
#> [741] "0112" "0112" "0112" "03112" "03112" "0112" "03112" "031222" "031222" "031221"
#> [751] "03112" "0112" "03112" "0112" "0112" "0112" "03112" "0112" "0324" "03112"
#> [761] "02123" "02222" "0112" "0112" "03112" "0112" "0112" "0112" "0111" "0111"
#> [771] "031222" "0112" "0112" "03112" "0112" "02222" "0111" "0112" "02113" "0112"
#> [781] "03112" "0112" "0112" "0111" "0112" "0112" "031222" "0111" "03111" "03112"
#> [791] "0112" "0112" "031221" "02222" "0112" "031222" "0111" "0111" "0234" "03112"
#> [801] "031222" "02222" "03112" "03112" "03112" "0234" "03112" "0112" "03112" "0112"
#> [811] "0112" "0112" "0324" "0324" "01231" "0143" "0111" "0112" "0111" "02123"
#> [821] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [831] "0111" "0111" "0111" "01231" "0111" "0111" "0111" "0111" "0111" "0111"
#> [841] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0324" "01231"
#> [851] "01132" "0234" "0324" "02222" "0111" "0143" "0143" "0143" "0324" "0111"
#> [861] "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "03111"
#> [871] "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324"
#> [891] "0111" "03112" "0111" "0111" "0111" "02221" "02221" "0111" "0111" "0111"
#> [901] "0111" "0111" "03112" "0111" "0111" "0112" "0112" "0112" "0112" "0111"
#> [911] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [921] "0111" "0111" "0111" "0112" "0111" "0111" "0111" "0111" "02221" "0111"
#> [931] "0143" "0111" "0111" "0111" "02221" "0324" "0111" "02221" "0111" "0111"
#> [941] "0111" "0143" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [951] "0111" "0111" "0111" "03111" "01132" "02123" "0143" "0143" "0111" "02123"
#> [961] "02221" "0112" "0111" "02123" "03112" "0112" "0111" "0111" "0111" "03111"
#> [971] "0111" "0111" "02221" "0112" "01231" "0111" "0111" "0111" "0111" "0111"
#> [981] "0324" "0324" "02222" "02221" "03112" "0112" "0111" "0112" "0112" "03112"
#> [991] "03112" "0112" "0112" "0111" "03112" "0111" "0111" "0324" "0324" "0111"
#> [1001] "02221" "0132" "01211" "01212" "01212" "01212" "01212" "01212" "01212" "01212"
#> [1011] "01212" "01212" "01212" "02221" "01212" "02221" "01212" "01212" "01212" "01212"
#> [1021] "01212" "01212" "01212" "01212" "01212" "0323" "0142" "01212" "01212" "01212"
#> [1031] "01212" "01212" "01212" "01212" "01212" "03111" "01212" "03121" "01212" "02113"
#> [1041] "01212" "01132" "0322" "01212" "01212" "01212" "03121" "01211" "01211" "01212"
#> [1051] "01132" "01212" "01212" "01211" "01211" "01211" "01212" "01212" "01132" "01211"
#> [1061] "01211" "01212" "01212" "01211" "01211" "01212" "01212" "01212" "01212" "01211"
#> [1071] "01211" "01212" "0132" "01212" "0322" "01211" "01211" "01211" "0132" "01211"
#> [1081] "01212" "01211" "03121" "01211" "01211" "01211" "01211" "03112" "01211" "01212"
#> [1091] "01211" "01212" "01132" "0132" "01211" "01211" "01211" "01212" "01211" "01212"
#> [1101] "0142" "01212" "01212" "01212" "01212" "01212" "01211" "01211" "01212" "03112"
#> [1111] "01212" "01212" "01212" "01212" "01212" "0132" "01212" "0313" "01212" "0141"
#> [1121] "01212" "0313" "01212" "01212" "01211" "01212" "01211" "031221" "01212" "01211"
#> [1131] "01211" "01132" "01132" "01211" "0322" "01211" "01212" "01211" "01211" "01211"
#> [1141] "0221" "01211" "01211" "01132" "0322" "01232" "01211" "0111" "01211" "0142"
#> [1151] "01211" "01211" "01231" "01232" "01211" "01211" "01211" "01211" "01211" "01211"
#> [1161] "01132" "01232" "01132" "01211" "01211" "01211" "01211" "0111" "01132" "0111"
#> [1171] "01231" "03112" "01132" "01211" "01132" "01132" "01211" "01211" "01211" "01211"
#> [1181] "0223" "01211" "0141" "01211" "0221" "0111" "01211" "01211" "01132" "01211"
#> [1191] "01211" "01211" "01211" "01211" "01211" "0141" "01211" "01231" "0131" "01211"
#> [1201] "01211" "0141" "01211" "01211" "01211" "01211" "01211" "0112" "01211" "01211"
#> [1211] "0131" "01211" "01232" "0141" "02221" "01211" "0321" "0313" "01211" "01211"
#> [1221] "01131" "01211" "01211" "0221" "01211" "0223" "01232" "01211" "01211" "0141"
#> [1231] "01211" "01211" "01211" "01132" "01211" "01131" "0112" "0313" "0141" "01211"
#> [1241] "0333" "0321" "03112" "01211" "01211" "01211" "01132" "01132" "01211" "01211"
#> [1251] "01132" "01234" "0112" "0111" "0112" "0221" "0221" "0221" "0223" "01223"
#> [1261] "01212" "01223" "01211" "01234" "03111" "0141" "0111" "01132" "0132" "0132"
#> [1271] "0132" "0131" "0331" "0131" "0131" "0132" "0333" "02122" "0332" "0332"
#> [1281] "0331" "0132" "0332" "0132" "0132" "0132" "0132" "0132" "0332" "0332"
#> [1291] "0132" "0331" "0132" "0132" "0233" "0333" "0132" "0132" "0132" "0131"
#> [1301] "0132" "0131" "0131" "0132" "0131" "0131" "0131" "0132" "0132" "0132"
#> [1311] "0132" "0132" "0223" "0331" "0221" "0131" "0333" "02122" "0132" "0131"
#> [1321] "0131" "0132" "0132" "0132" "0131" "0132" "0333" "01223" "0131" "0131"
#> [1331] "0132" "02113" "0132" "0331" "0132" "0333" "0132" "0132" "0132" "0131"
#> [1341] "0132" "0132" "02221" "0132" "0131" "0223" "0233" "02113" "0131" "0132"
#> [1351] "0131" "0131" "02113" "0223" "0132" "0131" "0131" "0132" "0131" "0131"
#> [1361] "02122" "02122" "0131" "0132" "0331" "0331" "0131" "0331" "0132" "0331"
#> [1371] "0331" "0331" "0332" "0132" "0331" "0132" "0132" "0131" "0131" "0132"
#> [1381] "0131" "0132" "0132" "0132" "0132" "0132" "0132" "01234" "01223" "01231"
#> [1391] "01234" "0321" "0131" "0131" "0231" "0141" "02113" "0233" "0233" "01231"
#> [1401] "0233" "0132" "0132" "0131" "0131" "0333" "0233" "0131" "0131" "03112"
#> [1411] "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0333" "0131"
#> [1421] "0131" "03111" "0131" "0131" "0131" "0132" "0131" "0131" "0333" "03111"
#> [1431] "0131" "0112" "0131" "03112" "01231" "0131" "0131" "0131" "0131" "0131"
#> [1441] "0131" "0131" "0131" "0131" "0131" "01231" "0131" "0131" "0233" "0131"
#> [1451] "0333" "0221" "0132" "0131" "0221" "0131" "0131" "0131" "0223" "0131"
#> [1461] "0131" "0131" "0132" "0132" "01231" "0131" "0111" "0111" "01131" "0132"
#> [1471] "0131" "0333" "01231" "0313" "0333" "0313" "0112" "02121" "0131" "0221"
#> [1481] "01232" "0131" "0132" "0111" "0131" "0131" "0131" "0321" "0141" "0131"
#> [1491] "0141" "0131" "0131" "0111" "0231" "0141" "0131" "0111" "0131" "0233"
#> [1501] "01231" "0141" "0131" "0111" "01231" "0321" "0132" "02222" "0131" "0223"
#> [1511] "01231" "0131" "01231" "0132" "0131" "01231" "0131" "0221" "0331" "0221"
#> [1521] "0233" "0233" "0142" "0221" "0142" "0132" "0333" "0132" "0132" "0131"
#> [1531] "0142" "0131" "0132" "02113" "01223" "0223" "0112" "0111" "0132" "0131"
#> [1541] "01232" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131"
#> [1551] "03111" "0131" "0111" "0131" "0131" "0131" "0142" "02121" "0233" "0131"
#> [1561] "01231" "01231" "0143" "03121" "0223" "01133" "0132" "0333" "0131" "01231"
#> [1571] "0131" "0223" "02121" "0142" "02121" "0332" "0332" "02113" "0233" "0233"
#> [1581] "0332" "02113" "0332" "0233" "0332" "0332" "0331" "0332" "0331" "0332"
#> [1591] "0132" "0331" "0332" "02221" "0331" "02113" "02121" "0233" "0132" "02113"
#> [1601] "0132" "0332" "0132" "02123" "02113" "0132" "0132" "0233" "02113" "02113"
#> [1611] "0331" "0331" "0332" "0331" "0331" "0331" "0331" "02113" "0132" "02221"
#> [1621] "02113" "0233" "0132" "0331" "01132" "02122" "01234" "0132" "01234" "0132"
#> [1631] "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221" "01234"
#> [1641] "01234" "0132" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234"
#> [1661] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234"
#> [1671] "01234" "01231" "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233"
#> [1681] "01233" "01233" "01221" "0141" "01233" "01212" "01132" "01211" "01232" "01223"
#> [1691] "01233" "01233" "0322" "01212" "01233" "01233" "01233" "0112" "01233" "01233"
#> [1701] "0112" "01233" "03112" "01233" "01233" "01221" "0323" "01223" "01233" "01233"
#> [1711] "0131" "03111" "01233" "01233" "01223" "0132" "01233" "01233" "0323" "0323"
#> [1721] "0131" "01233" "0141" "01233" "01233" "01233" "01233" "0313" "01233" "03111"
#> [1731] "03111" "01233" "01211" "02121" "01231" "01133" "01223" "01133" "0112" "0111"
#> [1741] "01221" "01223" "0132" "01221" "0131" "01221" "01222" "01223" "0323" "01222"
#> [1751] "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "031221"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "03111" "0111" "0131" "0221"
#> [1771] "01221" "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221"
#> [1781] "0322" "01132" "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323"
#> [1791] "01222" "0313" "0313" "0323" "0223" "01132" "01221" "0313" "0223" "01221"
#> [1801] "01221" "01221" "01222" "0323" "01221" "01221" "0233" "02121" "0223" "03112"
#> [1811] "0221" "01221" "01221" "01131" "01223" "01221" "01221" "01221" "01221" "01221"
#> [1821] "0313" "01221" "01221" "01221" "01221" "01221" "01221" "01221" "01222" "0223"
#> [1831] "01221" "01221" "0323" "01221" "01222" "02122" "0223" "01221" "0111" "01221"
#> [1841] "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221" "01222" "01221"
#> [1851] "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322" "01233"
#> [1861] "031222" "01233" "01233" "0332" "0223" "031221" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "01211"
#> [1881] "021121" "01223" "01223" "01223" "01223" "0323" "01222" "031222" "01222" "01222"
#> [1891] "01132" "0221" "01221" "01221" "01222" "03112" "01221" "01222" "01221" "03121"
#> [1901] "0132" "0323" "01223" "03111" "01223" "01223" "0332" "01223" "01222" "01222"
#> [1911] "01222" "01222" "01222" "01221" "0323" "01222" "01221" "01132" "01221" "031221"
#> [1921] "0223" "01222" "0323" "0323" "01222" "03112" "01222" "01222" "01222" "0233"
#> [1931] "0323" "01222" "021121" "01222" "0323" "0233" "0333" "01222" "01222" "0323"
#> [1941] "0323" "01222" "0323" "01222" "0332" "02221" "031221" "0323" "01222" "03121"
#> [1951] "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323" "02113" "0323"
#> [1961] "0221" "0323" "0323" "02221" "01222" "0323" "021121" "0331" "0323" "031222"
#> [1971] "01222" "0233" "031222" "0323" "02122" "03112" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "021122" "0232" "02121" "02122" "02122" "021122"
#> [1991] "0221" "02122" "0231" "0232" "0223" "02123" "0231" "0231" "021122" "0231"
#> [2001] "0223" "02113" "021121" "0232" "021122" "02221" "0221" "02121" "0232" "0232"
#> [2011] "02123" "0231" "02121" "0231" "0142" "0221" "0231" "0321" "0223" "021122"
#> [2021] "02122" "02221" "0223" "0221" "02221" "0321" "0223" "02122" "02122" "0223"
#> [2031] "02221" "02122" "0223" "0232" "0221" "02113" "0221" "021121" "0223" "0223"
#> [2041] "0221" "0321" "021121" "0233" "0232" "02113" "02122" "02121" "02121" "0142"
#> [2051] "0221" "02113" "0231" "02113" "021122" "02121" "0223" "02122" "0321" "0223"
#> [2061] "021121" "0223" "0223" "02122" "0221" "0223" "02122" "02122" "02122" "021121"
#> [2071] "021121" "0223" "0232" "02221" "02113" "0233" "021122" "02221" "021121" "021121"
#> [2081] "021121" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231"
#> [2101] "02121" "02121" "02121" "02122" "021121" "021121" "02121" "021122" "0231" "021122"
#> [2111] "0231" "0223" "021121" "021122" "021122" "021122" "0223" "02123" "0231" "0232"
#> [2121] "02121" "02121" "0233" "0232" "0142" "0223" "02121" "0142" "021121" "021122"
#> [2131] "02122" "02121" "021122" "021122" "021121" "02121" "02122" "02121" "0221" "02121"
#> [2141] "02221" "0223" "02122" "0221" "0221" "02221" "0223" "02121" "0223" "02121"
#> [2151] "021121" "02122" "0223" "0223" "02122" "021121" "02121" "0223" "0223" "021121"
#> [2161] "0221" "0223" "0221" "02122" "0223" "0223" "0221" "02121" "0223" "0223"
#> [2171] "0223" "0221" "02121" "0321" "0221" "0221" "0221" "021112" "02122" "02122"
#> [2181] "02122" "0223" "0234" "02222" "0223" "0221" "0221" "0221" "0221" "0143"
#> [2191] "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "021121" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "02221" "02121" "02121" "02121" "0231"
#> [2211] "0232" "0221" "0232" "0223" "02121" "02123" "021122" "021121" "02121" "02121"
#> [2221] "0223" "02123" "02121" "02121" "0221" "021122" "021122" "02121" "0223" "02121"
#> [2231] "0223" "0223" "0223" "0223" "02121" "0221" "0321" "02221" "0221" "0321"
#> [2241] "0221" "0321" "0223" "0221" "0223" "0223" "0223" "0231" "0231" "0221"
#> [2251] "02221" "0321" "02221" "0221" "0231" "0231" "0221" "0221" "0141" "0321"
#> [2261] "021122" "0221" "0221" "0221" "0223" "0321" "0231" "0221" "0321" "0223"
#> [2271] "0223" "0223" "0142" "0223" "0142" "02221" "0223" "0321" "0221" "0231"
#> [2281] "02221" "0221" "0141" "02221" "0221" "0221" "0142" "0321" "0321" "0221"
#> [2291] "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141" "0321"
#> [2301] "0142" "0142" "0141" "0223" "0142" "02221" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221"
#> [2321] "0142" "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221"
#> [2331] "0223" "0221" "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141"
#> [2341] "0321" "0321" "0221" "02122" "0232" "0223" "0223" "0223" "0221" "0221"
#> [2351] "0321" "02221" "0223" "0223" "0221" "0221" "0321" "02121" "021122" "0221"
#> [2361] "02121" "0221" "02121" "0234" "02121" "02122" "0221" "021122" "021122" "0221"
#> [2371] "0223" "0223" "02121" "0223" "02121" "0223" "0221" "0221" "02123" "02121"
#> [2381] "0232" "0223" "021121" "02122" "0232" "0221" "0223" "0223" "0223" "0231"
#> [2391] "02113" "0223" "0221" "0221" "021112" "02121" "02122" "0223" "0321" "0221"
#> [2401] "0141" "0141" "0141" "02122" "0221" "0231" "021112" "0223" "02122" "02221"
#> [2411] "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223"
#> [2431] "0221" "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141"
#> [2441] "0141" "01234" "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223"
#> [2451] "02122" "02122" "02122" "021121" "02122" "02122" "021121" "02122" "02122" "0231"
#> [2461] "02122" "02122" "02122" "02123" "02122" "02123" "02122" "02122" "02221" "0221"
#> [2471] "0321" "0221" "0221" "0221" "02122" "02122" "0223" "02122" "0223" "0221"
#> [2481] "0223" "02122" "0223" "0223" "021121" "0223" "02122" "02122" "02122" "021121"
#> [2491] "02123" "02122" "02122" "021122" "0223" "02122" "0223" "02122" "02122" "0221"
#> [2501] "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321" "0321" "0221"
#> [2511] "0324" "02122" "02122" "021121" "02122" "02122" "021121" "02122" "0221" "02122"
#> [2521] "02121" "021122" "0221" "02221" "0221" "02122" "021122" "0221" "02122" "02113"
#> [2531] "0223" "02122" "021121" "0141" "02121" "0321" "0221" "0221" "0221" "0231"
#> [2541] "0221" "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221"
#> [2551] "0321" "0321" "0142" "0141" "02121" "0321" "0221" "0141" "021122" "02121"
#> [2561] "0321" "02122" "0321" "0223" "0221" "0321" "0221" "0221" "0221" "0221"
#> [2571] "0223" "0142" "0141" "0141" "0321" "0321" "0221" "0221" "021121" "02122"
#> [2581] "02122" "0223" "0223" "0221" "0221" "02221" "0221" "0142" "021112" "0232"
#> [2591] "0234" "0232" "02113" "02113" "021111" "02113" "02113" "021111" "0231" "02113"
#> [2601] "021111" "021111" "0232" "02113" "0232" "0231" "0234" "0232" "0323" "0142"
#> [2611] "0232" "021121" "0231" "0221" "0223" "0321" "0221" "0231" "0231" "0234"
#> [2621] "0233" "0232" "0142" "021122" "02222" "0231" "0142" "0142" "0141" "0231"
#> [2631] "021121" "021122" "02121" "021122" "021121" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221"
#> [2651] "01221" "0221" "0221" "0221" "0221" "0221" "0231" "0221" "02221" "0221"
#> [2661] "0221" "0221" "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321"
#> [2671] "0221" "0141" "0321" "0221" "0321" "0221" "0221" "0324" "01231" "0141"
#> [2681] "01231" "0221" "0141" "01231" "01212" "0232" "0232" "021122" "021122" "0321"
#> [2691] "02121" "02121" "0234" "0231" "0143" "0221" "0324" "02121" "0221" "0321"
#> [2701] "0221" "02121" "0141" "02221" "02221" "0321" "0142" "02221" "0141" "0142"
#> [2711] "02221" "0141" "0141" "0231" "02221" "0231" "0141" "0142" "0231" "0141"
#> [2721] "0223" "02221" "0141" "021122" "0321" "0141" "0321" "0141" "01231" "0321"
#> [2731] "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141" "0321"
#> [2741] "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "021111" "021111" "021111"
#> [2761] "021111" "021111" "021111" "021111" "0232" "0142" "0142" "0221" "021111" "02113"
#> [2771] "021112" "021112" "021111" "021111" "021111" "021112" "021111" "021111" "021112" "021112"
#> [2781] "021112" "0231" "021111" "021111" "021111" "0232" "021111" "0232" "021111" "0142"
#> [2791] "0142" "0223" "0231" "0231" "021112" "021112" "021112" "021112" "021111" "021111"
#> [2801] "021111" "02113" "0233" "021112" "02113" "021111" "021112" "0232" "021111" "021111"
#> [2811] "021111" "021111" "021112" "021112" "021111" "021112" "0221" "0142" "0142" "0142"
#> [2821] "0221" "02121" "0231" "021112" "021112" "02121" "021112" "02121" "021112" "0223"
#> [2831] "02121" "02121" "021112" "02121" "0231" "0223" "02121" "02121" "0232" "0231"
#> [2841] "021112" "021112" "021121" "021121" "02121" "021111" "021121" "021112" "021112" "021121"
#> [2851] "021112" "021111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "021122"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221"
#> [2871] "021112" "02121" "02123" "021111" "021112" "02121" "0223" "02121" "0142" "02121"
#> [2881] "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 438))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232"
#> [11] "01232" "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131"
#> [21] "0322" "01232" "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121"
#> [31] "0322" "01232" "0322" "0322" "0322" "01232" "01232" "01232" "03121" "0311"
#> [41] "01131" "02222" "01131" "0311" "01232" "01232" "0311" "03121" "0322" "01131"
#> [51] "03121" "01131" "01131" "01232" "01232" "02123" "02123" "0143" "01133" "0313"
#> [61] "0322" "01131" "01131" "02221" "0311" "01132" "01232" "01232" "0322" "01232"
#> [71] "0322" "0311" "0322" "01131" "01131" "0143" "0111" "0112" "02221" "01131"
#> [81] "0143" "0322" "01131" "0143" "01133" "02221" "01131" "01131" "01131" "01231"
#> [91] "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143" "0313"
#> [101] "01131" "01131" "0111" "01133" "0111" "0322" "02221" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "02221" "02221" "0322" "01132" "0321"
#> [121] "0313" "0322" "02222" "02221" "02222" "0234" "01231" "0111" "01133" "01133"
#> [131] "01231" "01131" "01133" "0324" "0111" "02222" "01131" "01131" "0322" "0111"
#> [141] "01131" "0111" "01232" "01231" "01231" "02222" "01131" "02123" "01131" "0324"
#> [151] "0313" "0313" "01131" "0313" "0322" "01131" "0313" "0234" "0322" "0322"
#> [161] "0322" "01131" "0313" "0313" "02222" "01131" "0322" "0313" "01131" "01131"
#> [171] "0322" "0313" "0313" "02222" "02222" "0313" "0313" "01131" "0313" "0313"
#> [181] "03121" "0313" "0322" "0313" "0322" "0313" "0313" "0313" "03121" "02222"
#> [191] "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121" "03121" "031221"
#> [201] "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322" "02222"
#> [211] "0313" "0234" "0313" "03121" "0313" "0313" "0322" "02222" "03121" "01133"
#> [221] "03121" "0313" "031221" "0313" "03121" "0313" "03121" "03121" "01131" "02113"
#> [231] "0313" "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313"
#> [241] "0313" "0313" "03121" "01133" "03121" "03121" "03121" "02222" "03121" "0313"
#> [251] "01133" "0313" "03121" "03121" "0313" "0313" "01133" "03121" "0313" "0313"
#> [261] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "01133" "0313"
#> [271] "01133" "01133" "0313" "0313" "01133" "0313" "0313" "0313" "0313" "0322"
#> [281] "02123" "01133" "0313" "0313" "0313" "02222" "0313" "0313" "03121" "03121"
#> [291] "03121" "031221" "03121" "03121" "03121" "03121" "03121" "031221" "02113" "03121"
#> [301] "02113" "0313" "0313" "0234" "0313" "02113" "02222" "031221" "02222" "03121"
#> [311] "03121" "0313" "02222" "0313" "0313" "03121" "01133" "0313" "0313" "01133"
#> [321] "0313" "01133" "03121" "0313" "0311" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "02222" "02222" "01132" "0313" "0112"
#> [341] "0313" "0313" "02222" "0313" "0313" "0313" "02222" "0311" "0311" "02222"
#> [351] "0313" "01133" "0313" "0313" "0313" "0313" "031221" "03121" "031221" "031221"
#> [361] "03121" "0313" "0313" "0313" "0313" "0313" "0313" "03121" "03121" "03121"
#> [371] "0313" "031221" "031221" "03121" "03121" "03121" "03121" "031221" "031221" "0313"
#> [381] "0111" "0112" "02222" "0311" "0112" "0111" "0112" "0311" "0324" "0311"
#> [391] "0112" "0311" "0112" "0311" "02222" "0311" "0313" "0311" "0112" "0111"
#> [401] "0311" "0112" "0143" "0311" "0112" "02222" "0111" "0311" "0112" "0112"
#> [411] "0311" "0311" "02123" "0112" "0112" "0112" "0111" "01133" "0311" "0111"
#> [421] "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112" "0112"
#> [431] "0112" "0234" "0112" "0234" "0111" "02221" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "031221" "031221" "03121"
#> [451] "0311" "0112" "0311" "0112" "0112" "03121" "0112" "0112" "031222" "031222"
#> [461] "0311" "031221" "0311" "0311" "031221" "031221" "031222" "0311" "031221" "031221"
#> [471] "031222" "0311" "031222" "031221" "031221" "0311" "0311" "03121" "0311" "0311"
#> [481] "0311" "0311" "0311" "0112" "02123" "031222" "0311" "0311" "02222" "02222"
#> [491] "02123" "03121" "031222" "02222" "031222" "0112" "02123" "02113" "0112" "031222"
#> [501] "02113" "0112" "0311" "031221" "0311" "02113" "0112" "0311" "0311" "0311"
#> [511] "0311" "02222" "0311" "0311" "0112" "0112" "02222" "0311" "03121" "0311"
#> [521] "0112" "0112" "0112" "031221" "03121" "0313" "03121" "0112" "0112" "02221"
#> [531] "02123" "02123" "0112" "02222" "0111" "0111" "0111" "02123" "0111" "0311"
#> [541] "0112" "02222" "0111" "0112" "02222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143"
#> [561] "0311" "01132" "0324" "0324" "01132" "0112" "0111" "02221" "0311" "0112"
#> [571] "0112" "02221" "0324" "0311" "0112" "03121" "0111" "0112" "0112" "02221"
#> [581] "0112" "0112" "0111" "0112" "0311" "0112" "0311" "0112" "0111" "01132"
#> [591] "0111" "0313" "0112" "031222" "0313" "0324" "0112" "0313" "0313" "0111"
#> [601] "0111" "01132" "0111" "0313" "0111" "0112" "02222" "0111" "0111" "0111"
#> [611] "0111" "0111" "0112" "0111" "0111" "0234" "0311" "0311" "0112" "0311"
#> [621] "0311" "0313" "0112" "0311" "0112" "0311" "0311" "0311" "0311" "0311"
#> [631] "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112" "0112" "0112"
#> [641] "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "02222" "0112"
#> [651] "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "031222" "0311" "0311" "0311" "0311"
#> [671] "02222" "0112" "02222" "0311" "0313" "0234" "0311" "0311" "02222" "0112"
#> [681] "0311" "0311" "031222" "031222" "0311" "031222" "031222" "031222" "0311" "0311"
#> [691] "0311" "0112" "031222" "0311" "02222" "0311" "031221" "0112" "031222" "0143"
#> [701] "031221" "0112" "0111" "0311" "0311" "02222" "02222" "0112" "0324" "0112"
#> [711] "0324" "02123" "0111" "0112" "0111" "0112" "0111" "0111" "02221" "0311"
#> [721] "0311" "02221" "0234" "0112" "02221" "0311" "0311" "0311" "0112" "0112"
#> [731] "0311" "0112" "0111" "0311" "0112" "0112" "0111" "0111" "0111" "0311"
#> [741] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "031222" "031222" "031221"
#> [751] "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324" "0311"
#> [761] "02123" "02222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "031222" "0112" "0112" "0311" "0112" "02222" "0111" "0112" "02113" "0112"
#> [781] "0311" "0112" "0112" "0111" "0112" "0112" "031222" "0111" "0311" "0311"
#> [791] "0112" "0112" "031221" "02222" "0112" "031222" "0111" "0111" "0234" "0311"
#> [801] "031222" "02222" "0311" "0311" "0311" "0234" "0311" "0112" "0311" "0112"
#> [811] "0112" "0112" "0324" "0324" "01231" "0143" "0111" "0112" "0111" "02123"
#> [821] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [831] "0111" "0111" "0111" "01231" "0111" "0111" "0111" "0111" "0111" "0111"
#> [841] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0324" "01231"
#> [851] "01132" "0234" "0324" "02222" "0111" "0143" "0143" "0143" "0324" "0111"
#> [861] "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0311"
#> [871] "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324"
#> [891] "0111" "0311" "0111" "0111" "0111" "02221" "02221" "0111" "0111" "0111"
#> [901] "0111" "0111" "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111"
#> [911] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [921] "0111" "0111" "0111" "0112" "0111" "0111" "0111" "0111" "02221" "0111"
#> [931] "0143" "0111" "0111" "0111" "02221" "0324" "0111" "02221" "0111" "0111"
#> [941] "0111" "0143" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [951] "0111" "0111" "0111" "0311" "01132" "02123" "0143" "0143" "0111" "02123"
#> [961] "02221" "0112" "0111" "02123" "0311" "0112" "0111" "0111" "0111" "0311"
#> [971] "0111" "0111" "02221" "0112" "01231" "0111" "0111" "0111" "0111" "0111"
#> [981] "0324" "0324" "02222" "02221" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111"
#> [1001] "02221" "0132" "01211" "01212" "01212" "01212" "01212" "01212" "01212" "01212"
#> [1011] "01212" "01212" "01212" "02221" "01212" "02221" "01212" "01212" "01212" "01212"
#> [1021] "01212" "01212" "01212" "01212" "01212" "0323" "0142" "01212" "01212" "01212"
#> [1031] "01212" "01212" "01212" "01212" "01212" "0311" "01212" "03121" "01212" "02113"
#> [1041] "01212" "01132" "0322" "01212" "01212" "01212" "03121" "01211" "01211" "01212"
#> [1051] "01132" "01212" "01212" "01211" "01211" "01211" "01212" "01212" "01132" "01211"
#> [1061] "01211" "01212" "01212" "01211" "01211" "01212" "01212" "01212" "01212" "01211"
#> [1071] "01211" "01212" "0132" "01212" "0322" "01211" "01211" "01211" "0132" "01211"
#> [1081] "01212" "01211" "03121" "01211" "01211" "01211" "01211" "0311" "01211" "01212"
#> [1091] "01211" "01212" "01132" "0132" "01211" "01211" "01211" "01212" "01211" "01212"
#> [1101] "0142" "01212" "01212" "01212" "01212" "01212" "01211" "01211" "01212" "0311"
#> [1111] "01212" "01212" "01212" "01212" "01212" "0132" "01212" "0313" "01212" "0141"
#> [1121] "01212" "0313" "01212" "01212" "01211" "01212" "01211" "031221" "01212" "01211"
#> [1131] "01211" "01132" "01132" "01211" "0322" "01211" "01212" "01211" "01211" "01211"
#> [1141] "0221" "01211" "01211" "01132" "0322" "01232" "01211" "0111" "01211" "0142"
#> [1151] "01211" "01211" "01231" "01232" "01211" "01211" "01211" "01211" "01211" "01211"
#> [1161] "01132" "01232" "01132" "01211" "01211" "01211" "01211" "0111" "01132" "0111"
#> [1171] "01231" "0311" "01132" "01211" "01132" "01132" "01211" "01211" "01211" "01211"
#> [1181] "0223" "01211" "0141" "01211" "0221" "0111" "01211" "01211" "01132" "01211"
#> [1191] "01211" "01211" "01211" "01211" "01211" "0141" "01211" "01231" "0131" "01211"
#> [1201] "01211" "0141" "01211" "01211" "01211" "01211" "01211" "0112" "01211" "01211"
#> [1211] "0131" "01211" "01232" "0141" "02221" "01211" "0321" "0313" "01211" "01211"
#> [1221] "01131" "01211" "01211" "0221" "01211" "0223" "01232" "01211" "01211" "0141"
#> [1231] "01211" "01211" "01211" "01132" "01211" "01131" "0112" "0313" "0141" "01211"
#> [1241] "0333" "0321" "0311" "01211" "01211" "01211" "01132" "01132" "01211" "01211"
#> [1251] "01132" "01234" "0112" "0111" "0112" "0221" "0221" "0221" "0223" "01223"
#> [1261] "01212" "01223" "01211" "01234" "0311" "0141" "0111" "01132" "0132" "0132"
#> [1271] "0132" "0131" "0331" "0131" "0131" "0132" "0333" "02122" "0332" "0332"
#> [1281] "0331" "0132" "0332" "0132" "0132" "0132" "0132" "0132" "0332" "0332"
#> [1291] "0132" "0331" "0132" "0132" "0233" "0333" "0132" "0132" "0132" "0131"
#> [1301] "0132" "0131" "0131" "0132" "0131" "0131" "0131" "0132" "0132" "0132"
#> [1311] "0132" "0132" "0223" "0331" "0221" "0131" "0333" "02122" "0132" "0131"
#> [1321] "0131" "0132" "0132" "0132" "0131" "0132" "0333" "01223" "0131" "0131"
#> [1331] "0132" "02113" "0132" "0331" "0132" "0333" "0132" "0132" "0132" "0131"
#> [1341] "0132" "0132" "02221" "0132" "0131" "0223" "0233" "02113" "0131" "0132"
#> [1351] "0131" "0131" "02113" "0223" "0132" "0131" "0131" "0132" "0131" "0131"
#> [1361] "02122" "02122" "0131" "0132" "0331" "0331" "0131" "0331" "0132" "0331"
#> [1371] "0331" "0331" "0332" "0132" "0331" "0132" "0132" "0131" "0131" "0132"
#> [1381] "0131" "0132" "0132" "0132" "0132" "0132" "0132" "01234" "01223" "01231"
#> [1391] "01234" "0321" "0131" "0131" "0231" "0141" "02113" "0233" "0233" "01231"
#> [1401] "0233" "0132" "0132" "0131" "0131" "0333" "0233" "0131" "0131" "0311"
#> [1411] "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0333" "0131"
#> [1421] "0131" "0311" "0131" "0131" "0131" "0132" "0131" "0131" "0333" "0311"
#> [1431] "0131" "0112" "0131" "0311" "01231" "0131" "0131" "0131" "0131" "0131"
#> [1441] "0131" "0131" "0131" "0131" "0131" "01231" "0131" "0131" "0233" "0131"
#> [1451] "0333" "0221" "0132" "0131" "0221" "0131" "0131" "0131" "0223" "0131"
#> [1461] "0131" "0131" "0132" "0132" "01231" "0131" "0111" "0111" "01131" "0132"
#> [1471] "0131" "0333" "01231" "0313" "0333" "0313" "0112" "02121" "0131" "0221"
#> [1481] "01232" "0131" "0132" "0111" "0131" "0131" "0131" "0321" "0141" "0131"
#> [1491] "0141" "0131" "0131" "0111" "0231" "0141" "0131" "0111" "0131" "0233"
#> [1501] "01231" "0141" "0131" "0111" "01231" "0321" "0132" "02222" "0131" "0223"
#> [1511] "01231" "0131" "01231" "0132" "0131" "01231" "0131" "0221" "0331" "0221"
#> [1521] "0233" "0233" "0142" "0221" "0142" "0132" "0333" "0132" "0132" "0131"
#> [1531] "0142" "0131" "0132" "02113" "01223" "0223" "0112" "0111" "0132" "0131"
#> [1541] "01232" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131"
#> [1551] "0311" "0131" "0111" "0131" "0131" "0131" "0142" "02121" "0233" "0131"
#> [1561] "01231" "01231" "0143" "03121" "0223" "01133" "0132" "0333" "0131" "01231"
#> [1571] "0131" "0223" "02121" "0142" "02121" "0332" "0332" "02113" "0233" "0233"
#> [1581] "0332" "02113" "0332" "0233" "0332" "0332" "0331" "0332" "0331" "0332"
#> [1591] "0132" "0331" "0332" "02221" "0331" "02113" "02121" "0233" "0132" "02113"
#> [1601] "0132" "0332" "0132" "02123" "02113" "0132" "0132" "0233" "02113" "02113"
#> [1611] "0331" "0331" "0332" "0331" "0331" "0331" "0331" "02113" "0132" "02221"
#> [1621] "02113" "0233" "0132" "0331" "01132" "02122" "01234" "0132" "01234" "0132"
#> [1631] "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221" "01234"
#> [1641] "01234" "0132" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234"
#> [1661] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234"
#> [1671] "01234" "01231" "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233"
#> [1681] "01233" "01233" "01221" "0141" "01233" "01212" "01132" "01211" "01232" "01223"
#> [1691] "01233" "01233" "0322" "01212" "01233" "01233" "01233" "0112" "01233" "01233"
#> [1701] "0112" "01233" "0311" "01233" "01233" "01221" "0323" "01223" "01233" "01233"
#> [1711] "0131" "0311" "01233" "01233" "01223" "0132" "01233" "01233" "0323" "0323"
#> [1721] "0131" "01233" "0141" "01233" "01233" "01233" "01233" "0313" "01233" "0311"
#> [1731] "0311" "01233" "01211" "02121" "01231" "01133" "01223" "01133" "0112" "0111"
#> [1741] "01221" "01223" "0132" "01221" "0131" "01221" "01222" "01223" "0323" "01222"
#> [1751] "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "031221"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "0131" "0221"
#> [1771] "01221" "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221"
#> [1781] "0322" "01132" "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323"
#> [1791] "01222" "0313" "0313" "0323" "0223" "01132" "01221" "0313" "0223" "01221"
#> [1801] "01221" "01221" "01222" "0323" "01221" "01221" "0233" "02121" "0223" "0311"
#> [1811] "0221" "01221" "01221" "01131" "01223" "01221" "01221" "01221" "01221" "01221"
#> [1821] "0313" "01221" "01221" "01221" "01221" "01221" "01221" "01221" "01222" "0223"
#> [1831] "01221" "01221" "0323" "01221" "01222" "02122" "0223" "01221" "0111" "01221"
#> [1841] "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221" "01222" "01221"
#> [1851] "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322" "01233"
#> [1861] "031222" "01233" "01233" "0332" "0223" "031221" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "01211"
#> [1881] "021121" "01223" "01223" "01223" "01223" "0323" "01222" "031222" "01222" "01222"
#> [1891] "01132" "0221" "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121"
#> [1901] "0132" "0323" "01223" "0311" "01223" "01223" "0332" "01223" "01222" "01222"
#> [1911] "01222" "01222" "01222" "01221" "0323" "01222" "01221" "01132" "01221" "031221"
#> [1921] "0223" "01222" "0323" "0323" "01222" "0311" "01222" "01222" "01222" "0233"
#> [1931] "0323" "01222" "021121" "01222" "0323" "0233" "0333" "01222" "01222" "0323"
#> [1941] "0323" "01222" "0323" "01222" "0332" "02221" "031221" "0323" "01222" "03121"
#> [1951] "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323" "02113" "0323"
#> [1961] "0221" "0323" "0323" "02221" "01222" "0323" "021121" "0331" "0323" "031222"
#> [1971] "01222" "0233" "031222" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "021122" "0232" "02121" "02122" "02122" "021122"
#> [1991] "0221" "02122" "0231" "0232" "0223" "02123" "0231" "0231" "021122" "0231"
#> [2001] "0223" "02113" "021121" "0232" "021122" "02221" "0221" "02121" "0232" "0232"
#> [2011] "02123" "0231" "02121" "0231" "0142" "0221" "0231" "0321" "0223" "021122"
#> [2021] "02122" "02221" "0223" "0221" "02221" "0321" "0223" "02122" "02122" "0223"
#> [2031] "02221" "02122" "0223" "0232" "0221" "02113" "0221" "021121" "0223" "0223"
#> [2041] "0221" "0321" "021121" "0233" "0232" "02113" "02122" "02121" "02121" "0142"
#> [2051] "0221" "02113" "0231" "02113" "021122" "02121" "0223" "02122" "0321" "0223"
#> [2061] "021121" "0223" "0223" "02122" "0221" "0223" "02122" "02122" "02122" "021121"
#> [2071] "021121" "0223" "0232" "02221" "02113" "0233" "021122" "02221" "021121" "021121"
#> [2081] "021121" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231"
#> [2101] "02121" "02121" "02121" "02122" "021121" "021121" "02121" "021122" "0231" "021122"
#> [2111] "0231" "0223" "021121" "021122" "021122" "021122" "0223" "02123" "0231" "0232"
#> [2121] "02121" "02121" "0233" "0232" "0142" "0223" "02121" "0142" "021121" "021122"
#> [2131] "02122" "02121" "021122" "021122" "021121" "02121" "02122" "02121" "0221" "02121"
#> [2141] "02221" "0223" "02122" "0221" "0221" "02221" "0223" "02121" "0223" "02121"
#> [2151] "021121" "02122" "0223" "0223" "02122" "021121" "02121" "0223" "0223" "021121"
#> [2161] "0221" "0223" "0221" "02122" "0223" "0223" "0221" "02121" "0223" "0223"
#> [2171] "0223" "0221" "02121" "0321" "0221" "0221" "0221" "021112" "02122" "02122"
#> [2181] "02122" "0223" "0234" "02222" "0223" "0221" "0221" "0221" "0221" "0143"
#> [2191] "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "021121" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "02221" "02121" "02121" "02121" "0231"
#> [2211] "0232" "0221" "0232" "0223" "02121" "02123" "021122" "021121" "02121" "02121"
#> [2221] "0223" "02123" "02121" "02121" "0221" "021122" "021122" "02121" "0223" "02121"
#> [2231] "0223" "0223" "0223" "0223" "02121" "0221" "0321" "02221" "0221" "0321"
#> [2241] "0221" "0321" "0223" "0221" "0223" "0223" "0223" "0231" "0231" "0221"
#> [2251] "02221" "0321" "02221" "0221" "0231" "0231" "0221" "0221" "0141" "0321"
#> [2261] "021122" "0221" "0221" "0221" "0223" "0321" "0231" "0221" "0321" "0223"
#> [2271] "0223" "0223" "0142" "0223" "0142" "02221" "0223" "0321" "0221" "0231"
#> [2281] "02221" "0221" "0141" "02221" "0221" "0221" "0142" "0321" "0321" "0221"
#> [2291] "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141" "0321"
#> [2301] "0142" "0142" "0141" "0223" "0142" "02221" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221"
#> [2321] "0142" "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221"
#> [2331] "0223" "0221" "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141"
#> [2341] "0321" "0321" "0221" "02122" "0232" "0223" "0223" "0223" "0221" "0221"
#> [2351] "0321" "02221" "0223" "0223" "0221" "0221" "0321" "02121" "021122" "0221"
#> [2361] "02121" "0221" "02121" "0234" "02121" "02122" "0221" "021122" "021122" "0221"
#> [2371] "0223" "0223" "02121" "0223" "02121" "0223" "0221" "0221" "02123" "02121"
#> [2381] "0232" "0223" "021121" "02122" "0232" "0221" "0223" "0223" "0223" "0231"
#> [2391] "02113" "0223" "0221" "0221" "021112" "02121" "02122" "0223" "0321" "0221"
#> [2401] "0141" "0141" "0141" "02122" "0221" "0231" "021112" "0223" "02122" "02221"
#> [2411] "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223"
#> [2431] "0221" "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141"
#> [2441] "0141" "01234" "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223"
#> [2451] "02122" "02122" "02122" "021121" "02122" "02122" "021121" "02122" "02122" "0231"
#> [2461] "02122" "02122" "02122" "02123" "02122" "02123" "02122" "02122" "02221" "0221"
#> [2471] "0321" "0221" "0221" "0221" "02122" "02122" "0223" "02122" "0223" "0221"
#> [2481] "0223" "02122" "0223" "0223" "021121" "0223" "02122" "02122" "02122" "021121"
#> [2491] "02123" "02122" "02122" "021122" "0223" "02122" "0223" "02122" "02122" "0221"
#> [2501] "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321" "0321" "0221"
#> [2511] "0324" "02122" "02122" "021121" "02122" "02122" "021121" "02122" "0221" "02122"
#> [2521] "02121" "021122" "0221" "02221" "0221" "02122" "021122" "0221" "02122" "02113"
#> [2531] "0223" "02122" "021121" "0141" "02121" "0321" "0221" "0221" "0221" "0231"
#> [2541] "0221" "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221"
#> [2551] "0321" "0321" "0142" "0141" "02121" "0321" "0221" "0141" "021122" "02121"
#> [2561] "0321" "02122" "0321" "0223" "0221" "0321" "0221" "0221" "0221" "0221"
#> [2571] "0223" "0142" "0141" "0141" "0321" "0321" "0221" "0221" "021121" "02122"
#> [2581] "02122" "0223" "0223" "0221" "0221" "02221" "0221" "0142" "021112" "0232"
#> [2591] "0234" "0232" "02113" "02113" "021111" "02113" "02113" "021111" "0231" "02113"
#> [2601] "021111" "021111" "0232" "02113" "0232" "0231" "0234" "0232" "0323" "0142"
#> [2611] "0232" "021121" "0231" "0221" "0223" "0321" "0221" "0231" "0231" "0234"
#> [2621] "0233" "0232" "0142" "021122" "02222" "0231" "0142" "0142" "0141" "0231"
#> [2631] "021121" "021122" "02121" "021122" "021121" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221"
#> [2651] "01221" "0221" "0221" "0221" "0221" "0221" "0231" "0221" "02221" "0221"
#> [2661] "0221" "0221" "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321"
#> [2671] "0221" "0141" "0321" "0221" "0321" "0221" "0221" "0324" "01231" "0141"
#> [2681] "01231" "0221" "0141" "01231" "01212" "0232" "0232" "021122" "021122" "0321"
#> [2691] "02121" "02121" "0234" "0231" "0143" "0221" "0324" "02121" "0221" "0321"
#> [2701] "0221" "02121" "0141" "02221" "02221" "0321" "0142" "02221" "0141" "0142"
#> [2711] "02221" "0141" "0141" "0231" "02221" "0231" "0141" "0142" "0231" "0141"
#> [2721] "0223" "02221" "0141" "021122" "0321" "0141" "0321" "0141" "01231" "0321"
#> [2731] "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141" "0321"
#> [2741] "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "021111" "021111" "021111"
#> [2761] "021111" "021111" "021111" "021111" "0232" "0142" "0142" "0221" "021111" "02113"
#> [2771] "021112" "021112" "021111" "021111" "021111" "021112" "021111" "021111" "021112" "021112"
#> [2781] "021112" "0231" "021111" "021111" "021111" "0232" "021111" "0232" "021111" "0142"
#> [2791] "0142" "0223" "0231" "0231" "021112" "021112" "021112" "021112" "021111" "021111"
#> [2801] "021111" "02113" "0233" "021112" "02113" "021111" "021112" "0232" "021111" "021111"
#> [2811] "021111" "021111" "021112" "021112" "021111" "021112" "0221" "0142" "0142" "0142"
#> [2821] "0221" "02121" "0231" "021112" "021112" "02121" "021112" "02121" "021112" "0223"
#> [2831] "02121" "02121" "021112" "02121" "0231" "0223" "02121" "02121" "0232" "0231"
#> [2841] "021112" "021112" "021121" "021121" "02121" "021111" "021121" "021112" "021112" "021121"
#> [2851] "021112" "021111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "021122"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221"
#> [2871] "021112" "02121" "02123" "021111" "021112" "02121" "0223" "02121" "0142" "02121"
#> [2881] "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 508))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232"
#> [11] "01232" "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131"
#> [21] "0322" "01232" "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121"
#> [31] "0322" "01232" "0322" "0322" "0322" "01232" "01232" "01232" "03121" "0311"
#> [41] "01131" "02222" "01131" "0311" "01232" "01232" "0311" "03121" "0322" "01131"
#> [51] "03121" "01131" "01131" "01232" "01232" "02123" "02123" "0143" "01133" "0313"
#> [61] "0322" "01131" "01131" "02221" "0311" "01132" "01232" "01232" "0322" "01232"
#> [71] "0322" "0311" "0322" "01131" "01131" "0143" "0111" "0112" "02221" "01131"
#> [81] "0143" "0322" "01131" "0143" "01133" "02221" "01131" "01131" "01131" "01231"
#> [91] "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143" "0313"
#> [101] "01131" "01131" "0111" "01133" "0111" "0322" "02221" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "02221" "02221" "0322" "01132" "0321"
#> [121] "0313" "0322" "02222" "02221" "02222" "0234" "01231" "0111" "01133" "01133"
#> [131] "01231" "01131" "01133" "0324" "0111" "02222" "01131" "01131" "0322" "0111"
#> [141] "01131" "0111" "01232" "01231" "01231" "02222" "01131" "02123" "01131" "0324"
#> [151] "0313" "0313" "01131" "0313" "0322" "01131" "0313" "0234" "0322" "0322"
#> [161] "0322" "01131" "0313" "0313" "02222" "01131" "0322" "0313" "01131" "01131"
#> [171] "0322" "0313" "0313" "02222" "02222" "0313" "0313" "01131" "0313" "0313"
#> [181] "03121" "0313" "0322" "0313" "0322" "0313" "0313" "0313" "03121" "02222"
#> [191] "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121" "03121" "031221"
#> [201] "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322" "02222"
#> [211] "0313" "0234" "0313" "03121" "0313" "0313" "0322" "02222" "03121" "01133"
#> [221] "03121" "0313" "031221" "0313" "03121" "0313" "03121" "03121" "01131" "02113"
#> [231] "0313" "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313"
#> [241] "0313" "0313" "03121" "01133" "03121" "03121" "03121" "02222" "03121" "0313"
#> [251] "01133" "0313" "03121" "03121" "0313" "0313" "01133" "03121" "0313" "0313"
#> [261] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "01133" "0313"
#> [271] "01133" "01133" "0313" "0313" "01133" "0313" "0313" "0313" "0313" "0322"
#> [281] "02123" "01133" "0313" "0313" "0313" "02222" "0313" "0313" "03121" "03121"
#> [291] "03121" "031221" "03121" "03121" "03121" "03121" "03121" "031221" "02113" "03121"
#> [301] "02113" "0313" "0313" "0234" "0313" "02113" "02222" "031221" "02222" "03121"
#> [311] "03121" "0313" "02222" "0313" "0313" "03121" "01133" "0313" "0313" "01133"
#> [321] "0313" "01133" "03121" "0313" "0311" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "02222" "02222" "01132" "0313" "0112"
#> [341] "0313" "0313" "02222" "0313" "0313" "0313" "02222" "0311" "0311" "02222"
#> [351] "0313" "01133" "0313" "0313" "0313" "0313" "031221" "03121" "031221" "031221"
#> [361] "03121" "0313" "0313" "0313" "0313" "0313" "0313" "03121" "03121" "03121"
#> [371] "0313" "031221" "031221" "03121" "03121" "03121" "03121" "031221" "031221" "0313"
#> [381] "0111" "0112" "02222" "0311" "0112" "0111" "0112" "0311" "0324" "0311"
#> [391] "0112" "0311" "0112" "0311" "02222" "0311" "0313" "0311" "0112" "0111"
#> [401] "0311" "0112" "0143" "0311" "0112" "02222" "0111" "0311" "0112" "0112"
#> [411] "0311" "0311" "02123" "0112" "0112" "0112" "0111" "01133" "0311" "0111"
#> [421] "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112" "0112"
#> [431] "0112" "0234" "0112" "0234" "0111" "02221" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "031221" "031221" "03121"
#> [451] "0311" "0112" "0311" "0112" "0112" "03121" "0112" "0112" "031222" "031222"
#> [461] "0311" "031221" "0311" "0311" "031221" "031221" "031222" "0311" "031221" "031221"
#> [471] "031222" "0311" "031222" "031221" "031221" "0311" "0311" "03121" "0311" "0311"
#> [481] "0311" "0311" "0311" "0112" "02123" "031222" "0311" "0311" "02222" "02222"
#> [491] "02123" "03121" "031222" "02222" "031222" "0112" "02123" "02113" "0112" "031222"
#> [501] "02113" "0112" "0311" "031221" "0311" "02113" "0112" "0311" "0311" "0311"
#> [511] "0311" "02222" "0311" "0311" "0112" "0112" "02222" "0311" "03121" "0311"
#> [521] "0112" "0112" "0112" "031221" "03121" "0313" "03121" "0112" "0112" "02221"
#> [531] "02123" "02123" "0112" "02222" "0111" "0111" "0111" "02123" "0111" "0311"
#> [541] "0112" "02222" "0111" "0112" "02222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143"
#> [561] "0311" "01132" "0324" "0324" "01132" "0112" "0111" "02221" "0311" "0112"
#> [571] "0112" "02221" "0324" "0311" "0112" "03121" "0111" "0112" "0112" "02221"
#> [581] "0112" "0112" "0111" "0112" "0311" "0112" "0311" "0112" "0111" "01132"
#> [591] "0111" "0313" "0112" "031222" "0313" "0324" "0112" "0313" "0313" "0111"
#> [601] "0111" "01132" "0111" "0313" "0111" "0112" "02222" "0111" "0111" "0111"
#> [611] "0111" "0111" "0112" "0111" "0111" "0234" "0311" "0311" "0112" "0311"
#> [621] "0311" "0313" "0112" "0311" "0112" "0311" "0311" "0311" "0311" "0311"
#> [631] "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112" "0112" "0112"
#> [641] "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "02222" "0112"
#> [651] "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "031222" "0311" "0311" "0311" "0311"
#> [671] "02222" "0112" "02222" "0311" "0313" "0234" "0311" "0311" "02222" "0112"
#> [681] "0311" "0311" "031222" "031222" "0311" "031222" "031222" "031222" "0311" "0311"
#> [691] "0311" "0112" "031222" "0311" "02222" "0311" "031221" "0112" "031222" "0143"
#> [701] "031221" "0112" "0111" "0311" "0311" "02222" "02222" "0112" "0324" "0112"
#> [711] "0324" "02123" "0111" "0112" "0111" "0112" "0111" "0111" "02221" "0311"
#> [721] "0311" "02221" "0234" "0112" "02221" "0311" "0311" "0311" "0112" "0112"
#> [731] "0311" "0112" "0111" "0311" "0112" "0112" "0111" "0111" "0111" "0311"
#> [741] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "031222" "031222" "031221"
#> [751] "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324" "0311"
#> [761] "02123" "02222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "031222" "0112" "0112" "0311" "0112" "02222" "0111" "0112" "02113" "0112"
#> [781] "0311" "0112" "0112" "0111" "0112" "0112" "031222" "0111" "0311" "0311"
#> [791] "0112" "0112" "031221" "02222" "0112" "031222" "0111" "0111" "0234" "0311"
#> [801] "031222" "02222" "0311" "0311" "0311" "0234" "0311" "0112" "0311" "0112"
#> [811] "0112" "0112" "0324" "0324" "01231" "0143" "0111" "0112" "0111" "02123"
#> [821] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [831] "0111" "0111" "0111" "01231" "0111" "0111" "0111" "0111" "0111" "0111"
#> [841] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0324" "01231"
#> [851] "01132" "0234" "0324" "02222" "0111" "0143" "0143" "0143" "0324" "0111"
#> [861] "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0311"
#> [871] "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324"
#> [891] "0111" "0311" "0111" "0111" "0111" "02221" "02221" "0111" "0111" "0111"
#> [901] "0111" "0111" "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111"
#> [911] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [921] "0111" "0111" "0111" "0112" "0111" "0111" "0111" "0111" "02221" "0111"
#> [931] "0143" "0111" "0111" "0111" "02221" "0324" "0111" "02221" "0111" "0111"
#> [941] "0111" "0143" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [951] "0111" "0111" "0111" "0311" "01132" "02123" "0143" "0143" "0111" "02123"
#> [961] "02221" "0112" "0111" "02123" "0311" "0112" "0111" "0111" "0111" "0311"
#> [971] "0111" "0111" "02221" "0112" "01231" "0111" "0111" "0111" "0111" "0111"
#> [981] "0324" "0324" "02222" "02221" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111"
#> [1001] "02221" "0132" "01211" "01212" "01212" "01212" "01212" "01212" "01212" "01212"
#> [1011] "01212" "01212" "01212" "02221" "01212" "02221" "01212" "01212" "01212" "01212"
#> [1021] "01212" "01212" "01212" "01212" "01212" "0323" "0142" "01212" "01212" "01212"
#> [1031] "01212" "01212" "01212" "01212" "01212" "0311" "01212" "03121" "01212" "02113"
#> [1041] "01212" "01132" "0322" "01212" "01212" "01212" "03121" "01211" "01211" "01212"
#> [1051] "01132" "01212" "01212" "01211" "01211" "01211" "01212" "01212" "01132" "01211"
#> [1061] "01211" "01212" "01212" "01211" "01211" "01212" "01212" "01212" "01212" "01211"
#> [1071] "01211" "01212" "0132" "01212" "0322" "01211" "01211" "01211" "0132" "01211"
#> [1081] "01212" "01211" "03121" "01211" "01211" "01211" "01211" "0311" "01211" "01212"
#> [1091] "01211" "01212" "01132" "0132" "01211" "01211" "01211" "01212" "01211" "01212"
#> [1101] "0142" "01212" "01212" "01212" "01212" "01212" "01211" "01211" "01212" "0311"
#> [1111] "01212" "01212" "01212" "01212" "01212" "0132" "01212" "0313" "01212" "0141"
#> [1121] "01212" "0313" "01212" "01212" "01211" "01212" "01211" "031221" "01212" "01211"
#> [1131] "01211" "01132" "01132" "01211" "0322" "01211" "01212" "01211" "01211" "01211"
#> [1141] "0221" "01211" "01211" "01132" "0322" "01232" "01211" "0111" "01211" "0142"
#> [1151] "01211" "01211" "01231" "01232" "01211" "01211" "01211" "01211" "01211" "01211"
#> [1161] "01132" "01232" "01132" "01211" "01211" "01211" "01211" "0111" "01132" "0111"
#> [1171] "01231" "0311" "01132" "01211" "01132" "01132" "01211" "01211" "01211" "01211"
#> [1181] "0223" "01211" "0141" "01211" "0221" "0111" "01211" "01211" "01132" "01211"
#> [1191] "01211" "01211" "01211" "01211" "01211" "0141" "01211" "01231" "0131" "01211"
#> [1201] "01211" "0141" "01211" "01211" "01211" "01211" "01211" "0112" "01211" "01211"
#> [1211] "0131" "01211" "01232" "0141" "02221" "01211" "0321" "0313" "01211" "01211"
#> [1221] "01131" "01211" "01211" "0221" "01211" "0223" "01232" "01211" "01211" "0141"
#> [1231] "01211" "01211" "01211" "01132" "01211" "01131" "0112" "0313" "0141" "01211"
#> [1241] "0333" "0321" "0311" "01211" "01211" "01211" "01132" "01132" "01211" "01211"
#> [1251] "01132" "01234" "0112" "0111" "0112" "0221" "0221" "0221" "0223" "01223"
#> [1261] "01212" "01223" "01211" "01234" "0311" "0141" "0111" "01132" "0132" "0132"
#> [1271] "0132" "0131" "0331" "0131" "0131" "0132" "0333" "02122" "0332" "0332"
#> [1281] "0331" "0132" "0332" "0132" "0132" "0132" "0132" "0132" "0332" "0332"
#> [1291] "0132" "0331" "0132" "0132" "0233" "0333" "0132" "0132" "0132" "0131"
#> [1301] "0132" "0131" "0131" "0132" "0131" "0131" "0131" "0132" "0132" "0132"
#> [1311] "0132" "0132" "0223" "0331" "0221" "0131" "0333" "02122" "0132" "0131"
#> [1321] "0131" "0132" "0132" "0132" "0131" "0132" "0333" "01223" "0131" "0131"
#> [1331] "0132" "02113" "0132" "0331" "0132" "0333" "0132" "0132" "0132" "0131"
#> [1341] "0132" "0132" "02221" "0132" "0131" "0223" "0233" "02113" "0131" "0132"
#> [1351] "0131" "0131" "02113" "0223" "0132" "0131" "0131" "0132" "0131" "0131"
#> [1361] "02122" "02122" "0131" "0132" "0331" "0331" "0131" "0331" "0132" "0331"
#> [1371] "0331" "0331" "0332" "0132" "0331" "0132" "0132" "0131" "0131" "0132"
#> [1381] "0131" "0132" "0132" "0132" "0132" "0132" "0132" "01234" "01223" "01231"
#> [1391] "01234" "0321" "0131" "0131" "0231" "0141" "02113" "0233" "0233" "01231"
#> [1401] "0233" "0132" "0132" "0131" "0131" "0333" "0233" "0131" "0131" "0311"
#> [1411] "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0333" "0131"
#> [1421] "0131" "0311" "0131" "0131" "0131" "0132" "0131" "0131" "0333" "0311"
#> [1431] "0131" "0112" "0131" "0311" "01231" "0131" "0131" "0131" "0131" "0131"
#> [1441] "0131" "0131" "0131" "0131" "0131" "01231" "0131" "0131" "0233" "0131"
#> [1451] "0333" "0221" "0132" "0131" "0221" "0131" "0131" "0131" "0223" "0131"
#> [1461] "0131" "0131" "0132" "0132" "01231" "0131" "0111" "0111" "01131" "0132"
#> [1471] "0131" "0333" "01231" "0313" "0333" "0313" "0112" "02121" "0131" "0221"
#> [1481] "01232" "0131" "0132" "0111" "0131" "0131" "0131" "0321" "0141" "0131"
#> [1491] "0141" "0131" "0131" "0111" "0231" "0141" "0131" "0111" "0131" "0233"
#> [1501] "01231" "0141" "0131" "0111" "01231" "0321" "0132" "02222" "0131" "0223"
#> [1511] "01231" "0131" "01231" "0132" "0131" "01231" "0131" "0221" "0331" "0221"
#> [1521] "0233" "0233" "0142" "0221" "0142" "0132" "0333" "0132" "0132" "0131"
#> [1531] "0142" "0131" "0132" "02113" "01223" "0223" "0112" "0111" "0132" "0131"
#> [1541] "01232" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131"
#> [1551] "0311" "0131" "0111" "0131" "0131" "0131" "0142" "02121" "0233" "0131"
#> [1561] "01231" "01231" "0143" "03121" "0223" "01133" "0132" "0333" "0131" "01231"
#> [1571] "0131" "0223" "02121" "0142" "02121" "0332" "0332" "02113" "0233" "0233"
#> [1581] "0332" "02113" "0332" "0233" "0332" "0332" "0331" "0332" "0331" "0332"
#> [1591] "0132" "0331" "0332" "02221" "0331" "02113" "02121" "0233" "0132" "02113"
#> [1601] "0132" "0332" "0132" "02123" "02113" "0132" "0132" "0233" "02113" "02113"
#> [1611] "0331" "0331" "0332" "0331" "0331" "0331" "0331" "02113" "0132" "02221"
#> [1621] "02113" "0233" "0132" "0331" "01132" "02122" "01234" "0132" "01234" "0132"
#> [1631] "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221" "01234"
#> [1641] "01234" "0132" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234"
#> [1661] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234"
#> [1671] "01234" "01231" "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233"
#> [1681] "01233" "01233" "01221" "0141" "01233" "01212" "01132" "01211" "01232" "01223"
#> [1691] "01233" "01233" "0322" "01212" "01233" "01233" "01233" "0112" "01233" "01233"
#> [1701] "0112" "01233" "0311" "01233" "01233" "01221" "0323" "01223" "01233" "01233"
#> [1711] "0131" "0311" "01233" "01233" "01223" "0132" "01233" "01233" "0323" "0323"
#> [1721] "0131" "01233" "0141" "01233" "01233" "01233" "01233" "0313" "01233" "0311"
#> [1731] "0311" "01233" "01211" "02121" "01231" "01133" "01223" "01133" "0112" "0111"
#> [1741] "01221" "01223" "0132" "01221" "0131" "01221" "01222" "01223" "0323" "01222"
#> [1751] "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "031221"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "0131" "0221"
#> [1771] "01221" "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221"
#> [1781] "0322" "01132" "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323"
#> [1791] "01222" "0313" "0313" "0323" "0223" "01132" "01221" "0313" "0223" "01221"
#> [1801] "01221" "01221" "01222" "0323" "01221" "01221" "0233" "02121" "0223" "0311"
#> [1811] "0221" "01221" "01221" "01131" "01223" "01221" "01221" "01221" "01221" "01221"
#> [1821] "0313" "01221" "01221" "01221" "01221" "01221" "01221" "01221" "01222" "0223"
#> [1831] "01221" "01221" "0323" "01221" "01222" "02122" "0223" "01221" "0111" "01221"
#> [1841] "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221" "01222" "01221"
#> [1851] "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322" "01233"
#> [1861] "031222" "01233" "01233" "0332" "0223" "031221" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "01211"
#> [1881] "02112" "01223" "01223" "01223" "01223" "0323" "01222" "031222" "01222" "01222"
#> [1891] "01132" "0221" "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121"
#> [1901] "0132" "0323" "01223" "0311" "01223" "01223" "0332" "01223" "01222" "01222"
#> [1911] "01222" "01222" "01222" "01221" "0323" "01222" "01221" "01132" "01221" "031221"
#> [1921] "0223" "01222" "0323" "0323" "01222" "0311" "01222" "01222" "01222" "0233"
#> [1931] "0323" "01222" "02112" "01222" "0323" "0233" "0333" "01222" "01222" "0323"
#> [1941] "0323" "01222" "0323" "01222" "0332" "02221" "031221" "0323" "01222" "03121"
#> [1951] "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323" "02113" "0323"
#> [1961] "0221" "0323" "0323" "02221" "01222" "0323" "02112" "0331" "0323" "031222"
#> [1971] "01222" "0233" "031222" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112"
#> [1991] "0221" "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231"
#> [2001] "0223" "02113" "02112" "0232" "02112" "02221" "0221" "02121" "0232" "0232"
#> [2011] "02123" "0231" "02121" "0231" "0142" "0221" "0231" "0321" "0223" "02112"
#> [2021] "02122" "02221" "0223" "0221" "02221" "0321" "0223" "02122" "02122" "0223"
#> [2031] "02221" "02122" "0223" "0232" "0221" "02113" "0221" "02112" "0223" "0223"
#> [2041] "0221" "0321" "02112" "0233" "0232" "02113" "02122" "02121" "02121" "0142"
#> [2051] "0221" "02113" "0231" "02113" "02112" "02121" "0223" "02122" "0321" "0223"
#> [2061] "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122" "02122" "02112"
#> [2071] "02112" "0223" "0232" "02221" "02113" "0233" "02112" "02221" "02112" "02112"
#> [2081] "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231"
#> [2101] "02121" "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112"
#> [2111] "0231" "0223" "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232"
#> [2121] "02121" "02121" "0233" "0232" "0142" "0223" "02121" "0142" "02112" "02112"
#> [2131] "02122" "02121" "02112" "02112" "02112" "02121" "02122" "02121" "0221" "02121"
#> [2141] "02221" "0223" "02122" "0221" "0221" "02221" "0223" "02121" "0223" "02121"
#> [2151] "02112" "02122" "0223" "0223" "02122" "02112" "02121" "0223" "0223" "02112"
#> [2161] "0221" "0223" "0221" "02122" "0223" "0223" "0221" "02121" "0223" "0223"
#> [2171] "0223" "0221" "02121" "0321" "0221" "0221" "0221" "021112" "02122" "02122"
#> [2181] "02122" "0223" "0234" "02222" "0223" "0221" "0221" "0221" "0221" "0143"
#> [2191] "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "02221" "02121" "02121" "02121" "0231"
#> [2211] "0232" "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121"
#> [2221] "0223" "02123" "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121"
#> [2231] "0223" "0223" "0223" "0223" "02121" "0221" "0321" "02221" "0221" "0321"
#> [2241] "0221" "0321" "0223" "0221" "0223" "0223" "0223" "0231" "0231" "0221"
#> [2251] "02221" "0321" "02221" "0221" "0231" "0231" "0221" "0221" "0141" "0321"
#> [2261] "02112" "0221" "0221" "0221" "0223" "0321" "0231" "0221" "0321" "0223"
#> [2271] "0223" "0223" "0142" "0223" "0142" "02221" "0223" "0321" "0221" "0231"
#> [2281] "02221" "0221" "0141" "02221" "0221" "0221" "0142" "0321" "0321" "0221"
#> [2291] "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141" "0321"
#> [2301] "0142" "0142" "0141" "0223" "0142" "02221" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221"
#> [2321] "0142" "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221"
#> [2331] "0223" "0221" "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141"
#> [2341] "0321" "0321" "0221" "02122" "0232" "0223" "0223" "0223" "0221" "0221"
#> [2351] "0321" "02221" "0223" "0223" "0221" "0221" "0321" "02121" "02112" "0221"
#> [2361] "02121" "0221" "02121" "0234" "02121" "02122" "0221" "02112" "02112" "0221"
#> [2371] "0223" "0223" "02121" "0223" "02121" "0223" "0221" "0221" "02123" "02121"
#> [2381] "0232" "0223" "02112" "02122" "0232" "0221" "0223" "0223" "0223" "0231"
#> [2391] "02113" "0223" "0221" "0221" "021112" "02121" "02122" "0223" "0321" "0221"
#> [2401] "0141" "0141" "0141" "02122" "0221" "0231" "021112" "0223" "02122" "02221"
#> [2411] "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223"
#> [2431] "0221" "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141"
#> [2441] "0141" "01234" "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223"
#> [2451] "02122" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "02122" "0231"
#> [2461] "02122" "02122" "02122" "02123" "02122" "02123" "02122" "02122" "02221" "0221"
#> [2471] "0321" "0221" "0221" "0221" "02122" "02122" "0223" "02122" "0223" "0221"
#> [2481] "0223" "02122" "0223" "0223" "02112" "0223" "02122" "02122" "02122" "02112"
#> [2491] "02123" "02122" "02122" "02112" "0223" "02122" "0223" "02122" "02122" "0221"
#> [2501] "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321" "0321" "0221"
#> [2511] "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221" "02122"
#> [2521] "02121" "02112" "0221" "02221" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231"
#> [2541] "0221" "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221"
#> [2551] "0321" "0321" "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121"
#> [2561] "0321" "02122" "0321" "0223" "0221" "0321" "0221" "0221" "0221" "0221"
#> [2571] "0223" "0142" "0141" "0141" "0321" "0321" "0221" "0221" "02112" "02122"
#> [2581] "02122" "0223" "0223" "0221" "0221" "02221" "0221" "0142" "021112" "0232"
#> [2591] "0234" "0232" "02113" "02113" "021111" "02113" "02113" "021111" "0231" "02113"
#> [2601] "021111" "021111" "0232" "02113" "0232" "0231" "0234" "0232" "0323" "0142"
#> [2611] "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231" "0231" "0234"
#> [2621] "0233" "0232" "0142" "02112" "02222" "0231" "0142" "0142" "0141" "0231"
#> [2631] "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221"
#> [2651] "01221" "0221" "0221" "0221" "0221" "0221" "0231" "0221" "02221" "0221"
#> [2661] "0221" "0221" "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321"
#> [2671] "0221" "0141" "0321" "0221" "0321" "0221" "0221" "0324" "01231" "0141"
#> [2681] "01231" "0221" "0141" "01231" "01212" "0232" "0232" "02112" "02112" "0321"
#> [2691] "02121" "02121" "0234" "0231" "0143" "0221" "0324" "02121" "0221" "0321"
#> [2701] "0221" "02121" "0141" "02221" "02221" "0321" "0142" "02221" "0141" "0142"
#> [2711] "02221" "0141" "0141" "0231" "02221" "0231" "0141" "0142" "0231" "0141"
#> [2721] "0223" "02221" "0141" "02112" "0321" "0141" "0321" "0141" "01231" "0321"
#> [2731] "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141" "0321"
#> [2741] "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "021111" "021111" "021111"
#> [2761] "021111" "021111" "021111" "021111" "0232" "0142" "0142" "0221" "021111" "02113"
#> [2771] "021112" "021112" "021111" "021111" "021111" "021112" "021111" "021111" "021112" "021112"
#> [2781] "021112" "0231" "021111" "021111" "021111" "0232" "021111" "0232" "021111" "0142"
#> [2791] "0142" "0223" "0231" "0231" "021112" "021112" "021112" "021112" "021111" "021111"
#> [2801] "021111" "02113" "0233" "021112" "02113" "021111" "021112" "0232" "021111" "021111"
#> [2811] "021111" "021111" "021112" "021112" "021111" "021112" "0221" "0142" "0142" "0142"
#> [2821] "0221" "02121" "0231" "021112" "021112" "02121" "021112" "02121" "021112" "0223"
#> [2831] "02121" "02121" "021112" "02121" "0231" "0223" "02121" "02121" "0232" "0231"
#> [2841] "021112" "021112" "02112" "02112" "02121" "021111" "02112" "021112" "021112" "02112"
#> [2851] "021112" "021111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221"
#> [2871] "021112" "02121" "02123" "021111" "021112" "02121" "0223" "02121" "0142" "02121"
#> [2881] "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 577))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232"
#> [11] "01232" "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131"
#> [21] "0322" "01232" "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121"
#> [31] "0322" "01232" "0322" "0322" "0322" "01232" "01232" "01232" "03121" "0311"
#> [41] "01131" "02222" "01131" "0311" "01232" "01232" "0311" "03121" "0322" "01131"
#> [51] "03121" "01131" "01131" "01232" "01232" "02123" "02123" "0143" "01133" "0313"
#> [61] "0322" "01131" "01131" "02221" "0311" "01132" "01232" "01232" "0322" "01232"
#> [71] "0322" "0311" "0322" "01131" "01131" "0143" "0111" "0112" "02221" "01131"
#> [81] "0143" "0322" "01131" "0143" "01133" "02221" "01131" "01131" "01131" "01231"
#> [91] "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143" "0313"
#> [101] "01131" "01131" "0111" "01133" "0111" "0322" "02221" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "02221" "02221" "0322" "01132" "0321"
#> [121] "0313" "0322" "02222" "02221" "02222" "0234" "01231" "0111" "01133" "01133"
#> [131] "01231" "01131" "01133" "0324" "0111" "02222" "01131" "01131" "0322" "0111"
#> [141] "01131" "0111" "01232" "01231" "01231" "02222" "01131" "02123" "01131" "0324"
#> [151] "0313" "0313" "01131" "0313" "0322" "01131" "0313" "0234" "0322" "0322"
#> [161] "0322" "01131" "0313" "0313" "02222" "01131" "0322" "0313" "01131" "01131"
#> [171] "0322" "0313" "0313" "02222" "02222" "0313" "0313" "01131" "0313" "0313"
#> [181] "03121" "0313" "0322" "0313" "0322" "0313" "0313" "0313" "03121" "02222"
#> [191] "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121" "03121" "03122"
#> [201] "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322" "02222"
#> [211] "0313" "0234" "0313" "03121" "0313" "0313" "0322" "02222" "03121" "01133"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "01131" "02113"
#> [231] "0313" "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313"
#> [241] "0313" "0313" "03121" "01133" "03121" "03121" "03121" "02222" "03121" "0313"
#> [251] "01133" "0313" "03121" "03121" "0313" "0313" "01133" "03121" "0313" "0313"
#> [261] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "01133" "0313"
#> [271] "01133" "01133" "0313" "0313" "01133" "0313" "0313" "0313" "0313" "0322"
#> [281] "02123" "01133" "0313" "0313" "0313" "02222" "0313" "0313" "03121" "03121"
#> [291] "03121" "03122" "03121" "03121" "03121" "03121" "03121" "03122" "02113" "03121"
#> [301] "02113" "0313" "0313" "0234" "0313" "02113" "02222" "03122" "02222" "03121"
#> [311] "03121" "0313" "02222" "0313" "0313" "03121" "01133" "0313" "0313" "01133"
#> [321] "0313" "01133" "03121" "0313" "0311" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "02222" "02222" "01132" "0313" "0112"
#> [341] "0313" "0313" "02222" "0313" "0313" "0313" "02222" "0311" "0311" "02222"
#> [351] "0313" "01133" "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122"
#> [361] "03121" "0313" "0313" "0313" "0313" "0313" "0313" "03121" "03121" "03121"
#> [371] "0313" "03122" "03122" "03121" "03121" "03121" "03121" "03122" "03122" "0313"
#> [381] "0111" "0112" "02222" "0311" "0112" "0111" "0112" "0311" "0324" "0311"
#> [391] "0112" "0311" "0112" "0311" "02222" "0311" "0313" "0311" "0112" "0111"
#> [401] "0311" "0112" "0143" "0311" "0112" "02222" "0111" "0311" "0112" "0112"
#> [411] "0311" "0311" "02123" "0112" "0112" "0112" "0111" "01133" "0311" "0111"
#> [421] "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112" "0112"
#> [431] "0112" "0234" "0112" "0234" "0111" "02221" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "03122" "03122" "03121"
#> [451] "0311" "0112" "0311" "0112" "0112" "03121" "0112" "0112" "03122" "03122"
#> [461] "0311" "03122" "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122"
#> [471] "03122" "0311" "03122" "03122" "03122" "0311" "0311" "03121" "0311" "0311"
#> [481] "0311" "0311" "0311" "0112" "02123" "03122" "0311" "0311" "02222" "02222"
#> [491] "02123" "03121" "03122" "02222" "03122" "0112" "02123" "02113" "0112" "03122"
#> [501] "02113" "0112" "0311" "03122" "0311" "02113" "0112" "0311" "0311" "0311"
#> [511] "0311" "02222" "0311" "0311" "0112" "0112" "02222" "0311" "03121" "0311"
#> [521] "0112" "0112" "0112" "03122" "03121" "0313" "03121" "0112" "0112" "02221"
#> [531] "02123" "02123" "0112" "02222" "0111" "0111" "0111" "02123" "0111" "0311"
#> [541] "0112" "02222" "0111" "0112" "02222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143"
#> [561] "0311" "01132" "0324" "0324" "01132" "0112" "0111" "02221" "0311" "0112"
#> [571] "0112" "02221" "0324" "0311" "0112" "03121" "0111" "0112" "0112" "02221"
#> [581] "0112" "0112" "0111" "0112" "0311" "0112" "0311" "0112" "0111" "01132"
#> [591] "0111" "0313" "0112" "03122" "0313" "0324" "0112" "0313" "0313" "0111"
#> [601] "0111" "01132" "0111" "0313" "0111" "0112" "02222" "0111" "0111" "0111"
#> [611] "0111" "0111" "0112" "0111" "0111" "0234" "0311" "0311" "0112" "0311"
#> [621] "0311" "0313" "0112" "0311" "0112" "0311" "0311" "0311" "0311" "0311"
#> [631] "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112" "0112" "0112"
#> [641] "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "02222" "0112"
#> [651] "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "03122" "0311" "0311" "0311" "0311"
#> [671] "02222" "0112" "02222" "0311" "0313" "0234" "0311" "0311" "02222" "0112"
#> [681] "0311" "0311" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311"
#> [691] "0311" "0112" "03122" "0311" "02222" "0311" "03122" "0112" "03122" "0143"
#> [701] "03122" "0112" "0111" "0311" "0311" "02222" "02222" "0112" "0324" "0112"
#> [711] "0324" "02123" "0111" "0112" "0111" "0112" "0111" "0111" "02221" "0311"
#> [721] "0311" "02221" "0234" "0112" "02221" "0311" "0311" "0311" "0112" "0112"
#> [731] "0311" "0112" "0111" "0311" "0112" "0112" "0111" "0111" "0111" "0311"
#> [741] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03122" "03122" "03122"
#> [751] "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324" "0311"
#> [761] "02123" "02222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "03122" "0112" "0112" "0311" "0112" "02222" "0111" "0112" "02113" "0112"
#> [781] "0311" "0112" "0112" "0111" "0112" "0112" "03122" "0111" "0311" "0311"
#> [791] "0112" "0112" "03122" "02222" "0112" "03122" "0111" "0111" "0234" "0311"
#> [801] "03122" "02222" "0311" "0311" "0311" "0234" "0311" "0112" "0311" "0112"
#> [811] "0112" "0112" "0324" "0324" "01231" "0143" "0111" "0112" "0111" "02123"
#> [821] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [831] "0111" "0111" "0111" "01231" "0111" "0111" "0111" "0111" "0111" "0111"
#> [841] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0324" "01231"
#> [851] "01132" "0234" "0324" "02222" "0111" "0143" "0143" "0143" "0324" "0111"
#> [861] "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0311"
#> [871] "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324"
#> [891] "0111" "0311" "0111" "0111" "0111" "02221" "02221" "0111" "0111" "0111"
#> [901] "0111" "0111" "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111"
#> [911] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [921] "0111" "0111" "0111" "0112" "0111" "0111" "0111" "0111" "02221" "0111"
#> [931] "0143" "0111" "0111" "0111" "02221" "0324" "0111" "02221" "0111" "0111"
#> [941] "0111" "0143" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [951] "0111" "0111" "0111" "0311" "01132" "02123" "0143" "0143" "0111" "02123"
#> [961] "02221" "0112" "0111" "02123" "0311" "0112" "0111" "0111" "0111" "0311"
#> [971] "0111" "0111" "02221" "0112" "01231" "0111" "0111" "0111" "0111" "0111"
#> [981] "0324" "0324" "02222" "02221" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111"
#> [1001] "02221" "0132" "01211" "01212" "01212" "01212" "01212" "01212" "01212" "01212"
#> [1011] "01212" "01212" "01212" "02221" "01212" "02221" "01212" "01212" "01212" "01212"
#> [1021] "01212" "01212" "01212" "01212" "01212" "0323" "0142" "01212" "01212" "01212"
#> [1031] "01212" "01212" "01212" "01212" "01212" "0311" "01212" "03121" "01212" "02113"
#> [1041] "01212" "01132" "0322" "01212" "01212" "01212" "03121" "01211" "01211" "01212"
#> [1051] "01132" "01212" "01212" "01211" "01211" "01211" "01212" "01212" "01132" "01211"
#> [1061] "01211" "01212" "01212" "01211" "01211" "01212" "01212" "01212" "01212" "01211"
#> [1071] "01211" "01212" "0132" "01212" "0322" "01211" "01211" "01211" "0132" "01211"
#> [1081] "01212" "01211" "03121" "01211" "01211" "01211" "01211" "0311" "01211" "01212"
#> [1091] "01211" "01212" "01132" "0132" "01211" "01211" "01211" "01212" "01211" "01212"
#> [1101] "0142" "01212" "01212" "01212" "01212" "01212" "01211" "01211" "01212" "0311"
#> [1111] "01212" "01212" "01212" "01212" "01212" "0132" "01212" "0313" "01212" "0141"
#> [1121] "01212" "0313" "01212" "01212" "01211" "01212" "01211" "03122" "01212" "01211"
#> [1131] "01211" "01132" "01132" "01211" "0322" "01211" "01212" "01211" "01211" "01211"
#> [1141] "0221" "01211" "01211" "01132" "0322" "01232" "01211" "0111" "01211" "0142"
#> [1151] "01211" "01211" "01231" "01232" "01211" "01211" "01211" "01211" "01211" "01211"
#> [1161] "01132" "01232" "01132" "01211" "01211" "01211" "01211" "0111" "01132" "0111"
#> [1171] "01231" "0311" "01132" "01211" "01132" "01132" "01211" "01211" "01211" "01211"
#> [1181] "0223" "01211" "0141" "01211" "0221" "0111" "01211" "01211" "01132" "01211"
#> [1191] "01211" "01211" "01211" "01211" "01211" "0141" "01211" "01231" "0131" "01211"
#> [1201] "01211" "0141" "01211" "01211" "01211" "01211" "01211" "0112" "01211" "01211"
#> [1211] "0131" "01211" "01232" "0141" "02221" "01211" "0321" "0313" "01211" "01211"
#> [1221] "01131" "01211" "01211" "0221" "01211" "0223" "01232" "01211" "01211" "0141"
#> [1231] "01211" "01211" "01211" "01132" "01211" "01131" "0112" "0313" "0141" "01211"
#> [1241] "0333" "0321" "0311" "01211" "01211" "01211" "01132" "01132" "01211" "01211"
#> [1251] "01132" "01234" "0112" "0111" "0112" "0221" "0221" "0221" "0223" "01223"
#> [1261] "01212" "01223" "01211" "01234" "0311" "0141" "0111" "01132" "0132" "0132"
#> [1271] "0132" "0131" "0331" "0131" "0131" "0132" "0333" "02122" "0332" "0332"
#> [1281] "0331" "0132" "0332" "0132" "0132" "0132" "0132" "0132" "0332" "0332"
#> [1291] "0132" "0331" "0132" "0132" "0233" "0333" "0132" "0132" "0132" "0131"
#> [1301] "0132" "0131" "0131" "0132" "0131" "0131" "0131" "0132" "0132" "0132"
#> [1311] "0132" "0132" "0223" "0331" "0221" "0131" "0333" "02122" "0132" "0131"
#> [1321] "0131" "0132" "0132" "0132" "0131" "0132" "0333" "01223" "0131" "0131"
#> [1331] "0132" "02113" "0132" "0331" "0132" "0333" "0132" "0132" "0132" "0131"
#> [1341] "0132" "0132" "02221" "0132" "0131" "0223" "0233" "02113" "0131" "0132"
#> [1351] "0131" "0131" "02113" "0223" "0132" "0131" "0131" "0132" "0131" "0131"
#> [1361] "02122" "02122" "0131" "0132" "0331" "0331" "0131" "0331" "0132" "0331"
#> [1371] "0331" "0331" "0332" "0132" "0331" "0132" "0132" "0131" "0131" "0132"
#> [1381] "0131" "0132" "0132" "0132" "0132" "0132" "0132" "01234" "01223" "01231"
#> [1391] "01234" "0321" "0131" "0131" "0231" "0141" "02113" "0233" "0233" "01231"
#> [1401] "0233" "0132" "0132" "0131" "0131" "0333" "0233" "0131" "0131" "0311"
#> [1411] "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0333" "0131"
#> [1421] "0131" "0311" "0131" "0131" "0131" "0132" "0131" "0131" "0333" "0311"
#> [1431] "0131" "0112" "0131" "0311" "01231" "0131" "0131" "0131" "0131" "0131"
#> [1441] "0131" "0131" "0131" "0131" "0131" "01231" "0131" "0131" "0233" "0131"
#> [1451] "0333" "0221" "0132" "0131" "0221" "0131" "0131" "0131" "0223" "0131"
#> [1461] "0131" "0131" "0132" "0132" "01231" "0131" "0111" "0111" "01131" "0132"
#> [1471] "0131" "0333" "01231" "0313" "0333" "0313" "0112" "02121" "0131" "0221"
#> [1481] "01232" "0131" "0132" "0111" "0131" "0131" "0131" "0321" "0141" "0131"
#> [1491] "0141" "0131" "0131" "0111" "0231" "0141" "0131" "0111" "0131" "0233"
#> [1501] "01231" "0141" "0131" "0111" "01231" "0321" "0132" "02222" "0131" "0223"
#> [1511] "01231" "0131" "01231" "0132" "0131" "01231" "0131" "0221" "0331" "0221"
#> [1521] "0233" "0233" "0142" "0221" "0142" "0132" "0333" "0132" "0132" "0131"
#> [1531] "0142" "0131" "0132" "02113" "01223" "0223" "0112" "0111" "0132" "0131"
#> [1541] "01232" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131"
#> [1551] "0311" "0131" "0111" "0131" "0131" "0131" "0142" "02121" "0233" "0131"
#> [1561] "01231" "01231" "0143" "03121" "0223" "01133" "0132" "0333" "0131" "01231"
#> [1571] "0131" "0223" "02121" "0142" "02121" "0332" "0332" "02113" "0233" "0233"
#> [1581] "0332" "02113" "0332" "0233" "0332" "0332" "0331" "0332" "0331" "0332"
#> [1591] "0132" "0331" "0332" "02221" "0331" "02113" "02121" "0233" "0132" "02113"
#> [1601] "0132" "0332" "0132" "02123" "02113" "0132" "0132" "0233" "02113" "02113"
#> [1611] "0331" "0331" "0332" "0331" "0331" "0331" "0331" "02113" "0132" "02221"
#> [1621] "02113" "0233" "0132" "0331" "01132" "02122" "01234" "0132" "01234" "0132"
#> [1631] "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221" "01234"
#> [1641] "01234" "0132" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234"
#> [1661] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234"
#> [1671] "01234" "01231" "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233"
#> [1681] "01233" "01233" "01221" "0141" "01233" "01212" "01132" "01211" "01232" "01223"
#> [1691] "01233" "01233" "0322" "01212" "01233" "01233" "01233" "0112" "01233" "01233"
#> [1701] "0112" "01233" "0311" "01233" "01233" "01221" "0323" "01223" "01233" "01233"
#> [1711] "0131" "0311" "01233" "01233" "01223" "0132" "01233" "01233" "0323" "0323"
#> [1721] "0131" "01233" "0141" "01233" "01233" "01233" "01233" "0313" "01233" "0311"
#> [1731] "0311" "01233" "01211" "02121" "01231" "01133" "01223" "01133" "0112" "0111"
#> [1741] "01221" "01223" "0132" "01221" "0131" "01221" "01222" "01223" "0323" "01222"
#> [1751] "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "03122"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "0131" "0221"
#> [1771] "01221" "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221"
#> [1781] "0322" "01132" "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323"
#> [1791] "01222" "0313" "0313" "0323" "0223" "01132" "01221" "0313" "0223" "01221"
#> [1801] "01221" "01221" "01222" "0323" "01221" "01221" "0233" "02121" "0223" "0311"
#> [1811] "0221" "01221" "01221" "01131" "01223" "01221" "01221" "01221" "01221" "01221"
#> [1821] "0313" "01221" "01221" "01221" "01221" "01221" "01221" "01221" "01222" "0223"
#> [1831] "01221" "01221" "0323" "01221" "01222" "02122" "0223" "01221" "0111" "01221"
#> [1841] "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221" "01222" "01221"
#> [1851] "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322" "01233"
#> [1861] "03122" "01233" "01233" "0332" "0223" "03122" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "01211"
#> [1881] "02112" "01223" "01223" "01223" "01223" "0323" "01222" "03122" "01222" "01222"
#> [1891] "01132" "0221" "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121"
#> [1901] "0132" "0323" "01223" "0311" "01223" "01223" "0332" "01223" "01222" "01222"
#> [1911] "01222" "01222" "01222" "01221" "0323" "01222" "01221" "01132" "01221" "03122"
#> [1921] "0223" "01222" "0323" "0323" "01222" "0311" "01222" "01222" "01222" "0233"
#> [1931] "0323" "01222" "02112" "01222" "0323" "0233" "0333" "01222" "01222" "0323"
#> [1941] "0323" "01222" "0323" "01222" "0332" "02221" "03122" "0323" "01222" "03121"
#> [1951] "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323" "02113" "0323"
#> [1961] "0221" "0323" "0323" "02221" "01222" "0323" "02112" "0331" "0323" "03122"
#> [1971] "01222" "0233" "03122" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112"
#> [1991] "0221" "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231"
#> [2001] "0223" "02113" "02112" "0232" "02112" "02221" "0221" "02121" "0232" "0232"
#> [2011] "02123" "0231" "02121" "0231" "0142" "0221" "0231" "0321" "0223" "02112"
#> [2021] "02122" "02221" "0223" "0221" "02221" "0321" "0223" "02122" "02122" "0223"
#> [2031] "02221" "02122" "0223" "0232" "0221" "02113" "0221" "02112" "0223" "0223"
#> [2041] "0221" "0321" "02112" "0233" "0232" "02113" "02122" "02121" "02121" "0142"
#> [2051] "0221" "02113" "0231" "02113" "02112" "02121" "0223" "02122" "0321" "0223"
#> [2061] "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122" "02122" "02112"
#> [2071] "02112" "0223" "0232" "02221" "02113" "0233" "02112" "02221" "02112" "02112"
#> [2081] "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231"
#> [2101] "02121" "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112"
#> [2111] "0231" "0223" "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232"
#> [2121] "02121" "02121" "0233" "0232" "0142" "0223" "02121" "0142" "02112" "02112"
#> [2131] "02122" "02121" "02112" "02112" "02112" "02121" "02122" "02121" "0221" "02121"
#> [2141] "02221" "0223" "02122" "0221" "0221" "02221" "0223" "02121" "0223" "02121"
#> [2151] "02112" "02122" "0223" "0223" "02122" "02112" "02121" "0223" "0223" "02112"
#> [2161] "0221" "0223" "0221" "02122" "0223" "0223" "0221" "02121" "0223" "0223"
#> [2171] "0223" "0221" "02121" "0321" "0221" "0221" "0221" "021112" "02122" "02122"
#> [2181] "02122" "0223" "0234" "02222" "0223" "0221" "0221" "0221" "0221" "0143"
#> [2191] "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "02221" "02121" "02121" "02121" "0231"
#> [2211] "0232" "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121"
#> [2221] "0223" "02123" "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121"
#> [2231] "0223" "0223" "0223" "0223" "02121" "0221" "0321" "02221" "0221" "0321"
#> [2241] "0221" "0321" "0223" "0221" "0223" "0223" "0223" "0231" "0231" "0221"
#> [2251] "02221" "0321" "02221" "0221" "0231" "0231" "0221" "0221" "0141" "0321"
#> [2261] "02112" "0221" "0221" "0221" "0223" "0321" "0231" "0221" "0321" "0223"
#> [2271] "0223" "0223" "0142" "0223" "0142" "02221" "0223" "0321" "0221" "0231"
#> [2281] "02221" "0221" "0141" "02221" "0221" "0221" "0142" "0321" "0321" "0221"
#> [2291] "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141" "0321"
#> [2301] "0142" "0142" "0141" "0223" "0142" "02221" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221"
#> [2321] "0142" "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221"
#> [2331] "0223" "0221" "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141"
#> [2341] "0321" "0321" "0221" "02122" "0232" "0223" "0223" "0223" "0221" "0221"
#> [2351] "0321" "02221" "0223" "0223" "0221" "0221" "0321" "02121" "02112" "0221"
#> [2361] "02121" "0221" "02121" "0234" "02121" "02122" "0221" "02112" "02112" "0221"
#> [2371] "0223" "0223" "02121" "0223" "02121" "0223" "0221" "0221" "02123" "02121"
#> [2381] "0232" "0223" "02112" "02122" "0232" "0221" "0223" "0223" "0223" "0231"
#> [2391] "02113" "0223" "0221" "0221" "021112" "02121" "02122" "0223" "0321" "0221"
#> [2401] "0141" "0141" "0141" "02122" "0221" "0231" "021112" "0223" "02122" "02221"
#> [2411] "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223"
#> [2431] "0221" "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141"
#> [2441] "0141" "01234" "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223"
#> [2451] "02122" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "02122" "0231"
#> [2461] "02122" "02122" "02122" "02123" "02122" "02123" "02122" "02122" "02221" "0221"
#> [2471] "0321" "0221" "0221" "0221" "02122" "02122" "0223" "02122" "0223" "0221"
#> [2481] "0223" "02122" "0223" "0223" "02112" "0223" "02122" "02122" "02122" "02112"
#> [2491] "02123" "02122" "02122" "02112" "0223" "02122" "0223" "02122" "02122" "0221"
#> [2501] "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321" "0321" "0221"
#> [2511] "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221" "02122"
#> [2521] "02121" "02112" "0221" "02221" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231"
#> [2541] "0221" "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221"
#> [2551] "0321" "0321" "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121"
#> [2561] "0321" "02122" "0321" "0223" "0221" "0321" "0221" "0221" "0221" "0221"
#> [2571] "0223" "0142" "0141" "0141" "0321" "0321" "0221" "0221" "02112" "02122"
#> [2581] "02122" "0223" "0223" "0221" "0221" "02221" "0221" "0142" "021112" "0232"
#> [2591] "0234" "0232" "02113" "02113" "021111" "02113" "02113" "021111" "0231" "02113"
#> [2601] "021111" "021111" "0232" "02113" "0232" "0231" "0234" "0232" "0323" "0142"
#> [2611] "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231" "0231" "0234"
#> [2621] "0233" "0232" "0142" "02112" "02222" "0231" "0142" "0142" "0141" "0231"
#> [2631] "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221"
#> [2651] "01221" "0221" "0221" "0221" "0221" "0221" "0231" "0221" "02221" "0221"
#> [2661] "0221" "0221" "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321"
#> [2671] "0221" "0141" "0321" "0221" "0321" "0221" "0221" "0324" "01231" "0141"
#> [2681] "01231" "0221" "0141" "01231" "01212" "0232" "0232" "02112" "02112" "0321"
#> [2691] "02121" "02121" "0234" "0231" "0143" "0221" "0324" "02121" "0221" "0321"
#> [2701] "0221" "02121" "0141" "02221" "02221" "0321" "0142" "02221" "0141" "0142"
#> [2711] "02221" "0141" "0141" "0231" "02221" "0231" "0141" "0142" "0231" "0141"
#> [2721] "0223" "02221" "0141" "02112" "0321" "0141" "0321" "0141" "01231" "0321"
#> [2731] "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141" "0321"
#> [2741] "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "021111" "021111" "021111"
#> [2761] "021111" "021111" "021111" "021111" "0232" "0142" "0142" "0221" "021111" "02113"
#> [2771] "021112" "021112" "021111" "021111" "021111" "021112" "021111" "021111" "021112" "021112"
#> [2781] "021112" "0231" "021111" "021111" "021111" "0232" "021111" "0232" "021111" "0142"
#> [2791] "0142" "0223" "0231" "0231" "021112" "021112" "021112" "021112" "021111" "021111"
#> [2801] "021111" "02113" "0233" "021112" "02113" "021111" "021112" "0232" "021111" "021111"
#> [2811] "021111" "021111" "021112" "021112" "021111" "021112" "0221" "0142" "0142" "0142"
#> [2821] "0221" "02121" "0231" "021112" "021112" "02121" "021112" "02121" "021112" "0223"
#> [2831] "02121" "02121" "021112" "02121" "0231" "0223" "02121" "02121" "0232" "0231"
#> [2841] "021112" "021112" "02112" "02112" "02121" "021111" "02112" "021112" "021112" "02112"
#> [2851] "021112" "021111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221"
#> [2871] "021112" "02121" "02123" "021111" "021112" "02121" "0223" "02121" "0142" "02121"
#> [2881] "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 628))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232"
#> [11] "01232" "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131"
#> [21] "0322" "01232" "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121"
#> [31] "0322" "01232" "0322" "0322" "0322" "01232" "01232" "01232" "03121" "0311"
#> [41] "01131" "02222" "01131" "0311" "01232" "01232" "0311" "03121" "0322" "01131"
#> [51] "03121" "01131" "01131" "01232" "01232" "02123" "02123" "0143" "01133" "0313"
#> [61] "0322" "01131" "01131" "02221" "0311" "01132" "01232" "01232" "0322" "01232"
#> [71] "0322" "0311" "0322" "01131" "01131" "0143" "0111" "0112" "02221" "01131"
#> [81] "0143" "0322" "01131" "0143" "01133" "02221" "01131" "01131" "01131" "01231"
#> [91] "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143" "0313"
#> [101] "01131" "01131" "0111" "01133" "0111" "0322" "02221" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "02221" "02221" "0322" "01132" "0321"
#> [121] "0313" "0322" "02222" "02221" "02222" "0234" "01231" "0111" "01133" "01133"
#> [131] "01231" "01131" "01133" "0324" "0111" "02222" "01131" "01131" "0322" "0111"
#> [141] "01131" "0111" "01232" "01231" "01231" "02222" "01131" "02123" "01131" "0324"
#> [151] "0313" "0313" "01131" "0313" "0322" "01131" "0313" "0234" "0322" "0322"
#> [161] "0322" "01131" "0313" "0313" "02222" "01131" "0322" "0313" "01131" "01131"
#> [171] "0322" "0313" "0313" "02222" "02222" "0313" "0313" "01131" "0313" "0313"
#> [181] "03121" "0313" "0322" "0313" "0322" "0313" "0313" "0313" "03121" "02222"
#> [191] "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121" "03121" "03122"
#> [201] "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322" "02222"
#> [211] "0313" "0234" "0313" "03121" "0313" "0313" "0322" "02222" "03121" "01133"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "01131" "02113"
#> [231] "0313" "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313"
#> [241] "0313" "0313" "03121" "01133" "03121" "03121" "03121" "02222" "03121" "0313"
#> [251] "01133" "0313" "03121" "03121" "0313" "0313" "01133" "03121" "0313" "0313"
#> [261] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "01133" "0313"
#> [271] "01133" "01133" "0313" "0313" "01133" "0313" "0313" "0313" "0313" "0322"
#> [281] "02123" "01133" "0313" "0313" "0313" "02222" "0313" "0313" "03121" "03121"
#> [291] "03121" "03122" "03121" "03121" "03121" "03121" "03121" "03122" "02113" "03121"
#> [301] "02113" "0313" "0313" "0234" "0313" "02113" "02222" "03122" "02222" "03121"
#> [311] "03121" "0313" "02222" "0313" "0313" "03121" "01133" "0313" "0313" "01133"
#> [321] "0313" "01133" "03121" "0313" "0311" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "02222" "02222" "01132" "0313" "0112"
#> [341] "0313" "0313" "02222" "0313" "0313" "0313" "02222" "0311" "0311" "02222"
#> [351] "0313" "01133" "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122"
#> [361] "03121" "0313" "0313" "0313" "0313" "0313" "0313" "03121" "03121" "03121"
#> [371] "0313" "03122" "03122" "03121" "03121" "03121" "03121" "03122" "03122" "0313"
#> [381] "0111" "0112" "02222" "0311" "0112" "0111" "0112" "0311" "0324" "0311"
#> [391] "0112" "0311" "0112" "0311" "02222" "0311" "0313" "0311" "0112" "0111"
#> [401] "0311" "0112" "0143" "0311" "0112" "02222" "0111" "0311" "0112" "0112"
#> [411] "0311" "0311" "02123" "0112" "0112" "0112" "0111" "01133" "0311" "0111"
#> [421] "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112" "0112"
#> [431] "0112" "0234" "0112" "0234" "0111" "02221" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "03122" "03122" "03121"
#> [451] "0311" "0112" "0311" "0112" "0112" "03121" "0112" "0112" "03122" "03122"
#> [461] "0311" "03122" "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122"
#> [471] "03122" "0311" "03122" "03122" "03122" "0311" "0311" "03121" "0311" "0311"
#> [481] "0311" "0311" "0311" "0112" "02123" "03122" "0311" "0311" "02222" "02222"
#> [491] "02123" "03121" "03122" "02222" "03122" "0112" "02123" "02113" "0112" "03122"
#> [501] "02113" "0112" "0311" "03122" "0311" "02113" "0112" "0311" "0311" "0311"
#> [511] "0311" "02222" "0311" "0311" "0112" "0112" "02222" "0311" "03121" "0311"
#> [521] "0112" "0112" "0112" "03122" "03121" "0313" "03121" "0112" "0112" "02221"
#> [531] "02123" "02123" "0112" "02222" "0111" "0111" "0111" "02123" "0111" "0311"
#> [541] "0112" "02222" "0111" "0112" "02222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143"
#> [561] "0311" "01132" "0324" "0324" "01132" "0112" "0111" "02221" "0311" "0112"
#> [571] "0112" "02221" "0324" "0311" "0112" "03121" "0111" "0112" "0112" "02221"
#> [581] "0112" "0112" "0111" "0112" "0311" "0112" "0311" "0112" "0111" "01132"
#> [591] "0111" "0313" "0112" "03122" "0313" "0324" "0112" "0313" "0313" "0111"
#> [601] "0111" "01132" "0111" "0313" "0111" "0112" "02222" "0111" "0111" "0111"
#> [611] "0111" "0111" "0112" "0111" "0111" "0234" "0311" "0311" "0112" "0311"
#> [621] "0311" "0313" "0112" "0311" "0112" "0311" "0311" "0311" "0311" "0311"
#> [631] "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112" "0112" "0112"
#> [641] "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "02222" "0112"
#> [651] "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "03122" "0311" "0311" "0311" "0311"
#> [671] "02222" "0112" "02222" "0311" "0313" "0234" "0311" "0311" "02222" "0112"
#> [681] "0311" "0311" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311"
#> [691] "0311" "0112" "03122" "0311" "02222" "0311" "03122" "0112" "03122" "0143"
#> [701] "03122" "0112" "0111" "0311" "0311" "02222" "02222" "0112" "0324" "0112"
#> [711] "0324" "02123" "0111" "0112" "0111" "0112" "0111" "0111" "02221" "0311"
#> [721] "0311" "02221" "0234" "0112" "02221" "0311" "0311" "0311" "0112" "0112"
#> [731] "0311" "0112" "0111" "0311" "0112" "0112" "0111" "0111" "0111" "0311"
#> [741] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03122" "03122" "03122"
#> [751] "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324" "0311"
#> [761] "02123" "02222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "03122" "0112" "0112" "0311" "0112" "02222" "0111" "0112" "02113" "0112"
#> [781] "0311" "0112" "0112" "0111" "0112" "0112" "03122" "0111" "0311" "0311"
#> [791] "0112" "0112" "03122" "02222" "0112" "03122" "0111" "0111" "0234" "0311"
#> [801] "03122" "02222" "0311" "0311" "0311" "0234" "0311" "0112" "0311" "0112"
#> [811] "0112" "0112" "0324" "0324" "01231" "0143" "0111" "0112" "0111" "02123"
#> [821] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [831] "0111" "0111" "0111" "01231" "0111" "0111" "0111" "0111" "0111" "0111"
#> [841] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0324" "01231"
#> [851] "01132" "0234" "0324" "02222" "0111" "0143" "0143" "0143" "0324" "0111"
#> [861] "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0311"
#> [871] "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324"
#> [891] "0111" "0311" "0111" "0111" "0111" "02221" "02221" "0111" "0111" "0111"
#> [901] "0111" "0111" "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111"
#> [911] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [921] "0111" "0111" "0111" "0112" "0111" "0111" "0111" "0111" "02221" "0111"
#> [931] "0143" "0111" "0111" "0111" "02221" "0324" "0111" "02221" "0111" "0111"
#> [941] "0111" "0143" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [951] "0111" "0111" "0111" "0311" "01132" "02123" "0143" "0143" "0111" "02123"
#> [961] "02221" "0112" "0111" "02123" "0311" "0112" "0111" "0111" "0111" "0311"
#> [971] "0111" "0111" "02221" "0112" "01231" "0111" "0111" "0111" "0111" "0111"
#> [981] "0324" "0324" "02222" "02221" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111"
#> [1001] "02221" "0132" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1011] "0121" "0121" "0121" "02221" "0121" "02221" "0121" "0121" "0121" "0121"
#> [1021] "0121" "0121" "0121" "0121" "0121" "0323" "0142" "0121" "0121" "0121"
#> [1031] "0121" "0121" "0121" "0121" "0121" "0311" "0121" "03121" "0121" "02113"
#> [1041] "0121" "01132" "0322" "0121" "0121" "0121" "03121" "0121" "0121" "0121"
#> [1051] "01132" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "01132" "0121"
#> [1061] "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1071] "0121" "0121" "0132" "0121" "0322" "0121" "0121" "0121" "0132" "0121"
#> [1081] "0121" "0121" "03121" "0121" "0121" "0121" "0121" "0311" "0121" "0121"
#> [1091] "0121" "0121" "01132" "0132" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1101] "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0311"
#> [1111] "0121" "0121" "0121" "0121" "0121" "0132" "0121" "0313" "0121" "0141"
#> [1121] "0121" "0313" "0121" "0121" "0121" "0121" "0121" "03122" "0121" "0121"
#> [1131] "0121" "01132" "01132" "0121" "0322" "0121" "0121" "0121" "0121" "0121"
#> [1141] "0221" "0121" "0121" "01132" "0322" "01232" "0121" "0111" "0121" "0142"
#> [1151] "0121" "0121" "01231" "01232" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1161] "01132" "01232" "01132" "0121" "0121" "0121" "0121" "0111" "01132" "0111"
#> [1171] "01231" "0311" "01132" "0121" "01132" "01132" "0121" "0121" "0121" "0121"
#> [1181] "0223" "0121" "0141" "0121" "0221" "0111" "0121" "0121" "01132" "0121"
#> [1191] "0121" "0121" "0121" "0121" "0121" "0141" "0121" "01231" "0131" "0121"
#> [1201] "0121" "0141" "0121" "0121" "0121" "0121" "0121" "0112" "0121" "0121"
#> [1211] "0131" "0121" "01232" "0141" "02221" "0121" "0321" "0313" "0121" "0121"
#> [1221] "01131" "0121" "0121" "0221" "0121" "0223" "01232" "0121" "0121" "0141"
#> [1231] "0121" "0121" "0121" "01132" "0121" "01131" "0112" "0313" "0141" "0121"
#> [1241] "0333" "0321" "0311" "0121" "0121" "0121" "01132" "01132" "0121" "0121"
#> [1251] "01132" "01234" "0112" "0111" "0112" "0221" "0221" "0221" "0223" "01223"
#> [1261] "0121" "01223" "0121" "01234" "0311" "0141" "0111" "01132" "0132" "0132"
#> [1271] "0132" "0131" "0331" "0131" "0131" "0132" "0333" "02122" "0332" "0332"
#> [1281] "0331" "0132" "0332" "0132" "0132" "0132" "0132" "0132" "0332" "0332"
#> [1291] "0132" "0331" "0132" "0132" "0233" "0333" "0132" "0132" "0132" "0131"
#> [1301] "0132" "0131" "0131" "0132" "0131" "0131" "0131" "0132" "0132" "0132"
#> [1311] "0132" "0132" "0223" "0331" "0221" "0131" "0333" "02122" "0132" "0131"
#> [1321] "0131" "0132" "0132" "0132" "0131" "0132" "0333" "01223" "0131" "0131"
#> [1331] "0132" "02113" "0132" "0331" "0132" "0333" "0132" "0132" "0132" "0131"
#> [1341] "0132" "0132" "02221" "0132" "0131" "0223" "0233" "02113" "0131" "0132"
#> [1351] "0131" "0131" "02113" "0223" "0132" "0131" "0131" "0132" "0131" "0131"
#> [1361] "02122" "02122" "0131" "0132" "0331" "0331" "0131" "0331" "0132" "0331"
#> [1371] "0331" "0331" "0332" "0132" "0331" "0132" "0132" "0131" "0131" "0132"
#> [1381] "0131" "0132" "0132" "0132" "0132" "0132" "0132" "01234" "01223" "01231"
#> [1391] "01234" "0321" "0131" "0131" "0231" "0141" "02113" "0233" "0233" "01231"
#> [1401] "0233" "0132" "0132" "0131" "0131" "0333" "0233" "0131" "0131" "0311"
#> [1411] "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0333" "0131"
#> [1421] "0131" "0311" "0131" "0131" "0131" "0132" "0131" "0131" "0333" "0311"
#> [1431] "0131" "0112" "0131" "0311" "01231" "0131" "0131" "0131" "0131" "0131"
#> [1441] "0131" "0131" "0131" "0131" "0131" "01231" "0131" "0131" "0233" "0131"
#> [1451] "0333" "0221" "0132" "0131" "0221" "0131" "0131" "0131" "0223" "0131"
#> [1461] "0131" "0131" "0132" "0132" "01231" "0131" "0111" "0111" "01131" "0132"
#> [1471] "0131" "0333" "01231" "0313" "0333" "0313" "0112" "02121" "0131" "0221"
#> [1481] "01232" "0131" "0132" "0111" "0131" "0131" "0131" "0321" "0141" "0131"
#> [1491] "0141" "0131" "0131" "0111" "0231" "0141" "0131" "0111" "0131" "0233"
#> [1501] "01231" "0141" "0131" "0111" "01231" "0321" "0132" "02222" "0131" "0223"
#> [1511] "01231" "0131" "01231" "0132" "0131" "01231" "0131" "0221" "0331" "0221"
#> [1521] "0233" "0233" "0142" "0221" "0142" "0132" "0333" "0132" "0132" "0131"
#> [1531] "0142" "0131" "0132" "02113" "01223" "0223" "0112" "0111" "0132" "0131"
#> [1541] "01232" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131" "0131"
#> [1551] "0311" "0131" "0111" "0131" "0131" "0131" "0142" "02121" "0233" "0131"
#> [1561] "01231" "01231" "0143" "03121" "0223" "01133" "0132" "0333" "0131" "01231"
#> [1571] "0131" "0223" "02121" "0142" "02121" "0332" "0332" "02113" "0233" "0233"
#> [1581] "0332" "02113" "0332" "0233" "0332" "0332" "0331" "0332" "0331" "0332"
#> [1591] "0132" "0331" "0332" "02221" "0331" "02113" "02121" "0233" "0132" "02113"
#> [1601] "0132" "0332" "0132" "02123" "02113" "0132" "0132" "0233" "02113" "02113"
#> [1611] "0331" "0331" "0332" "0331" "0331" "0331" "0331" "02113" "0132" "02221"
#> [1621] "02113" "0233" "0132" "0331" "01132" "02122" "01234" "0132" "01234" "0132"
#> [1631] "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221" "01234"
#> [1641] "01234" "0132" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234"
#> [1661] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234"
#> [1671] "01234" "01231" "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233"
#> [1681] "01233" "01233" "01221" "0141" "01233" "0121" "01132" "0121" "01232" "01223"
#> [1691] "01233" "01233" "0322" "0121" "01233" "01233" "01233" "0112" "01233" "01233"
#> [1701] "0112" "01233" "0311" "01233" "01233" "01221" "0323" "01223" "01233" "01233"
#> [1711] "0131" "0311" "01233" "01233" "01223" "0132" "01233" "01233" "0323" "0323"
#> [1721] "0131" "01233" "0141" "01233" "01233" "01233" "01233" "0313" "01233" "0311"
#> [1731] "0311" "01233" "0121" "02121" "01231" "01133" "01223" "01133" "0112" "0111"
#> [1741] "01221" "01223" "0132" "01221" "0131" "01221" "01222" "01223" "0323" "01222"
#> [1751] "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "03122"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "0131" "0221"
#> [1771] "01221" "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221"
#> [1781] "0322" "01132" "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323"
#> [1791] "01222" "0313" "0313" "0323" "0223" "01132" "01221" "0313" "0223" "01221"
#> [1801] "01221" "01221" "01222" "0323" "01221" "01221" "0233" "02121" "0223" "0311"
#> [1811] "0221" "01221" "01221" "01131" "01223" "01221" "01221" "01221" "01221" "01221"
#> [1821] "0313" "01221" "01221" "01221" "01221" "01221" "01221" "01221" "01222" "0223"
#> [1831] "01221" "01221" "0323" "01221" "01222" "02122" "0223" "01221" "0111" "01221"
#> [1841] "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221" "01222" "01221"
#> [1851] "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322" "01233"
#> [1861] "03122" "01233" "01233" "0332" "0223" "03122" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "0121"
#> [1881] "02112" "01223" "01223" "01223" "01223" "0323" "01222" "03122" "01222" "01222"
#> [1891] "01132" "0221" "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121"
#> [1901] "0132" "0323" "01223" "0311" "01223" "01223" "0332" "01223" "01222" "01222"
#> [1911] "01222" "01222" "01222" "01221" "0323" "01222" "01221" "01132" "01221" "03122"
#> [1921] "0223" "01222" "0323" "0323" "01222" "0311" "01222" "01222" "01222" "0233"
#> [1931] "0323" "01222" "02112" "01222" "0323" "0233" "0333" "01222" "01222" "0323"
#> [1941] "0323" "01222" "0323" "01222" "0332" "02221" "03122" "0323" "01222" "03121"
#> [1951] "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323" "02113" "0323"
#> [1961] "0221" "0323" "0323" "02221" "01222" "0323" "02112" "0331" "0323" "03122"
#> [1971] "01222" "0233" "03122" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112"
#> [1991] "0221" "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231"
#> [2001] "0223" "02113" "02112" "0232" "02112" "02221" "0221" "02121" "0232" "0232"
#> [2011] "02123" "0231" "02121" "0231" "0142" "0221" "0231" "0321" "0223" "02112"
#> [2021] "02122" "02221" "0223" "0221" "02221" "0321" "0223" "02122" "02122" "0223"
#> [2031] "02221" "02122" "0223" "0232" "0221" "02113" "0221" "02112" "0223" "0223"
#> [2041] "0221" "0321" "02112" "0233" "0232" "02113" "02122" "02121" "02121" "0142"
#> [2051] "0221" "02113" "0231" "02113" "02112" "02121" "0223" "02122" "0321" "0223"
#> [2061] "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122" "02122" "02112"
#> [2071] "02112" "0223" "0232" "02221" "02113" "0233" "02112" "02221" "02112" "02112"
#> [2081] "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231"
#> [2101] "02121" "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112"
#> [2111] "0231" "0223" "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232"
#> [2121] "02121" "02121" "0233" "0232" "0142" "0223" "02121" "0142" "02112" "02112"
#> [2131] "02122" "02121" "02112" "02112" "02112" "02121" "02122" "02121" "0221" "02121"
#> [2141] "02221" "0223" "02122" "0221" "0221" "02221" "0223" "02121" "0223" "02121"
#> [2151] "02112" "02122" "0223" "0223" "02122" "02112" "02121" "0223" "0223" "02112"
#> [2161] "0221" "0223" "0221" "02122" "0223" "0223" "0221" "02121" "0223" "0223"
#> [2171] "0223" "0221" "02121" "0321" "0221" "0221" "0221" "021112" "02122" "02122"
#> [2181] "02122" "0223" "0234" "02222" "0223" "0221" "0221" "0221" "0221" "0143"
#> [2191] "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "02221" "02121" "02121" "02121" "0231"
#> [2211] "0232" "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121"
#> [2221] "0223" "02123" "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121"
#> [2231] "0223" "0223" "0223" "0223" "02121" "0221" "0321" "02221" "0221" "0321"
#> [2241] "0221" "0321" "0223" "0221" "0223" "0223" "0223" "0231" "0231" "0221"
#> [2251] "02221" "0321" "02221" "0221" "0231" "0231" "0221" "0221" "0141" "0321"
#> [2261] "02112" "0221" "0221" "0221" "0223" "0321" "0231" "0221" "0321" "0223"
#> [2271] "0223" "0223" "0142" "0223" "0142" "02221" "0223" "0321" "0221" "0231"
#> [2281] "02221" "0221" "0141" "02221" "0221" "0221" "0142" "0321" "0321" "0221"
#> [2291] "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141" "0321"
#> [2301] "0142" "0142" "0141" "0223" "0142" "02221" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221"
#> [2321] "0142" "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221"
#> [2331] "0223" "0221" "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141"
#> [2341] "0321" "0321" "0221" "02122" "0232" "0223" "0223" "0223" "0221" "0221"
#> [2351] "0321" "02221" "0223" "0223" "0221" "0221" "0321" "02121" "02112" "0221"
#> [2361] "02121" "0221" "02121" "0234" "02121" "02122" "0221" "02112" "02112" "0221"
#> [2371] "0223" "0223" "02121" "0223" "02121" "0223" "0221" "0221" "02123" "02121"
#> [2381] "0232" "0223" "02112" "02122" "0232" "0221" "0223" "0223" "0223" "0231"
#> [2391] "02113" "0223" "0221" "0221" "021112" "02121" "02122" "0223" "0321" "0221"
#> [2401] "0141" "0141" "0141" "02122" "0221" "0231" "021112" "0223" "02122" "02221"
#> [2411] "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223"
#> [2431] "0221" "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141"
#> [2441] "0141" "01234" "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223"
#> [2451] "02122" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "02122" "0231"
#> [2461] "02122" "02122" "02122" "02123" "02122" "02123" "02122" "02122" "02221" "0221"
#> [2471] "0321" "0221" "0221" "0221" "02122" "02122" "0223" "02122" "0223" "0221"
#> [2481] "0223" "02122" "0223" "0223" "02112" "0223" "02122" "02122" "02122" "02112"
#> [2491] "02123" "02122" "02122" "02112" "0223" "02122" "0223" "02122" "02122" "0221"
#> [2501] "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321" "0321" "0221"
#> [2511] "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221" "02122"
#> [2521] "02121" "02112" "0221" "02221" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231"
#> [2541] "0221" "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221"
#> [2551] "0321" "0321" "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121"
#> [2561] "0321" "02122" "0321" "0223" "0221" "0321" "0221" "0221" "0221" "0221"
#> [2571] "0223" "0142" "0141" "0141" "0321" "0321" "0221" "0221" "02112" "02122"
#> [2581] "02122" "0223" "0223" "0221" "0221" "02221" "0221" "0142" "021112" "0232"
#> [2591] "0234" "0232" "02113" "02113" "021111" "02113" "02113" "021111" "0231" "02113"
#> [2601] "021111" "021111" "0232" "02113" "0232" "0231" "0234" "0232" "0323" "0142"
#> [2611] "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231" "0231" "0234"
#> [2621] "0233" "0232" "0142" "02112" "02222" "0231" "0142" "0142" "0141" "0231"
#> [2631] "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221"
#> [2651] "01221" "0221" "0221" "0221" "0221" "0221" "0231" "0221" "02221" "0221"
#> [2661] "0221" "0221" "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321"
#> [2671] "0221" "0141" "0321" "0221" "0321" "0221" "0221" "0324" "01231" "0141"
#> [2681] "01231" "0221" "0141" "01231" "0121" "0232" "0232" "02112" "02112" "0321"
#> [2691] "02121" "02121" "0234" "0231" "0143" "0221" "0324" "02121" "0221" "0321"
#> [2701] "0221" "02121" "0141" "02221" "02221" "0321" "0142" "02221" "0141" "0142"
#> [2711] "02221" "0141" "0141" "0231" "02221" "0231" "0141" "0142" "0231" "0141"
#> [2721] "0223" "02221" "0141" "02112" "0321" "0141" "0321" "0141" "01231" "0321"
#> [2731] "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141" "0321"
#> [2741] "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "021111" "021111" "021111"
#> [2761] "021111" "021111" "021111" "021111" "0232" "0142" "0142" "0221" "021111" "02113"
#> [2771] "021112" "021112" "021111" "021111" "021111" "021112" "021111" "021111" "021112" "021112"
#> [2781] "021112" "0231" "021111" "021111" "021111" "0232" "021111" "0232" "021111" "0142"
#> [2791] "0142" "0223" "0231" "0231" "021112" "021112" "021112" "021112" "021111" "021111"
#> [2801] "021111" "02113" "0233" "021112" "02113" "021111" "021112" "0232" "021111" "021111"
#> [2811] "021111" "021111" "021112" "021112" "021111" "021112" "0221" "0142" "0142" "0142"
#> [2821] "0221" "02121" "0231" "021112" "021112" "02121" "021112" "02121" "021112" "0223"
#> [2831] "02121" "02121" "021112" "02121" "0231" "0223" "02121" "02121" "0232" "0231"
#> [2841] "021112" "021112" "02112" "02112" "02121" "021111" "02112" "021112" "021112" "02112"
#> [2851] "021112" "021111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221"
#> [2871] "021112" "02121" "02123" "021111" "021112" "02121" "0223" "02121" "0142" "02121"
#> [2881] "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 634))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232"
#> [11] "01232" "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131"
#> [21] "0322" "01232" "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121"
#> [31] "0322" "01232" "0322" "0322" "0322" "01232" "01232" "01232" "03121" "0311"
#> [41] "01131" "02222" "01131" "0311" "01232" "01232" "0311" "03121" "0322" "01131"
#> [51] "03121" "01131" "01131" "01232" "01232" "02123" "02123" "0143" "01133" "0313"
#> [61] "0322" "01131" "01131" "02221" "0311" "01132" "01232" "01232" "0322" "01232"
#> [71] "0322" "0311" "0322" "01131" "01131" "0143" "0111" "0112" "02221" "01131"
#> [81] "0143" "0322" "01131" "0143" "01133" "02221" "01131" "01131" "01131" "01231"
#> [91] "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143" "0313"
#> [101] "01131" "01131" "0111" "01133" "0111" "0322" "02221" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "02221" "02221" "0322" "01132" "0321"
#> [121] "0313" "0322" "02222" "02221" "02222" "0234" "01231" "0111" "01133" "01133"
#> [131] "01231" "01131" "01133" "0324" "0111" "02222" "01131" "01131" "0322" "0111"
#> [141] "01131" "0111" "01232" "01231" "01231" "02222" "01131" "02123" "01131" "0324"
#> [151] "0313" "0313" "01131" "0313" "0322" "01131" "0313" "0234" "0322" "0322"
#> [161] "0322" "01131" "0313" "0313" "02222" "01131" "0322" "0313" "01131" "01131"
#> [171] "0322" "0313" "0313" "02222" "02222" "0313" "0313" "01131" "0313" "0313"
#> [181] "03121" "0313" "0322" "0313" "0322" "0313" "0313" "0313" "03121" "02222"
#> [191] "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121" "03121" "03122"
#> [201] "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322" "02222"
#> [211] "0313" "0234" "0313" "03121" "0313" "0313" "0322" "02222" "03121" "01133"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "01131" "02113"
#> [231] "0313" "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313"
#> [241] "0313" "0313" "03121" "01133" "03121" "03121" "03121" "02222" "03121" "0313"
#> [251] "01133" "0313" "03121" "03121" "0313" "0313" "01133" "03121" "0313" "0313"
#> [261] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "01133" "0313"
#> [271] "01133" "01133" "0313" "0313" "01133" "0313" "0313" "0313" "0313" "0322"
#> [281] "02123" "01133" "0313" "0313" "0313" "02222" "0313" "0313" "03121" "03121"
#> [291] "03121" "03122" "03121" "03121" "03121" "03121" "03121" "03122" "02113" "03121"
#> [301] "02113" "0313" "0313" "0234" "0313" "02113" "02222" "03122" "02222" "03121"
#> [311] "03121" "0313" "02222" "0313" "0313" "03121" "01133" "0313" "0313" "01133"
#> [321] "0313" "01133" "03121" "0313" "0311" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "02222" "02222" "01132" "0313" "0112"
#> [341] "0313" "0313" "02222" "0313" "0313" "0313" "02222" "0311" "0311" "02222"
#> [351] "0313" "01133" "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122"
#> [361] "03121" "0313" "0313" "0313" "0313" "0313" "0313" "03121" "03121" "03121"
#> [371] "0313" "03122" "03122" "03121" "03121" "03121" "03121" "03122" "03122" "0313"
#> [381] "0111" "0112" "02222" "0311" "0112" "0111" "0112" "0311" "0324" "0311"
#> [391] "0112" "0311" "0112" "0311" "02222" "0311" "0313" "0311" "0112" "0111"
#> [401] "0311" "0112" "0143" "0311" "0112" "02222" "0111" "0311" "0112" "0112"
#> [411] "0311" "0311" "02123" "0112" "0112" "0112" "0111" "01133" "0311" "0111"
#> [421] "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112" "0112"
#> [431] "0112" "0234" "0112" "0234" "0111" "02221" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "03122" "03122" "03121"
#> [451] "0311" "0112" "0311" "0112" "0112" "03121" "0112" "0112" "03122" "03122"
#> [461] "0311" "03122" "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122"
#> [471] "03122" "0311" "03122" "03122" "03122" "0311" "0311" "03121" "0311" "0311"
#> [481] "0311" "0311" "0311" "0112" "02123" "03122" "0311" "0311" "02222" "02222"
#> [491] "02123" "03121" "03122" "02222" "03122" "0112" "02123" "02113" "0112" "03122"
#> [501] "02113" "0112" "0311" "03122" "0311" "02113" "0112" "0311" "0311" "0311"
#> [511] "0311" "02222" "0311" "0311" "0112" "0112" "02222" "0311" "03121" "0311"
#> [521] "0112" "0112" "0112" "03122" "03121" "0313" "03121" "0112" "0112" "02221"
#> [531] "02123" "02123" "0112" "02222" "0111" "0111" "0111" "02123" "0111" "0311"
#> [541] "0112" "02222" "0111" "0112" "02222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143"
#> [561] "0311" "01132" "0324" "0324" "01132" "0112" "0111" "02221" "0311" "0112"
#> [571] "0112" "02221" "0324" "0311" "0112" "03121" "0111" "0112" "0112" "02221"
#> [581] "0112" "0112" "0111" "0112" "0311" "0112" "0311" "0112" "0111" "01132"
#> [591] "0111" "0313" "0112" "03122" "0313" "0324" "0112" "0313" "0313" "0111"
#> [601] "0111" "01132" "0111" "0313" "0111" "0112" "02222" "0111" "0111" "0111"
#> [611] "0111" "0111" "0112" "0111" "0111" "0234" "0311" "0311" "0112" "0311"
#> [621] "0311" "0313" "0112" "0311" "0112" "0311" "0311" "0311" "0311" "0311"
#> [631] "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112" "0112" "0112"
#> [641] "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "02222" "0112"
#> [651] "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "03122" "0311" "0311" "0311" "0311"
#> [671] "02222" "0112" "02222" "0311" "0313" "0234" "0311" "0311" "02222" "0112"
#> [681] "0311" "0311" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311"
#> [691] "0311" "0112" "03122" "0311" "02222" "0311" "03122" "0112" "03122" "0143"
#> [701] "03122" "0112" "0111" "0311" "0311" "02222" "02222" "0112" "0324" "0112"
#> [711] "0324" "02123" "0111" "0112" "0111" "0112" "0111" "0111" "02221" "0311"
#> [721] "0311" "02221" "0234" "0112" "02221" "0311" "0311" "0311" "0112" "0112"
#> [731] "0311" "0112" "0111" "0311" "0112" "0112" "0111" "0111" "0111" "0311"
#> [741] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03122" "03122" "03122"
#> [751] "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324" "0311"
#> [761] "02123" "02222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "03122" "0112" "0112" "0311" "0112" "02222" "0111" "0112" "02113" "0112"
#> [781] "0311" "0112" "0112" "0111" "0112" "0112" "03122" "0111" "0311" "0311"
#> [791] "0112" "0112" "03122" "02222" "0112" "03122" "0111" "0111" "0234" "0311"
#> [801] "03122" "02222" "0311" "0311" "0311" "0234" "0311" "0112" "0311" "0112"
#> [811] "0112" "0112" "0324" "0324" "01231" "0143" "0111" "0112" "0111" "02123"
#> [821] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [831] "0111" "0111" "0111" "01231" "0111" "0111" "0111" "0111" "0111" "0111"
#> [841] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0324" "01231"
#> [851] "01132" "0234" "0324" "02222" "0111" "0143" "0143" "0143" "0324" "0111"
#> [861] "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0311"
#> [871] "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324"
#> [891] "0111" "0311" "0111" "0111" "0111" "02221" "02221" "0111" "0111" "0111"
#> [901] "0111" "0111" "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111"
#> [911] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [921] "0111" "0111" "0111" "0112" "0111" "0111" "0111" "0111" "02221" "0111"
#> [931] "0143" "0111" "0111" "0111" "02221" "0324" "0111" "02221" "0111" "0111"
#> [941] "0111" "0143" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [951] "0111" "0111" "0111" "0311" "01132" "02123" "0143" "0143" "0111" "02123"
#> [961] "02221" "0112" "0111" "02123" "0311" "0112" "0111" "0111" "0111" "0311"
#> [971] "0111" "0111" "02221" "0112" "01231" "0111" "0111" "0111" "0111" "0111"
#> [981] "0324" "0324" "02222" "02221" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111"
#> [1001] "02221" "013" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1011] "0121" "0121" "0121" "02221" "0121" "02221" "0121" "0121" "0121" "0121"
#> [1021] "0121" "0121" "0121" "0121" "0121" "0323" "0142" "0121" "0121" "0121"
#> [1031] "0121" "0121" "0121" "0121" "0121" "0311" "0121" "03121" "0121" "02113"
#> [1041] "0121" "01132" "0322" "0121" "0121" "0121" "03121" "0121" "0121" "0121"
#> [1051] "01132" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "01132" "0121"
#> [1061] "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1071] "0121" "0121" "013" "0121" "0322" "0121" "0121" "0121" "013" "0121"
#> [1081] "0121" "0121" "03121" "0121" "0121" "0121" "0121" "0311" "0121" "0121"
#> [1091] "0121" "0121" "01132" "013" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1101] "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0311"
#> [1111] "0121" "0121" "0121" "0121" "0121" "013" "0121" "0313" "0121" "0141"
#> [1121] "0121" "0313" "0121" "0121" "0121" "0121" "0121" "03122" "0121" "0121"
#> [1131] "0121" "01132" "01132" "0121" "0322" "0121" "0121" "0121" "0121" "0121"
#> [1141] "0221" "0121" "0121" "01132" "0322" "01232" "0121" "0111" "0121" "0142"
#> [1151] "0121" "0121" "01231" "01232" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1161] "01132" "01232" "01132" "0121" "0121" "0121" "0121" "0111" "01132" "0111"
#> [1171] "01231" "0311" "01132" "0121" "01132" "01132" "0121" "0121" "0121" "0121"
#> [1181] "0223" "0121" "0141" "0121" "0221" "0111" "0121" "0121" "01132" "0121"
#> [1191] "0121" "0121" "0121" "0121" "0121" "0141" "0121" "01231" "013" "0121"
#> [1201] "0121" "0141" "0121" "0121" "0121" "0121" "0121" "0112" "0121" "0121"
#> [1211] "013" "0121" "01232" "0141" "02221" "0121" "0321" "0313" "0121" "0121"
#> [1221] "01131" "0121" "0121" "0221" "0121" "0223" "01232" "0121" "0121" "0141"
#> [1231] "0121" "0121" "0121" "01132" "0121" "01131" "0112" "0313" "0141" "0121"
#> [1241] "0333" "0321" "0311" "0121" "0121" "0121" "01132" "01132" "0121" "0121"
#> [1251] "01132" "01234" "0112" "0111" "0112" "0221" "0221" "0221" "0223" "01223"
#> [1261] "0121" "01223" "0121" "01234" "0311" "0141" "0111" "01132" "013" "013"
#> [1271] "013" "013" "0331" "013" "013" "013" "0333" "02122" "0332" "0332"
#> [1281] "0331" "013" "0332" "013" "013" "013" "013" "013" "0332" "0332"
#> [1291] "013" "0331" "013" "013" "0233" "0333" "013" "013" "013" "013"
#> [1301] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1311] "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "01223" "013" "013"
#> [1331] "013" "02113" "013" "0331" "013" "0333" "013" "013" "013" "013"
#> [1341] "013" "013" "02221" "013" "013" "0223" "0233" "02113" "013" "013"
#> [1351] "013" "013" "02113" "0223" "013" "013" "013" "013" "013" "013"
#> [1361] "02122" "02122" "013" "013" "0331" "0331" "013" "0331" "013" "0331"
#> [1371] "0331" "0331" "0332" "013" "0331" "013" "013" "013" "013" "013"
#> [1381] "013" "013" "013" "013" "013" "013" "013" "01234" "01223" "01231"
#> [1391] "01234" "0321" "013" "013" "0231" "0141" "02113" "0233" "0233" "01231"
#> [1401] "0233" "013" "013" "013" "013" "0333" "0233" "013" "013" "0311"
#> [1411] "013" "013" "013" "013" "013" "013" "013" "013" "0333" "013"
#> [1421] "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "0112" "013" "0311" "01231" "013" "013" "013" "013" "013"
#> [1441] "013" "013" "013" "013" "013" "01231" "013" "013" "0233" "013"
#> [1451] "0333" "0221" "013" "013" "0221" "013" "013" "013" "0223" "013"
#> [1461] "013" "013" "013" "013" "01231" "013" "0111" "0111" "01131" "013"
#> [1471] "013" "0333" "01231" "0313" "0333" "0313" "0112" "02121" "013" "0221"
#> [1481] "01232" "013" "013" "0111" "013" "013" "013" "0321" "0141" "013"
#> [1491] "0141" "013" "013" "0111" "0231" "0141" "013" "0111" "013" "0233"
#> [1501] "01231" "0141" "013" "0111" "01231" "0321" "013" "02222" "013" "0223"
#> [1511] "01231" "013" "01231" "013" "013" "01231" "013" "0221" "0331" "0221"
#> [1521] "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013" "013"
#> [1531] "0142" "013" "013" "02113" "01223" "0223" "0112" "0111" "013" "013"
#> [1541] "01232" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1551] "0311" "013" "0111" "013" "013" "013" "0142" "02121" "0233" "013"
#> [1561] "01231" "01231" "0143" "03121" "0223" "01133" "013" "0333" "013" "01231"
#> [1571] "013" "0223" "02121" "0142" "02121" "0332" "0332" "02113" "0233" "0233"
#> [1581] "0332" "02113" "0332" "0233" "0332" "0332" "0331" "0332" "0331" "0332"
#> [1591] "013" "0331" "0332" "02221" "0331" "02113" "02121" "0233" "013" "02113"
#> [1601] "013" "0332" "013" "02123" "02113" "013" "013" "0233" "02113" "02113"
#> [1611] "0331" "0331" "0332" "0331" "0331" "0331" "0331" "02113" "013" "02221"
#> [1621] "02113" "0233" "013" "0331" "01132" "02122" "01234" "013" "01234" "013"
#> [1631] "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221" "01234"
#> [1641] "01234" "013" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234"
#> [1661] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234"
#> [1671] "01234" "01231" "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233"
#> [1681] "01233" "01233" "01221" "0141" "01233" "0121" "01132" "0121" "01232" "01223"
#> [1691] "01233" "01233" "0322" "0121" "01233" "01233" "01233" "0112" "01233" "01233"
#> [1701] "0112" "01233" "0311" "01233" "01233" "01221" "0323" "01223" "01233" "01233"
#> [1711] "013" "0311" "01233" "01233" "01223" "013" "01233" "01233" "0323" "0323"
#> [1721] "013" "01233" "0141" "01233" "01233" "01233" "01233" "0313" "01233" "0311"
#> [1731] "0311" "01233" "0121" "02121" "01231" "01133" "01223" "01133" "0112" "0111"
#> [1741] "01221" "01223" "013" "01221" "013" "01221" "01222" "01223" "0323" "01222"
#> [1751] "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "03122"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "013" "0221"
#> [1771] "01221" "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221"
#> [1781] "0322" "01132" "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323"
#> [1791] "01222" "0313" "0313" "0323" "0223" "01132" "01221" "0313" "0223" "01221"
#> [1801] "01221" "01221" "01222" "0323" "01221" "01221" "0233" "02121" "0223" "0311"
#> [1811] "0221" "01221" "01221" "01131" "01223" "01221" "01221" "01221" "01221" "01221"
#> [1821] "0313" "01221" "01221" "01221" "01221" "01221" "01221" "01221" "01222" "0223"
#> [1831] "01221" "01221" "0323" "01221" "01222" "02122" "0223" "01221" "0111" "01221"
#> [1841] "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221" "01222" "01221"
#> [1851] "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322" "01233"
#> [1861] "03122" "01233" "01233" "0332" "0223" "03122" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "0121"
#> [1881] "02112" "01223" "01223" "01223" "01223" "0323" "01222" "03122" "01222" "01222"
#> [1891] "01132" "0221" "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121"
#> [1901] "013" "0323" "01223" "0311" "01223" "01223" "0332" "01223" "01222" "01222"
#> [1911] "01222" "01222" "01222" "01221" "0323" "01222" "01221" "01132" "01221" "03122"
#> [1921] "0223" "01222" "0323" "0323" "01222" "0311" "01222" "01222" "01222" "0233"
#> [1931] "0323" "01222" "02112" "01222" "0323" "0233" "0333" "01222" "01222" "0323"
#> [1941] "0323" "01222" "0323" "01222" "0332" "02221" "03122" "0323" "01222" "03121"
#> [1951] "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323" "02113" "0323"
#> [1961] "0221" "0323" "0323" "02221" "01222" "0323" "02112" "0331" "0323" "03122"
#> [1971] "01222" "0233" "03122" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112"
#> [1991] "0221" "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231"
#> [2001] "0223" "02113" "02112" "0232" "02112" "02221" "0221" "02121" "0232" "0232"
#> [2011] "02123" "0231" "02121" "0231" "0142" "0221" "0231" "0321" "0223" "02112"
#> [2021] "02122" "02221" "0223" "0221" "02221" "0321" "0223" "02122" "02122" "0223"
#> [2031] "02221" "02122" "0223" "0232" "0221" "02113" "0221" "02112" "0223" "0223"
#> [2041] "0221" "0321" "02112" "0233" "0232" "02113" "02122" "02121" "02121" "0142"
#> [2051] "0221" "02113" "0231" "02113" "02112" "02121" "0223" "02122" "0321" "0223"
#> [2061] "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122" "02122" "02112"
#> [2071] "02112" "0223" "0232" "02221" "02113" "0233" "02112" "02221" "02112" "02112"
#> [2081] "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231"
#> [2101] "02121" "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112"
#> [2111] "0231" "0223" "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232"
#> [2121] "02121" "02121" "0233" "0232" "0142" "0223" "02121" "0142" "02112" "02112"
#> [2131] "02122" "02121" "02112" "02112" "02112" "02121" "02122" "02121" "0221" "02121"
#> [2141] "02221" "0223" "02122" "0221" "0221" "02221" "0223" "02121" "0223" "02121"
#> [2151] "02112" "02122" "0223" "0223" "02122" "02112" "02121" "0223" "0223" "02112"
#> [2161] "0221" "0223" "0221" "02122" "0223" "0223" "0221" "02121" "0223" "0223"
#> [2171] "0223" "0221" "02121" "0321" "0221" "0221" "0221" "021112" "02122" "02122"
#> [2181] "02122" "0223" "0234" "02222" "0223" "0221" "0221" "0221" "0221" "0143"
#> [2191] "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "02221" "02121" "02121" "02121" "0231"
#> [2211] "0232" "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121"
#> [2221] "0223" "02123" "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121"
#> [2231] "0223" "0223" "0223" "0223" "02121" "0221" "0321" "02221" "0221" "0321"
#> [2241] "0221" "0321" "0223" "0221" "0223" "0223" "0223" "0231" "0231" "0221"
#> [2251] "02221" "0321" "02221" "0221" "0231" "0231" "0221" "0221" "0141" "0321"
#> [2261] "02112" "0221" "0221" "0221" "0223" "0321" "0231" "0221" "0321" "0223"
#> [2271] "0223" "0223" "0142" "0223" "0142" "02221" "0223" "0321" "0221" "0231"
#> [2281] "02221" "0221" "0141" "02221" "0221" "0221" "0142" "0321" "0321" "0221"
#> [2291] "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141" "0321"
#> [2301] "0142" "0142" "0141" "0223" "0142" "02221" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221"
#> [2321] "0142" "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221"
#> [2331] "0223" "0221" "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141"
#> [2341] "0321" "0321" "0221" "02122" "0232" "0223" "0223" "0223" "0221" "0221"
#> [2351] "0321" "02221" "0223" "0223" "0221" "0221" "0321" "02121" "02112" "0221"
#> [2361] "02121" "0221" "02121" "0234" "02121" "02122" "0221" "02112" "02112" "0221"
#> [2371] "0223" "0223" "02121" "0223" "02121" "0223" "0221" "0221" "02123" "02121"
#> [2381] "0232" "0223" "02112" "02122" "0232" "0221" "0223" "0223" "0223" "0231"
#> [2391] "02113" "0223" "0221" "0221" "021112" "02121" "02122" "0223" "0321" "0221"
#> [2401] "0141" "0141" "0141" "02122" "0221" "0231" "021112" "0223" "02122" "02221"
#> [2411] "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223"
#> [2431] "0221" "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141"
#> [2441] "0141" "01234" "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223"
#> [2451] "02122" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "02122" "0231"
#> [2461] "02122" "02122" "02122" "02123" "02122" "02123" "02122" "02122" "02221" "0221"
#> [2471] "0321" "0221" "0221" "0221" "02122" "02122" "0223" "02122" "0223" "0221"
#> [2481] "0223" "02122" "0223" "0223" "02112" "0223" "02122" "02122" "02122" "02112"
#> [2491] "02123" "02122" "02122" "02112" "0223" "02122" "0223" "02122" "02122" "0221"
#> [2501] "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321" "0321" "0221"
#> [2511] "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221" "02122"
#> [2521] "02121" "02112" "0221" "02221" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231"
#> [2541] "0221" "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221"
#> [2551] "0321" "0321" "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121"
#> [2561] "0321" "02122" "0321" "0223" "0221" "0321" "0221" "0221" "0221" "0221"
#> [2571] "0223" "0142" "0141" "0141" "0321" "0321" "0221" "0221" "02112" "02122"
#> [2581] "02122" "0223" "0223" "0221" "0221" "02221" "0221" "0142" "021112" "0232"
#> [2591] "0234" "0232" "02113" "02113" "021111" "02113" "02113" "021111" "0231" "02113"
#> [2601] "021111" "021111" "0232" "02113" "0232" "0231" "0234" "0232" "0323" "0142"
#> [2611] "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231" "0231" "0234"
#> [2621] "0233" "0232" "0142" "02112" "02222" "0231" "0142" "0142" "0141" "0231"
#> [2631] "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221"
#> [2651] "01221" "0221" "0221" "0221" "0221" "0221" "0231" "0221" "02221" "0221"
#> [2661] "0221" "0221" "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321"
#> [2671] "0221" "0141" "0321" "0221" "0321" "0221" "0221" "0324" "01231" "0141"
#> [2681] "01231" "0221" "0141" "01231" "0121" "0232" "0232" "02112" "02112" "0321"
#> [2691] "02121" "02121" "0234" "0231" "0143" "0221" "0324" "02121" "0221" "0321"
#> [2701] "0221" "02121" "0141" "02221" "02221" "0321" "0142" "02221" "0141" "0142"
#> [2711] "02221" "0141" "0141" "0231" "02221" "0231" "0141" "0142" "0231" "0141"
#> [2721] "0223" "02221" "0141" "02112" "0321" "0141" "0321" "0141" "01231" "0321"
#> [2731] "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141" "0321"
#> [2741] "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "021111" "021111" "021111"
#> [2761] "021111" "021111" "021111" "021111" "0232" "0142" "0142" "0221" "021111" "02113"
#> [2771] "021112" "021112" "021111" "021111" "021111" "021112" "021111" "021111" "021112" "021112"
#> [2781] "021112" "0231" "021111" "021111" "021111" "0232" "021111" "0232" "021111" "0142"
#> [2791] "0142" "0223" "0231" "0231" "021112" "021112" "021112" "021112" "021111" "021111"
#> [2801] "021111" "02113" "0233" "021112" "02113" "021111" "021112" "0232" "021111" "021111"
#> [2811] "021111" "021111" "021112" "021112" "021111" "021112" "0221" "0142" "0142" "0142"
#> [2821] "0221" "02121" "0231" "021112" "021112" "02121" "021112" "02121" "021112" "0223"
#> [2831] "02121" "02121" "021112" "02121" "0231" "0223" "02121" "02121" "0232" "0231"
#> [2841] "021112" "021112" "02112" "02112" "02121" "021111" "02112" "021112" "021112" "02112"
#> [2851] "021112" "021111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221"
#> [2871] "021112" "02121" "02123" "021111" "021112" "02121" "0223" "02121" "0142" "02121"
#> [2881] "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 677))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232"
#> [11] "01232" "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131"
#> [21] "0322" "01232" "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121"
#> [31] "0322" "01232" "0322" "0322" "0322" "01232" "01232" "01232" "03121" "0311"
#> [41] "01131" "0222" "01131" "0311" "01232" "01232" "0311" "03121" "0322" "01131"
#> [51] "03121" "01131" "01131" "01232" "01232" "02123" "02123" "0143" "01133" "0313"
#> [61] "0322" "01131" "01131" "0222" "0311" "01132" "01232" "01232" "0322" "01232"
#> [71] "0322" "0311" "0322" "01131" "01131" "0143" "0111" "0112" "0222" "01131"
#> [81] "0143" "0322" "01131" "0143" "01133" "0222" "01131" "01131" "01131" "01231"
#> [91] "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143" "0313"
#> [101] "01131" "01131" "0111" "01133" "0111" "0322" "0222" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "0222" "0222" "0322" "01132" "0321"
#> [121] "0313" "0322" "0222" "0222" "0222" "0234" "01231" "0111" "01133" "01133"
#> [131] "01231" "01131" "01133" "0324" "0111" "0222" "01131" "01131" "0322" "0111"
#> [141] "01131" "0111" "01232" "01231" "01231" "0222" "01131" "02123" "01131" "0324"
#> [151] "0313" "0313" "01131" "0313" "0322" "01131" "0313" "0234" "0322" "0322"
#> [161] "0322" "01131" "0313" "0313" "0222" "01131" "0322" "0313" "01131" "01131"
#> [171] "0322" "0313" "0313" "0222" "0222" "0313" "0313" "01131" "0313" "0313"
#> [181] "03121" "0313" "0322" "0313" "0322" "0313" "0313" "0313" "03121" "0222"
#> [191] "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121" "03121" "03122"
#> [201] "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322" "0222"
#> [211] "0313" "0234" "0313" "03121" "0313" "0313" "0322" "0222" "03121" "01133"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "01131" "02113"
#> [231] "0313" "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313"
#> [241] "0313" "0313" "03121" "01133" "03121" "03121" "03121" "0222" "03121" "0313"
#> [251] "01133" "0313" "03121" "03121" "0313" "0313" "01133" "03121" "0313" "0313"
#> [261] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "01133" "0313"
#> [271] "01133" "01133" "0313" "0313" "01133" "0313" "0313" "0313" "0313" "0322"
#> [281] "02123" "01133" "0313" "0313" "0313" "0222" "0313" "0313" "03121" "03121"
#> [291] "03121" "03122" "03121" "03121" "03121" "03121" "03121" "03122" "02113" "03121"
#> [301] "02113" "0313" "0313" "0234" "0313" "02113" "0222" "03122" "0222" "03121"
#> [311] "03121" "0313" "0222" "0313" "0313" "03121" "01133" "0313" "0313" "01133"
#> [321] "0313" "01133" "03121" "0313" "0311" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "0222" "0222" "01132" "0313" "0112"
#> [341] "0313" "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222"
#> [351] "0313" "01133" "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122"
#> [361] "03121" "0313" "0313" "0313" "0313" "0313" "0313" "03121" "03121" "03121"
#> [371] "0313" "03122" "03122" "03121" "03121" "03121" "03121" "03122" "03122" "0313"
#> [381] "0111" "0112" "0222" "0311" "0112" "0111" "0112" "0311" "0324" "0311"
#> [391] "0112" "0311" "0112" "0311" "0222" "0311" "0313" "0311" "0112" "0111"
#> [401] "0311" "0112" "0143" "0311" "0112" "0222" "0111" "0311" "0112" "0112"
#> [411] "0311" "0311" "02123" "0112" "0112" "0112" "0111" "01133" "0311" "0111"
#> [421] "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112" "0112"
#> [431] "0112" "0234" "0112" "0234" "0111" "0222" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "03122" "03122" "03121"
#> [451] "0311" "0112" "0311" "0112" "0112" "03121" "0112" "0112" "03122" "03122"
#> [461] "0311" "03122" "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122"
#> [471] "03122" "0311" "03122" "03122" "03122" "0311" "0311" "03121" "0311" "0311"
#> [481] "0311" "0311" "0311" "0112" "02123" "03122" "0311" "0311" "0222" "0222"
#> [491] "02123" "03121" "03122" "0222" "03122" "0112" "02123" "02113" "0112" "03122"
#> [501] "02113" "0112" "0311" "03122" "0311" "02113" "0112" "0311" "0311" "0311"
#> [511] "0311" "0222" "0311" "0311" "0112" "0112" "0222" "0311" "03121" "0311"
#> [521] "0112" "0112" "0112" "03122" "03121" "0313" "03121" "0112" "0112" "0222"
#> [531] "02123" "02123" "0112" "0222" "0111" "0111" "0111" "02123" "0111" "0311"
#> [541] "0112" "0222" "0111" "0112" "0222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143"
#> [561] "0311" "01132" "0324" "0324" "01132" "0112" "0111" "0222" "0311" "0112"
#> [571] "0112" "0222" "0324" "0311" "0112" "03121" "0111" "0112" "0112" "0222"
#> [581] "0112" "0112" "0111" "0112" "0311" "0112" "0311" "0112" "0111" "01132"
#> [591] "0111" "0313" "0112" "03122" "0313" "0324" "0112" "0313" "0313" "0111"
#> [601] "0111" "01132" "0111" "0313" "0111" "0112" "0222" "0111" "0111" "0111"
#> [611] "0111" "0111" "0112" "0111" "0111" "0234" "0311" "0311" "0112" "0311"
#> [621] "0311" "0313" "0112" "0311" "0112" "0311" "0311" "0311" "0311" "0311"
#> [631] "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112" "0112" "0112"
#> [641] "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "0222" "0112"
#> [651] "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "03122" "0311" "0311" "0311" "0311"
#> [671] "0222" "0112" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "0112"
#> [681] "0311" "0311" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311"
#> [691] "0311" "0112" "03122" "0311" "0222" "0311" "03122" "0112" "03122" "0143"
#> [701] "03122" "0112" "0111" "0311" "0311" "0222" "0222" "0112" "0324" "0112"
#> [711] "0324" "02123" "0111" "0112" "0111" "0112" "0111" "0111" "0222" "0311"
#> [721] "0311" "0222" "0234" "0112" "0222" "0311" "0311" "0311" "0112" "0112"
#> [731] "0311" "0112" "0111" "0311" "0112" "0112" "0111" "0111" "0111" "0311"
#> [741] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03122" "03122" "03122"
#> [751] "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324" "0311"
#> [761] "02123" "0222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "03122" "0112" "0112" "0311" "0112" "0222" "0111" "0112" "02113" "0112"
#> [781] "0311" "0112" "0112" "0111" "0112" "0112" "03122" "0111" "0311" "0311"
#> [791] "0112" "0112" "03122" "0222" "0112" "03122" "0111" "0111" "0234" "0311"
#> [801] "03122" "0222" "0311" "0311" "0311" "0234" "0311" "0112" "0311" "0112"
#> [811] "0112" "0112" "0324" "0324" "01231" "0143" "0111" "0112" "0111" "02123"
#> [821] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [831] "0111" "0111" "0111" "01231" "0111" "0111" "0111" "0111" "0111" "0111"
#> [841] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0324" "01231"
#> [851] "01132" "0234" "0324" "0222" "0111" "0143" "0143" "0143" "0324" "0111"
#> [861] "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112" "0311"
#> [871] "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324"
#> [891] "0111" "0311" "0111" "0111" "0111" "0222" "0222" "0111" "0111" "0111"
#> [901] "0111" "0111" "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111"
#> [911] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [921] "0111" "0111" "0111" "0112" "0111" "0111" "0111" "0111" "0222" "0111"
#> [931] "0143" "0111" "0111" "0111" "0222" "0324" "0111" "0222" "0111" "0111"
#> [941] "0111" "0143" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [951] "0111" "0111" "0111" "0311" "01132" "02123" "0143" "0143" "0111" "02123"
#> [961] "0222" "0112" "0111" "02123" "0311" "0112" "0111" "0111" "0111" "0311"
#> [971] "0111" "0111" "0222" "0112" "01231" "0111" "0111" "0111" "0111" "0111"
#> [981] "0324" "0324" "0222" "0222" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111"
#> [1001] "0222" "013" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1011] "0121" "0121" "0121" "0222" "0121" "0222" "0121" "0121" "0121" "0121"
#> [1021] "0121" "0121" "0121" "0121" "0121" "0323" "0142" "0121" "0121" "0121"
#> [1031] "0121" "0121" "0121" "0121" "0121" "0311" "0121" "03121" "0121" "02113"
#> [1041] "0121" "01132" "0322" "0121" "0121" "0121" "03121" "0121" "0121" "0121"
#> [1051] "01132" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "01132" "0121"
#> [1061] "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1071] "0121" "0121" "013" "0121" "0322" "0121" "0121" "0121" "013" "0121"
#> [1081] "0121" "0121" "03121" "0121" "0121" "0121" "0121" "0311" "0121" "0121"
#> [1091] "0121" "0121" "01132" "013" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1101] "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0311"
#> [1111] "0121" "0121" "0121" "0121" "0121" "013" "0121" "0313" "0121" "0141"
#> [1121] "0121" "0313" "0121" "0121" "0121" "0121" "0121" "03122" "0121" "0121"
#> [1131] "0121" "01132" "01132" "0121" "0322" "0121" "0121" "0121" "0121" "0121"
#> [1141] "0221" "0121" "0121" "01132" "0322" "01232" "0121" "0111" "0121" "0142"
#> [1151] "0121" "0121" "01231" "01232" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1161] "01132" "01232" "01132" "0121" "0121" "0121" "0121" "0111" "01132" "0111"
#> [1171] "01231" "0311" "01132" "0121" "01132" "01132" "0121" "0121" "0121" "0121"
#> [1181] "0223" "0121" "0141" "0121" "0221" "0111" "0121" "0121" "01132" "0121"
#> [1191] "0121" "0121" "0121" "0121" "0121" "0141" "0121" "01231" "013" "0121"
#> [1201] "0121" "0141" "0121" "0121" "0121" "0121" "0121" "0112" "0121" "0121"
#> [1211] "013" "0121" "01232" "0141" "0222" "0121" "0321" "0313" "0121" "0121"
#> [1221] "01131" "0121" "0121" "0221" "0121" "0223" "01232" "0121" "0121" "0141"
#> [1231] "0121" "0121" "0121" "01132" "0121" "01131" "0112" "0313" "0141" "0121"
#> [1241] "0333" "0321" "0311" "0121" "0121" "0121" "01132" "01132" "0121" "0121"
#> [1251] "01132" "01234" "0112" "0111" "0112" "0221" "0221" "0221" "0223" "01223"
#> [1261] "0121" "01223" "0121" "01234" "0311" "0141" "0111" "01132" "013" "013"
#> [1271] "013" "013" "0331" "013" "013" "013" "0333" "02122" "0332" "0332"
#> [1281] "0331" "013" "0332" "013" "013" "013" "013" "013" "0332" "0332"
#> [1291] "013" "0331" "013" "013" "0233" "0333" "013" "013" "013" "013"
#> [1301] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1311] "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "01223" "013" "013"
#> [1331] "013" "02113" "013" "0331" "013" "0333" "013" "013" "013" "013"
#> [1341] "013" "013" "0222" "013" "013" "0223" "0233" "02113" "013" "013"
#> [1351] "013" "013" "02113" "0223" "013" "013" "013" "013" "013" "013"
#> [1361] "02122" "02122" "013" "013" "0331" "0331" "013" "0331" "013" "0331"
#> [1371] "0331" "0331" "0332" "013" "0331" "013" "013" "013" "013" "013"
#> [1381] "013" "013" "013" "013" "013" "013" "013" "01234" "01223" "01231"
#> [1391] "01234" "0321" "013" "013" "0231" "0141" "02113" "0233" "0233" "01231"
#> [1401] "0233" "013" "013" "013" "013" "0333" "0233" "013" "013" "0311"
#> [1411] "013" "013" "013" "013" "013" "013" "013" "013" "0333" "013"
#> [1421] "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "0112" "013" "0311" "01231" "013" "013" "013" "013" "013"
#> [1441] "013" "013" "013" "013" "013" "01231" "013" "013" "0233" "013"
#> [1451] "0333" "0221" "013" "013" "0221" "013" "013" "013" "0223" "013"
#> [1461] "013" "013" "013" "013" "01231" "013" "0111" "0111" "01131" "013"
#> [1471] "013" "0333" "01231" "0313" "0333" "0313" "0112" "02121" "013" "0221"
#> [1481] "01232" "013" "013" "0111" "013" "013" "013" "0321" "0141" "013"
#> [1491] "0141" "013" "013" "0111" "0231" "0141" "013" "0111" "013" "0233"
#> [1501] "01231" "0141" "013" "0111" "01231" "0321" "013" "0222" "013" "0223"
#> [1511] "01231" "013" "01231" "013" "013" "01231" "013" "0221" "0331" "0221"
#> [1521] "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013" "013"
#> [1531] "0142" "013" "013" "02113" "01223" "0223" "0112" "0111" "013" "013"
#> [1541] "01232" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1551] "0311" "013" "0111" "013" "013" "013" "0142" "02121" "0233" "013"
#> [1561] "01231" "01231" "0143" "03121" "0223" "01133" "013" "0333" "013" "01231"
#> [1571] "013" "0223" "02121" "0142" "02121" "0332" "0332" "02113" "0233" "0233"
#> [1581] "0332" "02113" "0332" "0233" "0332" "0332" "0331" "0332" "0331" "0332"
#> [1591] "013" "0331" "0332" "0222" "0331" "02113" "02121" "0233" "013" "02113"
#> [1601] "013" "0332" "013" "02123" "02113" "013" "013" "0233" "02113" "02113"
#> [1611] "0331" "0331" "0332" "0331" "0331" "0331" "0331" "02113" "013" "0222"
#> [1621] "02113" "0233" "013" "0331" "01132" "02122" "01234" "013" "01234" "013"
#> [1631] "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221" "01234"
#> [1641] "01234" "013" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234"
#> [1661] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234"
#> [1671] "01234" "01231" "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233"
#> [1681] "01233" "01233" "01221" "0141" "01233" "0121" "01132" "0121" "01232" "01223"
#> [1691] "01233" "01233" "0322" "0121" "01233" "01233" "01233" "0112" "01233" "01233"
#> [1701] "0112" "01233" "0311" "01233" "01233" "01221" "0323" "01223" "01233" "01233"
#> [1711] "013" "0311" "01233" "01233" "01223" "013" "01233" "01233" "0323" "0323"
#> [1721] "013" "01233" "0141" "01233" "01233" "01233" "01233" "0313" "01233" "0311"
#> [1731] "0311" "01233" "0121" "02121" "01231" "01133" "01223" "01133" "0112" "0111"
#> [1741] "01221" "01223" "013" "01221" "013" "01221" "01222" "01223" "0323" "01222"
#> [1751] "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "03122"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "013" "0221"
#> [1771] "01221" "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221"
#> [1781] "0322" "01132" "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323"
#> [1791] "01222" "0313" "0313" "0323" "0223" "01132" "01221" "0313" "0223" "01221"
#> [1801] "01221" "01221" "01222" "0323" "01221" "01221" "0233" "02121" "0223" "0311"
#> [1811] "0221" "01221" "01221" "01131" "01223" "01221" "01221" "01221" "01221" "01221"
#> [1821] "0313" "01221" "01221" "01221" "01221" "01221" "01221" "01221" "01222" "0223"
#> [1831] "01221" "01221" "0323" "01221" "01222" "02122" "0223" "01221" "0111" "01221"
#> [1841] "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221" "01222" "01221"
#> [1851] "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322" "01233"
#> [1861] "03122" "01233" "01233" "0332" "0223" "03122" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "0121"
#> [1881] "02112" "01223" "01223" "01223" "01223" "0323" "01222" "03122" "01222" "01222"
#> [1891] "01132" "0221" "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121"
#> [1901] "013" "0323" "01223" "0311" "01223" "01223" "0332" "01223" "01222" "01222"
#> [1911] "01222" "01222" "01222" "01221" "0323" "01222" "01221" "01132" "01221" "03122"
#> [1921] "0223" "01222" "0323" "0323" "01222" "0311" "01222" "01222" "01222" "0233"
#> [1931] "0323" "01222" "02112" "01222" "0323" "0233" "0333" "01222" "01222" "0323"
#> [1941] "0323" "01222" "0323" "01222" "0332" "0222" "03122" "0323" "01222" "03121"
#> [1951] "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323" "02113" "0323"
#> [1961] "0221" "0323" "0323" "0222" "01222" "0323" "02112" "0331" "0323" "03122"
#> [1971] "01222" "0233" "03122" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112"
#> [1991] "0221" "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231"
#> [2001] "0223" "02113" "02112" "0232" "02112" "0222" "0221" "02121" "0232" "0232"
#> [2011] "02123" "0231" "02121" "0231" "0142" "0221" "0231" "0321" "0223" "02112"
#> [2021] "02122" "0222" "0223" "0221" "0222" "0321" "0223" "02122" "02122" "0223"
#> [2031] "0222" "02122" "0223" "0232" "0221" "02113" "0221" "02112" "0223" "0223"
#> [2041] "0221" "0321" "02112" "0233" "0232" "02113" "02122" "02121" "02121" "0142"
#> [2051] "0221" "02113" "0231" "02113" "02112" "02121" "0223" "02122" "0321" "0223"
#> [2061] "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122" "02122" "02112"
#> [2071] "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112" "02112"
#> [2081] "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231"
#> [2101] "02121" "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112"
#> [2111] "0231" "0223" "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232"
#> [2121] "02121" "02121" "0233" "0232" "0142" "0223" "02121" "0142" "02112" "02112"
#> [2131] "02122" "02121" "02112" "02112" "02112" "02121" "02122" "02121" "0221" "02121"
#> [2141] "0222" "0223" "02122" "0221" "0221" "0222" "0223" "02121" "0223" "02121"
#> [2151] "02112" "02122" "0223" "0223" "02122" "02112" "02121" "0223" "0223" "02112"
#> [2161] "0221" "0223" "0221" "02122" "0223" "0223" "0221" "02121" "0223" "0223"
#> [2171] "0223" "0221" "02121" "0321" "0221" "0221" "0221" "021112" "02122" "02122"
#> [2181] "02122" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221" "0143"
#> [2191] "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "02121" "02121" "02121" "0231"
#> [2211] "0232" "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121"
#> [2221] "0223" "02123" "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121"
#> [2231] "0223" "0223" "0223" "0223" "02121" "0221" "0321" "0222" "0221" "0321"
#> [2241] "0221" "0321" "0223" "0221" "0223" "0223" "0223" "0231" "0231" "0221"
#> [2251] "0222" "0321" "0222" "0221" "0231" "0231" "0221" "0221" "0141" "0321"
#> [2261] "02112" "0221" "0221" "0221" "0223" "0321" "0231" "0221" "0321" "0223"
#> [2271] "0223" "0223" "0142" "0223" "0142" "0222" "0223" "0321" "0221" "0231"
#> [2281] "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321" "0321" "0221"
#> [2291] "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141" "0321"
#> [2301] "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221"
#> [2321] "0142" "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221"
#> [2331] "0223" "0221" "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141"
#> [2341] "0321" "0321" "0221" "02122" "0232" "0223" "0223" "0223" "0221" "0221"
#> [2351] "0321" "0222" "0223" "0223" "0221" "0221" "0321" "02121" "02112" "0221"
#> [2361] "02121" "0221" "02121" "0234" "02121" "02122" "0221" "02112" "02112" "0221"
#> [2371] "0223" "0223" "02121" "0223" "02121" "0223" "0221" "0221" "02123" "02121"
#> [2381] "0232" "0223" "02112" "02122" "0232" "0221" "0223" "0223" "0223" "0231"
#> [2391] "02113" "0223" "0221" "0221" "021112" "02121" "02122" "0223" "0321" "0221"
#> [2401] "0141" "0141" "0141" "02122" "0221" "0231" "021112" "0223" "02122" "0222"
#> [2411] "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223"
#> [2431] "0221" "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141"
#> [2441] "0141" "01234" "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223"
#> [2451] "02122" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "02122" "0231"
#> [2461] "02122" "02122" "02122" "02123" "02122" "02123" "02122" "02122" "0222" "0221"
#> [2471] "0321" "0221" "0221" "0221" "02122" "02122" "0223" "02122" "0223" "0221"
#> [2481] "0223" "02122" "0223" "0223" "02112" "0223" "02122" "02122" "02122" "02112"
#> [2491] "02123" "02122" "02122" "02112" "0223" "02122" "0223" "02122" "02122" "0221"
#> [2501] "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321" "0321" "0221"
#> [2511] "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221" "02122"
#> [2521] "02121" "02112" "0221" "0222" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231"
#> [2541] "0221" "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221"
#> [2551] "0321" "0321" "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121"
#> [2561] "0321" "02122" "0321" "0223" "0221" "0321" "0221" "0221" "0221" "0221"
#> [2571] "0223" "0142" "0141" "0141" "0321" "0321" "0221" "0221" "02112" "02122"
#> [2581] "02122" "0223" "0223" "0221" "0221" "0222" "0221" "0142" "021112" "0232"
#> [2591] "0234" "0232" "02113" "02113" "021111" "02113" "02113" "021111" "0231" "02113"
#> [2601] "021111" "021111" "0232" "02113" "0232" "0231" "0234" "0232" "0323" "0142"
#> [2611] "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231" "0231" "0234"
#> [2621] "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141" "0231"
#> [2631] "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221"
#> [2651] "01221" "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221"
#> [2661] "0221" "0221" "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321"
#> [2671] "0221" "0141" "0321" "0221" "0321" "0221" "0221" "0324" "01231" "0141"
#> [2681] "01231" "0221" "0141" "01231" "0121" "0232" "0232" "02112" "02112" "0321"
#> [2691] "02121" "02121" "0234" "0231" "0143" "0221" "0324" "02121" "0221" "0321"
#> [2701] "0221" "02121" "0141" "0222" "0222" "0321" "0142" "0222" "0141" "0142"
#> [2711] "0222" "0141" "0141" "0231" "0222" "0231" "0141" "0142" "0231" "0141"
#> [2721] "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141" "01231" "0321"
#> [2731] "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141" "0321"
#> [2741] "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "021111" "021111" "021111"
#> [2761] "021111" "021111" "021111" "021111" "0232" "0142" "0142" "0221" "021111" "02113"
#> [2771] "021112" "021112" "021111" "021111" "021111" "021112" "021111" "021111" "021112" "021112"
#> [2781] "021112" "0231" "021111" "021111" "021111" "0232" "021111" "0232" "021111" "0142"
#> [2791] "0142" "0223" "0231" "0231" "021112" "021112" "021112" "021112" "021111" "021111"
#> [2801] "021111" "02113" "0233" "021112" "02113" "021111" "021112" "0232" "021111" "021111"
#> [2811] "021111" "021111" "021112" "021112" "021111" "021112" "0221" "0142" "0142" "0142"
#> [2821] "0221" "02121" "0231" "021112" "021112" "02121" "021112" "02121" "021112" "0223"
#> [2831] "02121" "02121" "021112" "02121" "0231" "0223" "02121" "02121" "0232" "0231"
#> [2841] "021112" "021112" "02112" "02112" "02121" "021111" "02112" "021112" "021112" "02112"
#> [2851] "021112" "021111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221"
#> [2871] "021112" "02121" "02123" "021111" "021112" "02121" "0223" "02121" "0142" "02121"
#> [2881] "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 755))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "01131" "01232" "01232" "01232"
#> [12] "01131" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "01131" "0322" "01232"
#> [23] "01131" "0322" "01232" "0322" "0322" "0322" "01232" "03121" "0322" "01232" "0322"
#> [34] "0322" "0322" "01232" "01232" "01232" "03121" "0311" "01131" "0222" "01131" "0311"
#> [45] "01232" "01232" "0311" "03121" "0322" "01131" "03121" "01131" "01131" "01232" "01232"
#> [56] "02123" "02123" "0143" "01133" "0313" "0322" "01131" "01131" "0222" "0311" "01132"
#> [67] "01232" "01232" "0322" "01232" "0322" "0311" "0322" "01131" "01131" "0143" "0111"
#> [78] "0112" "0222" "01131" "0143" "0322" "01131" "0143" "01133" "0222" "01131" "01131"
#> [89] "01131" "01231" "0322" "0111" "02113" "01131" "01131" "01131" "01131" "01132" "0143"
#> [100] "0313" "01131" "01131" "0111" "01133" "0111" "0322" "0222" "0141" "0142" "0111"
#> [111] "01131" "01131" "01133" "0143" "01132" "0222" "0222" "0322" "01132" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "01231" "0111" "01133" "01133" "01231" "01131"
#> [133] "01133" "0324" "0111" "0222" "01131" "01131" "0322" "0111" "01131" "0111" "01232"
#> [144] "01231" "01231" "0222" "01131" "02123" "01131" "0324" "0313" "0313" "01131" "0313"
#> [155] "0322" "01131" "0313" "0234" "0322" "0322" "0322" "01131" "0313" "0313" "0222"
#> [166] "01131" "0322" "0313" "01131" "01131" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "01131" "0313" "0313" "03121" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "03121" "0222" "0322" "01131" "0313" "03121" "0313" "0322" "03121" "03121"
#> [199] "03121" "03122" "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "03121" "0313" "0313" "0322" "0222" "03121" "01133"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "01131" "02113" "0313"
#> [232] "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313" "0313" "0313"
#> [243] "03121" "01133" "03121" "03121" "03121" "0222" "03121" "0313" "01133" "0313" "03121"
#> [254] "03121" "0313" "0313" "01133" "03121" "0313" "0313" "01133" "0313" "01133" "01133"
#> [265] "01133" "0313" "01133" "01133" "01133" "0313" "01133" "01133" "0313" "0313" "01133"
#> [276] "0313" "0313" "0313" "0313" "0322" "02123" "01133" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "03121" "03121" "03121" "03122" "03121" "03121" "03121" "03121" "03121"
#> [298] "03122" "02113" "03121" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "03122"
#> [309] "0222" "03121" "03121" "0313" "0222" "0313" "0313" "03121" "01133" "0313" "0313"
#> [320] "01133" "0313" "01133" "03121" "0313" "0311" "01133" "0313" "0313" "0313" "0313"
#> [331] "0313" "01133" "01133" "01133" "01132" "0222" "0222" "01132" "0313" "0112" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "01133"
#> [353] "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122" "03121" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "03121" "03121" "03121" "0313" "03122" "03122" "03121"
#> [375] "03121" "03121" "03121" "03122" "03122" "0313" "0111" "0112" "0222" "0311" "0112"
#> [386] "0111" "0112" "0311" "0324" "0311" "0112" "0311" "0112" "0311" "0222" "0311"
#> [397] "0313" "0311" "0112" "0111" "0311" "0112" "0143" "0311" "0112" "0222" "0111"
#> [408] "0311" "0112" "0112" "0311" "0311" "02123" "0112" "0112" "0112" "0111" "01133"
#> [419] "0311" "0111" "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112"
#> [430] "0112" "0112" "0234" "0112" "0234" "0111" "0222" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "03122" "03122" "03121" "0311"
#> [452] "0112" "0311" "0112" "0112" "03121" "0112" "0112" "03122" "03122" "0311" "03122"
#> [463] "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "03122"
#> [474] "03122" "03122" "0311" "0311" "03121" "0311" "0311" "0311" "0311" "0311" "0112"
#> [485] "02123" "03122" "0311" "0311" "0222" "0222" "02123" "03121" "03122" "0222" "03122"
#> [496] "0112" "02123" "02113" "0112" "03122" "02113" "0112" "0311" "03122" "0311" "02113"
#> [507] "0112" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "0112" "0112" "0222"
#> [518] "0311" "03121" "0311" "0112" "0112" "0112" "03122" "03121" "0313" "03121" "0112"
#> [529] "0112" "0222" "02123" "02123" "0112" "0222" "0111" "0111" "0111" "02123" "0111"
#> [540] "0311" "0112" "0222" "0111" "0112" "0222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143" "0311"
#> [562] "01132" "0324" "0324" "01132" "0112" "0111" "0222" "0311" "0112" "0112" "0222"
#> [573] "0324" "0311" "0112" "03121" "0111" "0112" "0112" "0222" "0112" "0112" "0111"
#> [584] "0112" "0311" "0112" "0311" "0112" "0111" "01132" "0111" "0313" "0112" "03122"
#> [595] "0313" "0324" "0112" "0313" "0313" "0111" "0111" "01132" "0111" "0313" "0111"
#> [606] "0112" "0222" "0111" "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0234"
#> [617] "0311" "0311" "0112" "0311" "0311" "0313" "0112" "0311" "0112" "0311" "0311"
#> [628] "0311" "0311" "0311" "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112"
#> [639] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "0222"
#> [650] "0112" "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "03122" "0311" "0311" "0311" "0311" "0222"
#> [672] "0112" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "0112" "0311" "0311"
#> [683] "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311" "0311" "0112" "03122"
#> [694] "0311" "0222" "0311" "03122" "0112" "03122" "0143" "03122" "0112" "0111" "0311"
#> [705] "0311" "0222" "0222" "0112" "0324" "0112" "0324" "02123" "0111" "0112" "0111"
#> [716] "0112" "0111" "0111" "0222" "0311" "0311" "0222" "0234" "0112" "0222" "0311"
#> [727] "0311" "0311" "0112" "0112" "0311" "0112" "0111" "0311" "0112" "0112" "0111"
#> [738] "0111" "0111" "0311" "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03122"
#> [749] "03122" "03122" "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324"
#> [760] "0311" "02123" "0222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "03122" "0112" "0112" "0311" "0112" "0222" "0111" "0112" "02113" "0112" "0311"
#> [782] "0112" "0112" "0111" "0112" "0112" "03122" "0111" "0311" "0311" "0112" "0112"
#> [793] "03122" "0222" "0112" "03122" "0111" "0111" "0234" "0311" "03122" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "0112" "0311" "0112" "0112" "0112" "0324" "0324"
#> [815] "01231" "0143" "0111" "0112" "0111" "02123" "0111" "0111" "0111" "0111" "0111"
#> [826] "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0111" "01231" "0111" "0111"
#> [837] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [848] "0112" "0324" "01231" "01132" "0234" "0324" "0222" "0111" "0143" "0143" "0143"
#> [859] "0324" "0111" "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [870] "0311" "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324" "0111"
#> [892] "0311" "0111" "0111" "0111" "0222" "0222" "0111" "0111" "0111" "0111" "0111"
#> [903] "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111" "0111" "0111" "0111"
#> [914] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [925] "0111" "0111" "0111" "0111" "0222" "0111" "0143" "0111" "0111" "0111" "0222"
#> [936] "0324" "0111" "0222" "0111" "0111" "0111" "0143" "0111" "0111" "0111" "0111"
#> [947] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0311" "01132" "02123" "0143"
#> [958] "0143" "0111" "02123" "0222" "0112" "0111" "02123" "0311" "0112" "0111" "0111"
#> [969] "0111" "0311" "0111" "0111" "0222" "0112" "01231" "0111" "0111" "0111" "0111"
#> [980] "0111" "0324" "0324" "0222" "0222" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111" "0222"
#> [1002] "013" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1013] "0121" "0222" "0121" "0222" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1024] "0121" "0121" "0323" "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1035] "0121" "0311" "0121" "03121" "0121" "02113" "0121" "01132" "0322" "0121" "0121"
#> [1046] "0121" "03121" "0121" "0121" "0121" "01132" "0121" "0121" "0121" "0121" "0121"
#> [1057] "0121" "0121" "01132" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1068] "0121" "0121" "0121" "0121" "0121" "013" "0121" "0322" "0121" "0121" "0121"
#> [1079] "013" "0121" "0121" "0121" "03121" "0121" "0121" "0121" "0121" "0311" "0121"
#> [1090] "0121" "0121" "0121" "01132" "013" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1101] "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0311" "0121"
#> [1112] "0121" "0121" "0121" "0121" "013" "0121" "0313" "0121" "0141" "0121" "0313"
#> [1123] "0121" "0121" "0121" "0121" "0121" "03122" "0121" "0121" "0121" "01132" "01132"
#> [1134] "0121" "0322" "0121" "0121" "0121" "0121" "0121" "0221" "0121" "0121" "01132"
#> [1145] "0322" "01232" "0121" "0111" "0121" "0142" "0121" "0121" "01231" "01232" "0121"
#> [1156] "0121" "0121" "0121" "0121" "0121" "01132" "01232" "01132" "0121" "0121" "0121"
#> [1167] "0121" "0111" "01132" "0111" "01231" "0311" "01132" "0121" "01132" "01132" "0121"
#> [1178] "0121" "0121" "0121" "0223" "0121" "0141" "0121" "0221" "0111" "0121" "0121"
#> [1189] "01132" "0121" "0121" "0121" "0121" "0121" "0121" "0141" "0121" "01231" "013"
#> [1200] "0121" "0121" "0141" "0121" "0121" "0121" "0121" "0121" "0112" "0121" "0121"
#> [1211] "013" "0121" "01232" "0141" "0222" "0121" "0321" "0313" "0121" "0121" "01131"
#> [1222] "0121" "0121" "0221" "0121" "0223" "01232" "0121" "0121" "0141" "0121" "0121"
#> [1233] "0121" "01132" "0121" "01131" "0112" "0313" "0141" "0121" "0333" "0321" "0311"
#> [1244] "0121" "0121" "0121" "01132" "01132" "0121" "0121" "01132" "01234" "0112" "0111"
#> [1255] "0112" "0221" "0221" "0221" "0223" "01223" "0121" "01223" "0121" "01234" "0311"
#> [1266] "0141" "0111" "01132" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "02122" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "01223" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "02122" "02122" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "01234" "01223" "01231" "01234" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "01231" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "0112" "013" "0311" "01231" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "01231" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "01231" "013" "0111" "0111" "01131" "013" "013" "0333" "01231" "0313"
#> [1475] "0333" "0313" "0112" "02121" "013" "0221" "01232" "013" "013" "0111" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "0111" "0231" "0141"
#> [1497] "013" "0111" "013" "0233" "01231" "0141" "013" "0111" "01231" "0321" "013"
#> [1508] "0222" "013" "0223" "01231" "013" "01231" "013" "013" "01231" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "01223" "0223" "0112" "0111" "013" "013"
#> [1541] "01232" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "0111" "013" "013" "013" "0142" "02121" "0233" "013" "01231" "01231"
#> [1563] "0143" "03121" "0223" "01133" "013" "0333" "013" "01231" "013" "0223" "02121"
#> [1574] "0142" "02121" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "02121" "0233" "013" "02113" "013" "0332" "013" "02123" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "01132" "02122" "01234" "013"
#> [1629] "01234" "013" "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221"
#> [1640] "01234" "01234" "013" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "01133" "01234" "01234"
#> [1662] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01231"
#> [1673] "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233" "01233" "01233" "01221"
#> [1684] "0141" "01233" "0121" "01132" "0121" "01232" "01223" "01233" "01233" "0322" "0121"
#> [1695] "01233" "01233" "01233" "0112" "01233" "01233" "0112" "01233" "0311" "01233" "01233"
#> [1706] "01221" "0323" "01223" "01233" "01233" "013" "0311" "01233" "01233" "01223" "013"
#> [1717] "01233" "01233" "0323" "0323" "013" "01233" "0141" "01233" "01233" "01233" "01233"
#> [1728] "0313" "01233" "0311" "0311" "01233" "0121" "02121" "01231" "01133" "01223" "01133"
#> [1739] "0112" "0111" "01221" "01223" "013" "01221" "013" "01221" "01222" "01223" "0323"
#> [1750] "01222" "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "03122"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "013" "0221" "01221"
#> [1772] "01221" "01221" "01221" "01221" "01221" "01132" "01221" "01221" "01221" "0322" "01132"
#> [1783] "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323" "01222" "0313" "0313"
#> [1794] "0323" "0223" "01132" "01221" "0313" "0223" "01221" "01221" "01221" "01222" "0323"
#> [1805] "01221" "01221" "0233" "02121" "0223" "0311" "0221" "01221" "01221" "01131" "01223"
#> [1816] "01221" "01221" "01221" "01221" "01221" "0313" "01221" "01221" "01221" "01221" "01221"
#> [1827] "01221" "01221" "01222" "0223" "01221" "01221" "0323" "01221" "01222" "02122" "0223"
#> [1838] "01221" "0111" "01221" "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221"
#> [1849] "01222" "01221" "01222" "0323" "01223" "01234" "0111" "01234" "01223" "01132" "0322"
#> [1860] "01233" "03122" "01233" "01233" "0332" "0223" "03122" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "0121" "02112"
#> [1882] "01223" "01223" "01223" "01223" "0323" "01222" "03122" "01222" "01222" "01132" "0221"
#> [1893] "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121" "013" "0323" "01223"
#> [1904] "0311" "01223" "01223" "0332" "01223" "01222" "01222" "01222" "01222" "01222" "01221"
#> [1915] "0323" "01222" "01221" "01132" "01221" "03122" "0223" "01222" "0323" "0323" "01222"
#> [1926] "0311" "01222" "01222" "01222" "0233" "0323" "01222" "02112" "01222" "0323" "0233"
#> [1937] "0333" "01222" "01222" "0323" "0323" "01222" "0323" "01222" "0332" "0222" "03122"
#> [1948] "0323" "01222" "03121" "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "01222" "0323" "02112" "0331" "0323"
#> [1970] "03122" "01222" "0233" "03122" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112" "0221"
#> [1992] "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "02121" "0232" "0232" "02123" "0231" "02121"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "02122" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "02122" "02122" "0223" "0222" "02122" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "02122" "02121" "02121" "0142" "0221" "02113" "0231" "02113" "02112" "02121" "0223"
#> [2058] "02122" "0321" "0223" "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122"
#> [2069] "02122" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231" "02121"
#> [2102] "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232" "02121" "02121" "0233"
#> [2124] "0232" "0142" "0223" "02121" "0142" "02112" "02112" "02122" "02121" "02112" "02112"
#> [2135] "02112" "02121" "02122" "02121" "0221" "02121" "0222" "0223" "02122" "0221" "0221"
#> [2146] "0222" "0223" "02121" "0223" "02121" "02112" "02122" "0223" "0223" "02122" "02112"
#> [2157] "02121" "0223" "0223" "02112" "0221" "0223" "0221" "02122" "0223" "0223" "0221"
#> [2168] "02121" "0223" "0223" "0223" "0221" "02121" "0321" "0221" "0221" "0221" "02111"
#> [2179] "02122" "02122" "02122" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "02121" "02121" "02121" "0231" "0232"
#> [2212] "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121" "0223" "02123"
#> [2223] "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121" "0223" "0223" "0223"
#> [2234] "0223" "02121" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "02122" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "02121" "02112" "0221" "02121" "0221" "02121" "0234" "02121"
#> [2366] "02122" "0221" "02112" "02112" "0221" "0223" "0223" "02121" "0223" "02121" "0223"
#> [2377] "0221" "0221" "02123" "02121" "0232" "0223" "02112" "02122" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "02121" "02122" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "02122" "0221" "0231" "02111" "0223" "02122"
#> [2410] "0222" "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "01234"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223" "02122" "02122" "02122"
#> [2454] "02112" "02122" "02122" "02112" "02122" "02122" "0231" "02122" "02122" "02122" "02123"
#> [2465] "02122" "02123" "02122" "02122" "0222" "0221" "0321" "0221" "0221" "0221" "02122"
#> [2476] "02122" "0223" "02122" "0223" "0221" "0223" "02122" "0223" "0223" "02112" "0223"
#> [2487] "02122" "02122" "02122" "02112" "02123" "02122" "02122" "02112" "0223" "02122" "0223"
#> [2498] "02122" "02122" "0221" "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221"
#> [2520] "02122" "02121" "02112" "0221" "0222" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121" "0321" "02122" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "02122" "02122" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221" "01221"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "01231" "0141" "01231" "0221" "0141" "01231"
#> [2685] "0121" "0232" "0232" "02112" "02112" "0321" "02121" "02121" "0234" "0231" "0143"
#> [2696] "0221" "0324" "02121" "0221" "0321" "0221" "02121" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "01231" "0321" "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "02121" "0231" "02111" "02111" "02121" "02111"
#> [2828] "02121" "02111" "0223" "02121" "02121" "02111" "02121" "0231" "0223" "02121" "02121"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "02121" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221" "02111"
#> [2872] "02121" "02123" "02111" "02111" "02121" "0223" "02121" "0142" "02121" "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 890))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "0113" "01232" "01232" "01232"
#> [12] "0113" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "0113" "0322" "01232"
#> [23] "0113" "0322" "01232" "0322" "0322" "0322" "01232" "03121" "0322" "01232" "0322"
#> [34] "0322" "0322" "01232" "01232" "01232" "03121" "0311" "0113" "0222" "0113" "0311"
#> [45] "01232" "01232" "0311" "03121" "0322" "0113" "03121" "0113" "0113" "01232" "01232"
#> [56] "02123" "02123" "0143" "0113" "0313" "0322" "0113" "0113" "0222" "0311" "0113"
#> [67] "01232" "01232" "0322" "01232" "0322" "0311" "0322" "0113" "0113" "0143" "0111"
#> [78] "0112" "0222" "0113" "0143" "0322" "0113" "0143" "0113" "0222" "0113" "0113"
#> [89] "0113" "01231" "0322" "0111" "02113" "0113" "0113" "0113" "0113" "0113" "0143"
#> [100] "0313" "0113" "0113" "0111" "0113" "0111" "0322" "0222" "0141" "0142" "0111"
#> [111] "0113" "0113" "0113" "0143" "0113" "0222" "0222" "0322" "0113" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "01231" "0111" "0113" "0113" "01231" "0113"
#> [133] "0113" "0324" "0111" "0222" "0113" "0113" "0322" "0111" "0113" "0111" "01232"
#> [144] "01231" "01231" "0222" "0113" "02123" "0113" "0324" "0313" "0313" "0113" "0313"
#> [155] "0322" "0113" "0313" "0234" "0322" "0322" "0322" "0113" "0313" "0313" "0222"
#> [166] "0113" "0322" "0313" "0113" "0113" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "0113" "0313" "0313" "03121" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "03121" "0222" "0322" "0113" "0313" "03121" "0313" "0322" "03121" "03121"
#> [199] "03121" "03122" "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "03121" "0313" "0313" "0322" "0222" "03121" "0113"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "0113" "02113" "0313"
#> [232] "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313" "0313" "0313"
#> [243] "03121" "0113" "03121" "03121" "03121" "0222" "03121" "0313" "0113" "0313" "03121"
#> [254] "03121" "0313" "0313" "0113" "03121" "0313" "0313" "0113" "0313" "0113" "0113"
#> [265] "0113" "0313" "0113" "0113" "0113" "0313" "0113" "0113" "0313" "0313" "0113"
#> [276] "0313" "0313" "0313" "0313" "0322" "02123" "0113" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "03121" "03121" "03121" "03122" "03121" "03121" "03121" "03121" "03121"
#> [298] "03122" "02113" "03121" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "03122"
#> [309] "0222" "03121" "03121" "0313" "0222" "0313" "0313" "03121" "0113" "0313" "0313"
#> [320] "0113" "0313" "0113" "03121" "0313" "0311" "0113" "0313" "0313" "0313" "0313"
#> [331] "0313" "0113" "0113" "0113" "0113" "0222" "0222" "0113" "0313" "0112" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "0113"
#> [353] "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122" "03121" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "03121" "03121" "03121" "0313" "03122" "03122" "03121"
#> [375] "03121" "03121" "03121" "03122" "03122" "0313" "0111" "0112" "0222" "0311" "0112"
#> [386] "0111" "0112" "0311" "0324" "0311" "0112" "0311" "0112" "0311" "0222" "0311"
#> [397] "0313" "0311" "0112" "0111" "0311" "0112" "0143" "0311" "0112" "0222" "0111"
#> [408] "0311" "0112" "0112" "0311" "0311" "02123" "0112" "0112" "0112" "0111" "0113"
#> [419] "0311" "0111" "0111" "0111" "0112" "0313" "0234" "0112" "0111" "0112" "0112"
#> [430] "0112" "0112" "0234" "0112" "0234" "0111" "0222" "0112" "02123" "0112" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "0112" "0112" "03122" "03122" "03121" "0311"
#> [452] "0112" "0311" "0112" "0112" "03121" "0112" "0112" "03122" "03122" "0311" "03122"
#> [463] "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "03122"
#> [474] "03122" "03122" "0311" "0311" "03121" "0311" "0311" "0311" "0311" "0311" "0112"
#> [485] "02123" "03122" "0311" "0311" "0222" "0222" "02123" "03121" "03122" "0222" "03122"
#> [496] "0112" "02123" "02113" "0112" "03122" "02113" "0112" "0311" "03122" "0311" "02113"
#> [507] "0112" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "0112" "0112" "0222"
#> [518] "0311" "03121" "0311" "0112" "0112" "0112" "03122" "03121" "0313" "03121" "0112"
#> [529] "0112" "0222" "02123" "02123" "0112" "0222" "0111" "0111" "0111" "02123" "0111"
#> [540] "0311" "0112" "0222" "0111" "0112" "0222" "0111" "0111" "0112" "0311" "0111"
#> [551] "0111" "0112" "0112" "0112" "0111" "0143" "0112" "0311" "0311" "0143" "0311"
#> [562] "0113" "0324" "0324" "0113" "0112" "0111" "0222" "0311" "0112" "0112" "0222"
#> [573] "0324" "0311" "0112" "03121" "0111" "0112" "0112" "0222" "0112" "0112" "0111"
#> [584] "0112" "0311" "0112" "0311" "0112" "0111" "0113" "0111" "0313" "0112" "03122"
#> [595] "0313" "0324" "0112" "0313" "0313" "0111" "0111" "0113" "0111" "0313" "0111"
#> [606] "0112" "0222" "0111" "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0234"
#> [617] "0311" "0311" "0112" "0311" "0311" "0313" "0112" "0311" "0112" "0311" "0311"
#> [628] "0311" "0311" "0311" "0112" "0112" "0313" "0112" "0311" "02113" "0311" "0112"
#> [639] "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03121" "0112" "0112" "0222"
#> [650] "0112" "0112" "0112" "0112" "0112" "0311" "0112" "0112" "0112" "0311" "0311"
#> [661] "0311" "0112" "0234" "0112" "0112" "03122" "0311" "0311" "0311" "0311" "0222"
#> [672] "0112" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "0112" "0311" "0311"
#> [683] "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311" "0311" "0112" "03122"
#> [694] "0311" "0222" "0311" "03122" "0112" "03122" "0143" "03122" "0112" "0111" "0311"
#> [705] "0311" "0222" "0222" "0112" "0324" "0112" "0324" "02123" "0111" "0112" "0111"
#> [716] "0112" "0111" "0111" "0222" "0311" "0311" "0222" "0234" "0112" "0222" "0311"
#> [727] "0311" "0311" "0112" "0112" "0311" "0112" "0111" "0311" "0112" "0112" "0111"
#> [738] "0111" "0111" "0311" "0112" "0112" "0112" "0311" "0311" "0112" "0311" "03122"
#> [749] "03122" "03122" "0311" "0112" "0311" "0112" "0112" "0112" "0311" "0112" "0324"
#> [760] "0311" "02123" "0222" "0112" "0112" "0311" "0112" "0112" "0112" "0111" "0111"
#> [771] "03122" "0112" "0112" "0311" "0112" "0222" "0111" "0112" "02113" "0112" "0311"
#> [782] "0112" "0112" "0111" "0112" "0112" "03122" "0111" "0311" "0311" "0112" "0112"
#> [793] "03122" "0222" "0112" "03122" "0111" "0111" "0234" "0311" "03122" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "0112" "0311" "0112" "0112" "0112" "0324" "0324"
#> [815] "01231" "0143" "0111" "0112" "0111" "02123" "0111" "0111" "0111" "0111" "0111"
#> [826] "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0111" "01231" "0111" "0111"
#> [837] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111"
#> [848] "0112" "0324" "01231" "0113" "0234" "0324" "0222" "0111" "0143" "0143" "0143"
#> [859] "0324" "0111" "0111" "0324" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [870] "0311" "0112" "0111" "0112" "0111" "0111" "0111" "0143" "0111" "0111" "0111"
#> [881] "0111" "0111" "0111" "0111" "01231" "02123" "0111" "0324" "0111" "0324" "0111"
#> [892] "0311" "0111" "0111" "0111" "0222" "0222" "0111" "0111" "0111" "0111" "0111"
#> [903] "0311" "0111" "0111" "0112" "0112" "0112" "0112" "0111" "0111" "0111" "0111"
#> [914] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0112"
#> [925] "0111" "0111" "0111" "0111" "0222" "0111" "0143" "0111" "0111" "0111" "0222"
#> [936] "0324" "0111" "0222" "0111" "0111" "0111" "0143" "0111" "0111" "0111" "0111"
#> [947] "0111" "0111" "0111" "0111" "0111" "0111" "0111" "0311" "0113" "02123" "0143"
#> [958] "0143" "0111" "02123" "0222" "0112" "0111" "02123" "0311" "0112" "0111" "0111"
#> [969] "0111" "0311" "0111" "0111" "0222" "0112" "01231" "0111" "0111" "0111" "0111"
#> [980] "0111" "0324" "0324" "0222" "0222" "0311" "0112" "0111" "0112" "0112" "0311"
#> [991] "0311" "0112" "0112" "0111" "0311" "0111" "0111" "0324" "0324" "0111" "0222"
#> [1002] "013" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1013] "0121" "0222" "0121" "0222" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1024] "0121" "0121" "0323" "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1035] "0121" "0311" "0121" "03121" "0121" "02113" "0121" "0113" "0322" "0121" "0121"
#> [1046] "0121" "03121" "0121" "0121" "0121" "0113" "0121" "0121" "0121" "0121" "0121"
#> [1057] "0121" "0121" "0113" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1068] "0121" "0121" "0121" "0121" "0121" "013" "0121" "0322" "0121" "0121" "0121"
#> [1079] "013" "0121" "0121" "0121" "03121" "0121" "0121" "0121" "0121" "0311" "0121"
#> [1090] "0121" "0121" "0121" "0113" "013" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1101] "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0311" "0121"
#> [1112] "0121" "0121" "0121" "0121" "013" "0121" "0313" "0121" "0141" "0121" "0313"
#> [1123] "0121" "0121" "0121" "0121" "0121" "03122" "0121" "0121" "0121" "0113" "0113"
#> [1134] "0121" "0322" "0121" "0121" "0121" "0121" "0121" "0221" "0121" "0121" "0113"
#> [1145] "0322" "01232" "0121" "0111" "0121" "0142" "0121" "0121" "01231" "01232" "0121"
#> [1156] "0121" "0121" "0121" "0121" "0121" "0113" "01232" "0113" "0121" "0121" "0121"
#> [1167] "0121" "0111" "0113" "0111" "01231" "0311" "0113" "0121" "0113" "0113" "0121"
#> [1178] "0121" "0121" "0121" "0223" "0121" "0141" "0121" "0221" "0111" "0121" "0121"
#> [1189] "0113" "0121" "0121" "0121" "0121" "0121" "0121" "0141" "0121" "01231" "013"
#> [1200] "0121" "0121" "0141" "0121" "0121" "0121" "0121" "0121" "0112" "0121" "0121"
#> [1211] "013" "0121" "01232" "0141" "0222" "0121" "0321" "0313" "0121" "0121" "0113"
#> [1222] "0121" "0121" "0221" "0121" "0223" "01232" "0121" "0121" "0141" "0121" "0121"
#> [1233] "0121" "0113" "0121" "0113" "0112" "0313" "0141" "0121" "0333" "0321" "0311"
#> [1244] "0121" "0121" "0121" "0113" "0113" "0121" "0121" "0113" "01234" "0112" "0111"
#> [1255] "0112" "0221" "0221" "0221" "0223" "01223" "0121" "01223" "0121" "01234" "0311"
#> [1266] "0141" "0111" "0113" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "02122" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "01223" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "02122" "02122" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "01234" "01223" "01231" "01234" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "01231" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "0112" "013" "0311" "01231" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "01231" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "01231" "013" "0111" "0111" "0113" "013" "013" "0333" "01231" "0313"
#> [1475] "0333" "0313" "0112" "02121" "013" "0221" "01232" "013" "013" "0111" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "0111" "0231" "0141"
#> [1497] "013" "0111" "013" "0233" "01231" "0141" "013" "0111" "01231" "0321" "013"
#> [1508] "0222" "013" "0223" "01231" "013" "01231" "013" "013" "01231" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "01223" "0223" "0112" "0111" "013" "013"
#> [1541] "01232" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "0111" "013" "013" "013" "0142" "02121" "0233" "013" "01231" "01231"
#> [1563] "0143" "03121" "0223" "0113" "013" "0333" "013" "01231" "013" "0223" "02121"
#> [1574] "0142" "02121" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "02121" "0233" "013" "02113" "013" "0332" "013" "02123" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "0113" "02122" "01234" "013"
#> [1629] "01234" "013" "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221"
#> [1640] "01234" "01234" "013" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "0111" "01231" "0111" "0113" "01234" "01234"
#> [1662] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01231"
#> [1673] "01233" "01233" "01233" "0231" "01233" "0112" "0112" "0233" "01233" "01233" "01221"
#> [1684] "0141" "01233" "0121" "0113" "0121" "01232" "01223" "01233" "01233" "0322" "0121"
#> [1695] "01233" "01233" "01233" "0112" "01233" "01233" "0112" "01233" "0311" "01233" "01233"
#> [1706] "01221" "0323" "01223" "01233" "01233" "013" "0311" "01233" "01233" "01223" "013"
#> [1717] "01233" "01233" "0323" "0323" "013" "01233" "0141" "01233" "01233" "01233" "01233"
#> [1728] "0313" "01233" "0311" "0311" "01233" "0121" "02121" "01231" "0113" "01223" "0113"
#> [1739] "0112" "0111" "01221" "01223" "013" "01221" "013" "01221" "01222" "01223" "0323"
#> [1750] "01222" "03121" "01223" "0221" "01221" "0221" "01221" "0111" "01221" "0142" "03122"
#> [1761] "0223" "01221" "0112" "01223" "0111" "0221" "0311" "0111" "013" "0221" "01221"
#> [1772] "01221" "01221" "01221" "01221" "01221" "0113" "01221" "01221" "01221" "0322" "0113"
#> [1783] "01221" "01221" "0112" "01221" "0313" "0111" "01221" "0323" "01222" "0313" "0313"
#> [1794] "0323" "0223" "0113" "01221" "0313" "0223" "01221" "01221" "01221" "01222" "0323"
#> [1805] "01221" "01221" "0233" "02121" "0223" "0311" "0221" "01221" "01221" "0113" "01223"
#> [1816] "01221" "01221" "01221" "01221" "01221" "0313" "01221" "01221" "01221" "01221" "01221"
#> [1827] "01221" "01221" "01222" "0223" "01221" "01221" "0323" "01221" "01222" "02122" "0223"
#> [1838] "01221" "0111" "01221" "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221"
#> [1849] "01222" "01221" "01222" "0323" "01223" "01234" "0111" "01234" "01223" "0113" "0322"
#> [1860] "01233" "03122" "01233" "01233" "0332" "0223" "03122" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "0121" "02112"
#> [1882] "01223" "01223" "01223" "01223" "0323" "01222" "03122" "01222" "01222" "0113" "0221"
#> [1893] "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121" "013" "0323" "01223"
#> [1904] "0311" "01223" "01223" "0332" "01223" "01222" "01222" "01222" "01222" "01222" "01221"
#> [1915] "0323" "01222" "01221" "0113" "01221" "03122" "0223" "01222" "0323" "0323" "01222"
#> [1926] "0311" "01222" "01222" "01222" "0233" "0323" "01222" "02112" "01222" "0323" "0233"
#> [1937] "0333" "01222" "01222" "0323" "0323" "01222" "0323" "01222" "0332" "0222" "03122"
#> [1948] "0323" "01222" "03121" "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "01222" "0323" "02112" "0331" "0323"
#> [1970] "03122" "01222" "0233" "03122" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112" "0221"
#> [1992] "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "02121" "0232" "0232" "02123" "0231" "02121"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "02122" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "02122" "02122" "0223" "0222" "02122" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "02122" "02121" "02121" "0142" "0221" "02113" "0231" "02113" "02112" "02121" "0223"
#> [2058] "02122" "0321" "0223" "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122"
#> [2069] "02122" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231" "02121"
#> [2102] "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232" "02121" "02121" "0233"
#> [2124] "0232" "0142" "0223" "02121" "0142" "02112" "02112" "02122" "02121" "02112" "02112"
#> [2135] "02112" "02121" "02122" "02121" "0221" "02121" "0222" "0223" "02122" "0221" "0221"
#> [2146] "0222" "0223" "02121" "0223" "02121" "02112" "02122" "0223" "0223" "02122" "02112"
#> [2157] "02121" "0223" "0223" "02112" "0221" "0223" "0221" "02122" "0223" "0223" "0221"
#> [2168] "02121" "0223" "0223" "0223" "0221" "02121" "0321" "0221" "0221" "0221" "02111"
#> [2179] "02122" "02122" "02122" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "02121" "02121" "02121" "0231" "0232"
#> [2212] "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121" "0223" "02123"
#> [2223] "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121" "0223" "0223" "0223"
#> [2234] "0223" "02121" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "02122" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "02121" "02112" "0221" "02121" "0221" "02121" "0234" "02121"
#> [2366] "02122" "0221" "02112" "02112" "0221" "0223" "0223" "02121" "0223" "02121" "0223"
#> [2377] "0221" "0221" "02123" "02121" "0232" "0223" "02112" "02122" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "02121" "02122" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "02122" "0221" "0231" "02111" "0223" "02122"
#> [2410] "0222" "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "01234"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223" "02122" "02122" "02122"
#> [2454] "02112" "02122" "02122" "02112" "02122" "02122" "0231" "02122" "02122" "02122" "02123"
#> [2465] "02122" "02123" "02122" "02122" "0222" "0221" "0321" "0221" "0221" "0221" "02122"
#> [2476] "02122" "0223" "02122" "0223" "0221" "0223" "02122" "0223" "0223" "02112" "0223"
#> [2487] "02122" "02122" "02122" "02112" "02123" "02122" "02122" "02112" "0223" "02122" "0223"
#> [2498] "02122" "02122" "0221" "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221"
#> [2520] "02122" "02121" "02112" "0221" "0222" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121" "0321" "02122" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "02122" "02122" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221" "01221"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "01231" "0141" "01231" "0221" "0141" "01231"
#> [2685] "0121" "0232" "0232" "02112" "02112" "0321" "02121" "02121" "0234" "0231" "0143"
#> [2696] "0221" "0324" "02121" "0221" "0321" "0221" "02121" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "01231" "0321" "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "02121" "0231" "02111" "02111" "02121" "02111"
#> [2828] "02121" "02111" "0223" "02121" "02121" "02111" "02121" "0231" "0223" "02121" "02121"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "02121" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221" "02111"
#> [2872] "02121" "02123" "02111" "02111" "02121" "0223" "02121" "0142" "02121" "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 960))
#> [1] "01232" "01232" "0231" "0322" "01232" "01232" "0322" "011" "01232" "01232" "01232"
#> [12] "011" "0322" "01232" "01232" "01232" "01232" "01232" "0313" "011" "0322" "01232"
#> [23] "011" "0322" "01232" "0322" "0322" "0322" "01232" "03121" "0322" "01232" "0322"
#> [34] "0322" "0322" "01232" "01232" "01232" "03121" "0311" "011" "0222" "011" "0311"
#> [45] "01232" "01232" "0311" "03121" "0322" "011" "03121" "011" "011" "01232" "01232"
#> [56] "02123" "02123" "0143" "011" "0313" "0322" "011" "011" "0222" "0311" "011"
#> [67] "01232" "01232" "0322" "01232" "0322" "0311" "0322" "011" "011" "0143" "011"
#> [78] "011" "0222" "011" "0143" "0322" "011" "0143" "011" "0222" "011" "011"
#> [89] "011" "01231" "0322" "011" "02113" "011" "011" "011" "011" "011" "0143"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "0222" "0141" "0142" "011"
#> [111] "011" "011" "011" "0143" "011" "0222" "0222" "0322" "011" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "01231" "011" "011" "011" "01231" "011"
#> [133] "011" "0324" "011" "0222" "011" "011" "0322" "011" "011" "011" "01232"
#> [144] "01231" "01231" "0222" "011" "02123" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "0222"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "011" "0313" "0313" "03121" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "03121" "0222" "0322" "011" "0313" "03121" "0313" "0322" "03121" "03121"
#> [199] "03121" "03122" "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "03121" "0313" "0313" "0322" "0222" "03121" "011"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "011" "02113" "0313"
#> [232] "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313" "0313" "0313"
#> [243] "03121" "011" "03121" "03121" "03121" "0222" "03121" "0313" "011" "0313" "03121"
#> [254] "03121" "0313" "0313" "011" "03121" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "02123" "011" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "03121" "03121" "03121" "03122" "03121" "03121" "03121" "03121" "03121"
#> [298] "03122" "02113" "03121" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "03122"
#> [309] "0222" "03121" "03121" "0313" "0222" "0313" "0313" "03121" "011" "0313" "0313"
#> [320] "011" "0313" "011" "03121" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "0222" "0222" "011" "0313" "011" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122" "03121" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "03121" "03121" "03121" "0313" "03122" "03122" "03121"
#> [375] "03121" "03121" "03121" "03122" "03122" "0313" "011" "011" "0222" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "0222" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "0143" "0311" "011" "0222" "011"
#> [408] "0311" "011" "011" "0311" "0311" "02123" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "0222" "011" "02123" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "03122" "03122" "03121" "0311"
#> [452] "011" "0311" "011" "011" "03121" "011" "011" "03122" "03122" "0311" "03122"
#> [463] "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "03122"
#> [474] "03122" "03122" "0311" "0311" "03121" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "02123" "03122" "0311" "0311" "0222" "0222" "02123" "03121" "03122" "0222" "03122"
#> [496] "011" "02123" "02113" "011" "03122" "02113" "011" "0311" "03122" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "011" "011" "0222"
#> [518] "0311" "03121" "0311" "011" "011" "011" "03122" "03121" "0313" "03121" "011"
#> [529] "011" "0222" "02123" "02123" "011" "0222" "011" "011" "011" "02123" "011"
#> [540] "0311" "011" "0222" "011" "011" "0222" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "0143" "011" "0311" "0311" "0143" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "0222" "0311" "011" "011" "0222"
#> [573] "0324" "0311" "011" "03121" "011" "011" "011" "0222" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "03122"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "0222" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "03121" "011" "011" "0222"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "03122" "0311" "0311" "0311" "0311" "0222"
#> [672] "011" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "011" "0311" "0311"
#> [683] "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311" "0311" "011" "03122"
#> [694] "0311" "0222" "0311" "03122" "011" "03122" "0143" "03122" "011" "011" "0311"
#> [705] "0311" "0222" "0222" "011" "0324" "011" "0324" "02123" "011" "011" "011"
#> [716] "011" "011" "011" "0222" "0311" "0311" "0222" "0234" "011" "0222" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "03122"
#> [749] "03122" "03122" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "02123" "0222" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "03122" "011" "011" "0311" "011" "0222" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "03122" "011" "0311" "0311" "011" "011"
#> [793] "03122" "0222" "011" "03122" "011" "011" "0234" "0311" "03122" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "01231" "0143" "011" "011" "011" "02123" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "01231" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "01231" "011" "0234" "0324" "0222" "011" "0143" "0143" "0143"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "0143" "011" "011" "011"
#> [881] "011" "011" "011" "011" "01231" "02123" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "0222" "0222" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "0222" "011" "0143" "011" "011" "011" "0222"
#> [936] "0324" "011" "0222" "011" "011" "011" "0143" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "02123" "0143"
#> [958] "0143" "011" "02123" "0222" "011" "011" "02123" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "0222" "011" "01231" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "0222" "0222" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "0222"
#> [1002] "013" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1013] "0121" "0222" "0121" "0222" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1024] "0121" "0121" "0323" "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1035] "0121" "0311" "0121" "03121" "0121" "02113" "0121" "011" "0322" "0121" "0121"
#> [1046] "0121" "03121" "0121" "0121" "0121" "011" "0121" "0121" "0121" "0121" "0121"
#> [1057] "0121" "0121" "011" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1068] "0121" "0121" "0121" "0121" "0121" "013" "0121" "0322" "0121" "0121" "0121"
#> [1079] "013" "0121" "0121" "0121" "03121" "0121" "0121" "0121" "0121" "0311" "0121"
#> [1090] "0121" "0121" "0121" "011" "013" "0121" "0121" "0121" "0121" "0121" "0121"
#> [1101] "0142" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0311" "0121"
#> [1112] "0121" "0121" "0121" "0121" "013" "0121" "0313" "0121" "0141" "0121" "0313"
#> [1123] "0121" "0121" "0121" "0121" "0121" "03122" "0121" "0121" "0121" "011" "011"
#> [1134] "0121" "0322" "0121" "0121" "0121" "0121" "0121" "0221" "0121" "0121" "011"
#> [1145] "0322" "01232" "0121" "011" "0121" "0142" "0121" "0121" "01231" "01232" "0121"
#> [1156] "0121" "0121" "0121" "0121" "0121" "011" "01232" "011" "0121" "0121" "0121"
#> [1167] "0121" "011" "011" "011" "01231" "0311" "011" "0121" "011" "011" "0121"
#> [1178] "0121" "0121" "0121" "0223" "0121" "0141" "0121" "0221" "011" "0121" "0121"
#> [1189] "011" "0121" "0121" "0121" "0121" "0121" "0121" "0141" "0121" "01231" "013"
#> [1200] "0121" "0121" "0141" "0121" "0121" "0121" "0121" "0121" "011" "0121" "0121"
#> [1211] "013" "0121" "01232" "0141" "0222" "0121" "0321" "0313" "0121" "0121" "011"
#> [1222] "0121" "0121" "0221" "0121" "0223" "01232" "0121" "0121" "0141" "0121" "0121"
#> [1233] "0121" "011" "0121" "011" "011" "0313" "0141" "0121" "0333" "0321" "0311"
#> [1244] "0121" "0121" "0121" "011" "011" "0121" "0121" "011" "01234" "011" "011"
#> [1255] "011" "0221" "0221" "0221" "0223" "01223" "0121" "01223" "0121" "01234" "0311"
#> [1266] "0141" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "02122" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "01223" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "02122" "02122" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "01234" "01223" "01231" "01234" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "01231" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "01231" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "01231" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "01231" "013" "011" "011" "011" "013" "013" "0333" "01231" "0313"
#> [1475] "0333" "0313" "011" "02121" "013" "0221" "01232" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "011" "0231" "0141"
#> [1497] "013" "011" "013" "0233" "01231" "0141" "013" "011" "01231" "0321" "013"
#> [1508] "0222" "013" "0223" "01231" "013" "01231" "013" "013" "01231" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "01223" "0223" "011" "011" "013" "013"
#> [1541] "01232" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "0142" "02121" "0233" "013" "01231" "01231"
#> [1563] "0143" "03121" "0223" "011" "013" "0333" "013" "01231" "013" "0223" "02121"
#> [1574] "0142" "02121" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "02121" "0233" "013" "02113" "013" "0332" "013" "02123" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "011" "02122" "01234" "013"
#> [1629] "01234" "013" "0141" "01234" "0323" "01234" "02122" "01234" "01234" "01234" "0221"
#> [1640] "01234" "01234" "013" "01234" "0233" "0141" "01234" "0141" "01234" "01234" "01234"
#> [1651] "0142" "01234" "01234" "0321" "01234" "011" "01231" "011" "011" "01234" "01234"
#> [1662] "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01234" "01231"
#> [1673] "01233" "01233" "01233" "0231" "01233" "011" "011" "0233" "01233" "01233" "01221"
#> [1684] "0141" "01233" "0121" "011" "0121" "01232" "01223" "01233" "01233" "0322" "0121"
#> [1695] "01233" "01233" "01233" "011" "01233" "01233" "011" "01233" "0311" "01233" "01233"
#> [1706] "01221" "0323" "01223" "01233" "01233" "013" "0311" "01233" "01233" "01223" "013"
#> [1717] "01233" "01233" "0323" "0323" "013" "01233" "0141" "01233" "01233" "01233" "01233"
#> [1728] "0313" "01233" "0311" "0311" "01233" "0121" "02121" "01231" "011" "01223" "011"
#> [1739] "011" "011" "01221" "01223" "013" "01221" "013" "01221" "01222" "01223" "0323"
#> [1750] "01222" "03121" "01223" "0221" "01221" "0221" "01221" "011" "01221" "0142" "03122"
#> [1761] "0223" "01221" "011" "01223" "011" "0221" "0311" "011" "013" "0221" "01221"
#> [1772] "01221" "01221" "01221" "01221" "01221" "011" "01221" "01221" "01221" "0322" "011"
#> [1783] "01221" "01221" "011" "01221" "0313" "011" "01221" "0323" "01222" "0313" "0313"
#> [1794] "0323" "0223" "011" "01221" "0313" "0223" "01221" "01221" "01221" "01222" "0323"
#> [1805] "01221" "01221" "0233" "02121" "0223" "0311" "0221" "01221" "01221" "011" "01223"
#> [1816] "01221" "01221" "01221" "01221" "01221" "0313" "01221" "01221" "01221" "01221" "01221"
#> [1827] "01221" "01221" "01222" "0223" "01221" "01221" "0323" "01221" "01222" "02122" "0223"
#> [1838] "01221" "011" "01221" "01222" "01222" "02121" "01221" "01221" "0143" "01221" "01221"
#> [1849] "01222" "01221" "01222" "0323" "01223" "01234" "011" "01234" "01223" "011" "0322"
#> [1860] "01233" "03122" "01233" "01233" "0332" "0223" "03122" "0321" "0323" "02122" "01221"
#> [1871] "01221" "0323" "0323" "01221" "01222" "01223" "0231" "01221" "01223" "0121" "02112"
#> [1882] "01223" "01223" "01223" "01223" "0323" "01222" "03122" "01222" "01222" "011" "0221"
#> [1893] "01221" "01221" "01222" "0311" "01221" "01222" "01221" "03121" "013" "0323" "01223"
#> [1904] "0311" "01223" "01223" "0332" "01223" "01222" "01222" "01222" "01222" "01222" "01221"
#> [1915] "0323" "01222" "01221" "011" "01221" "03122" "0223" "01222" "0323" "0323" "01222"
#> [1926] "0311" "01222" "01222" "01222" "0233" "0323" "01222" "02112" "01222" "0323" "0233"
#> [1937] "0333" "01222" "01222" "0323" "0323" "01222" "0323" "01222" "0332" "0222" "03122"
#> [1948] "0323" "01222" "03121" "0323" "01222" "01222" "02123" "01222" "01222" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "01222" "0323" "02112" "0331" "0323"
#> [1970] "03122" "01222" "0233" "03122" "0323" "02122" "0311" "01222" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112" "0221"
#> [1992] "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "02121" "0232" "0232" "02123" "0231" "02121"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "02122" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "02122" "02122" "0223" "0222" "02122" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "02122" "02121" "02121" "0142" "0221" "02113" "0231" "02113" "02112" "02121" "0223"
#> [2058] "02122" "0321" "0223" "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122"
#> [2069] "02122" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231" "02121"
#> [2102] "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232" "02121" "02121" "0233"
#> [2124] "0232" "0142" "0223" "02121" "0142" "02112" "02112" "02122" "02121" "02112" "02112"
#> [2135] "02112" "02121" "02122" "02121" "0221" "02121" "0222" "0223" "02122" "0221" "0221"
#> [2146] "0222" "0223" "02121" "0223" "02121" "02112" "02122" "0223" "0223" "02122" "02112"
#> [2157] "02121" "0223" "0223" "02112" "0221" "0223" "0221" "02122" "0223" "0223" "0221"
#> [2168] "02121" "0223" "0223" "0223" "0221" "02121" "0321" "0221" "0221" "0221" "02111"
#> [2179] "02122" "02122" "02122" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "02121" "02121" "02121" "0231" "0232"
#> [2212] "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121" "0223" "02123"
#> [2223] "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121" "0223" "0223" "0223"
#> [2234] "0223" "02121" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "01231" "02122" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "02122" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "02121" "02112" "0221" "02121" "0221" "02121" "0234" "02121"
#> [2366] "02122" "0221" "02112" "02112" "0221" "0223" "0223" "02121" "0223" "02121" "0223"
#> [2377] "0221" "0221" "02123" "02121" "0232" "0223" "02112" "02122" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "02121" "02122" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "02122" "0221" "0231" "02111" "0223" "02122"
#> [2410] "0222" "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "01231"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "01234"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223" "02122" "02122" "02122"
#> [2454] "02112" "02122" "02122" "02112" "02122" "02122" "0231" "02122" "02122" "02122" "02123"
#> [2465] "02122" "02123" "02122" "02122" "0222" "0221" "0321" "0221" "0221" "0221" "02122"
#> [2476] "02122" "0223" "02122" "0223" "0221" "0223" "02122" "0223" "0223" "02112" "0223"
#> [2487] "02122" "02122" "02122" "02112" "02123" "02122" "02122" "02112" "0223" "02122" "0223"
#> [2498] "02122" "02122" "0221" "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221"
#> [2520] "02122" "02121" "02112" "0221" "0222" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121" "0321" "02122" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "02122" "02122" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221" "01221"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "01231" "0141" "01231" "0221" "0141" "01231"
#> [2685] "0121" "0232" "0232" "02112" "02112" "0321" "02121" "02121" "0234" "0231" "0143"
#> [2696] "0221" "0324" "02121" "0221" "0321" "0221" "02121" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "01231" "0321" "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "02121" "0231" "02111" "02111" "02121" "02111"
#> [2828] "02121" "02111" "0223" "02121" "02121" "02111" "02121" "0231" "0223" "02121" "02121"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "02121" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221" "02111"
#> [2872] "02121" "02123" "02111" "02111" "02121" "0223" "02121" "0142" "02121" "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 979))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "03121" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "03121" "0311" "011" "0222" "011" "0311"
#> [45] "012" "012" "0311" "03121" "0322" "011" "03121" "011" "011" "012" "012"
#> [56] "02123" "02123" "0143" "011" "0313" "0322" "011" "011" "0222" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "0143" "011"
#> [78] "011" "0222" "011" "0143" "0322" "011" "0143" "011" "0222" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "0143"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "0222" "0141" "0142" "011"
#> [111] "011" "011" "011" "0143" "011" "0222" "0222" "0322" "011" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "0222" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "0222" "011" "02123" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "0222"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "011" "0313" "0313" "03121" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "03121" "0222" "0322" "011" "0313" "03121" "0313" "0322" "03121" "03121"
#> [199] "03121" "03122" "03121" "0313" "03121" "0313" "03121" "03121" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "03121" "0313" "0313" "0322" "0222" "03121" "011"
#> [221] "03121" "0313" "03122" "0313" "03121" "0313" "03121" "03121" "011" "02113" "0313"
#> [232] "0313" "03121" "0313" "02113" "03121" "03121" "0313" "03121" "0313" "0313" "0313"
#> [243] "03121" "011" "03121" "03121" "03121" "0222" "03121" "0313" "011" "0313" "03121"
#> [254] "03121" "0313" "0313" "011" "03121" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "02123" "011" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "03121" "03121" "03121" "03122" "03121" "03121" "03121" "03121" "03121"
#> [298] "03122" "02113" "03121" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "03122"
#> [309] "0222" "03121" "03121" "0313" "0222" "0313" "0313" "03121" "011" "0313" "0313"
#> [320] "011" "0313" "011" "03121" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "0222" "0222" "011" "0313" "011" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "03122" "03121" "03122" "03122" "03121" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "03121" "03121" "03121" "0313" "03122" "03122" "03121"
#> [375] "03121" "03121" "03121" "03122" "03122" "0313" "011" "011" "0222" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "0222" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "0143" "0311" "011" "0222" "011"
#> [408] "0311" "011" "011" "0311" "0311" "02123" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "0222" "011" "02123" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "03122" "03122" "03121" "0311"
#> [452] "011" "0311" "011" "011" "03121" "011" "011" "03122" "03122" "0311" "03122"
#> [463] "0311" "0311" "03122" "03122" "03122" "0311" "03122" "03122" "03122" "0311" "03122"
#> [474] "03122" "03122" "0311" "0311" "03121" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "02123" "03122" "0311" "0311" "0222" "0222" "02123" "03121" "03122" "0222" "03122"
#> [496] "011" "02123" "02113" "011" "03122" "02113" "011" "0311" "03122" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "011" "011" "0222"
#> [518] "0311" "03121" "0311" "011" "011" "011" "03122" "03121" "0313" "03121" "011"
#> [529] "011" "0222" "02123" "02123" "011" "0222" "011" "011" "011" "02123" "011"
#> [540] "0311" "011" "0222" "011" "011" "0222" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "0143" "011" "0311" "0311" "0143" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "0222" "0311" "011" "011" "0222"
#> [573] "0324" "0311" "011" "03121" "011" "011" "011" "0222" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "03122"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "0222" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "03121" "011" "011" "0222"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "03122" "0311" "0311" "0311" "0311" "0222"
#> [672] "011" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "011" "0311" "0311"
#> [683] "03122" "03122" "0311" "03122" "03122" "03122" "0311" "0311" "0311" "011" "03122"
#> [694] "0311" "0222" "0311" "03122" "011" "03122" "0143" "03122" "011" "011" "0311"
#> [705] "0311" "0222" "0222" "011" "0324" "011" "0324" "02123" "011" "011" "011"
#> [716] "011" "011" "011" "0222" "0311" "0311" "0222" "0234" "011" "0222" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "03122"
#> [749] "03122" "03122" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "02123" "0222" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "03122" "011" "011" "0311" "011" "0222" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "03122" "011" "0311" "0311" "011" "011"
#> [793] "03122" "0222" "011" "03122" "011" "011" "0234" "0311" "03122" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "0143" "011" "011" "011" "02123" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "0222" "011" "0143" "0143" "0143"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "0143" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "02123" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "0222" "0222" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "0222" "011" "0143" "011" "011" "011" "0222"
#> [936] "0324" "011" "0222" "011" "011" "011" "0143" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "02123" "0143"
#> [958] "0143" "011" "02123" "0222" "011" "011" "02123" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "0222" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "0222" "0222" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "0222"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "0222" "012" "0222" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "0142" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "03121" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "03121" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "03121" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "0142" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "0141" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "03122" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "0221" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "0142" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "0223" "012" "0141" "012" "0221" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "0141" "012" "012" "013"
#> [1200] "012" "012" "0141" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "0141" "0222" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "0221" "012" "0223" "012" "012" "012" "0141" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "0141" "012" "0333" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "0221" "0221" "0221" "0223" "012" "012" "012" "012" "012" "0311"
#> [1266] "0141" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "02122" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "012" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "02122" "02122" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "0333" "012" "0313"
#> [1475] "0333" "0313" "011" "02121" "013" "0221" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "011" "0231" "0141"
#> [1497] "013" "011" "013" "0233" "012" "0141" "013" "011" "012" "0321" "013"
#> [1508] "0222" "013" "0223" "012" "013" "012" "013" "013" "012" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "012" "0223" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "0142" "02121" "0233" "013" "012" "012"
#> [1563] "0143" "03121" "0223" "011" "013" "0333" "013" "012" "013" "0223" "02121"
#> [1574] "0142" "02121" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "02121" "0233" "013" "02113" "013" "0332" "013" "02123" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "011" "02122" "012" "013"
#> [1629] "012" "013" "0141" "012" "0323" "012" "02122" "012" "012" "012" "0221"
#> [1640] "012" "012" "013" "012" "0233" "0141" "012" "0141" "012" "012" "012"
#> [1651] "0142" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "0141" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "0141" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "02121" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "03121" "012" "0221" "012" "0221" "012" "011" "012" "0142" "03122"
#> [1761] "0223" "012" "011" "012" "011" "0221" "0311" "011" "013" "0221" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "0223" "011" "012" "0313" "0223" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "02121" "0223" "0311" "0221" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "0223" "012" "012" "0323" "012" "012" "02122" "0223"
#> [1838] "012" "011" "012" "012" "012" "02121" "012" "012" "0143" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "03122" "012" "012" "0332" "0223" "03122" "0321" "0323" "02122" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "03122" "012" "012" "011" "0221"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "03121" "013" "0323" "012"
#> [1904] "0311" "012" "012" "0332" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "03122" "0223" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "0333" "012" "012" "0323" "0323" "012" "0323" "012" "0332" "0222" "03122"
#> [1948] "0323" "012" "03121" "0323" "012" "012" "02123" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "012" "0323" "02112" "0331" "0323"
#> [1970] "03122" "012" "0233" "03122" "0323" "02122" "0311" "012" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112" "0221"
#> [1992] "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "02121" "0232" "0232" "02123" "0231" "02121"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "02122" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "02122" "02122" "0223" "0222" "02122" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "02122" "02121" "02121" "0142" "0221" "02113" "0231" "02113" "02112" "02121" "0223"
#> [2058] "02122" "0321" "0223" "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122"
#> [2069] "02122" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231" "02121"
#> [2102] "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232" "02121" "02121" "0233"
#> [2124] "0232" "0142" "0223" "02121" "0142" "02112" "02112" "02122" "02121" "02112" "02112"
#> [2135] "02112" "02121" "02122" "02121" "0221" "02121" "0222" "0223" "02122" "0221" "0221"
#> [2146] "0222" "0223" "02121" "0223" "02121" "02112" "02122" "0223" "0223" "02122" "02112"
#> [2157] "02121" "0223" "0223" "02112" "0221" "0223" "0221" "02122" "0223" "0223" "0221"
#> [2168] "02121" "0223" "0223" "0223" "0221" "02121" "0321" "0221" "0221" "0221" "02111"
#> [2179] "02122" "02122" "02122" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "03121" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "02121" "02121" "02121" "0231" "0232"
#> [2212] "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121" "0223" "02123"
#> [2223] "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121" "0223" "0223" "0223"
#> [2234] "0223" "02121" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "012" "02122" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "02122" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "02121" "02112" "0221" "02121" "0221" "02121" "0234" "02121"
#> [2366] "02122" "0221" "02112" "02112" "0221" "0223" "0223" "02121" "0223" "02121" "0223"
#> [2377] "0221" "0221" "02123" "02121" "0232" "0223" "02112" "02122" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "02121" "02122" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "02122" "0221" "0231" "02111" "0223" "02122"
#> [2410] "0222" "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "012"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "012"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223" "02122" "02122" "02122"
#> [2454] "02112" "02122" "02122" "02112" "02122" "02122" "0231" "02122" "02122" "02122" "02123"
#> [2465] "02122" "02123" "02122" "02122" "0222" "0221" "0321" "0221" "0221" "0221" "02122"
#> [2476] "02122" "0223" "02122" "0223" "0221" "0223" "02122" "0223" "0223" "02112" "0223"
#> [2487] "02122" "02122" "02122" "02112" "02123" "02122" "02122" "02112" "0223" "02122" "0223"
#> [2498] "02122" "02122" "0221" "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221"
#> [2520] "02122" "02121" "02112" "0221" "0222" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121" "0321" "02122" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "02122" "02122" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221" "012"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "012" "0141" "012" "0221" "0141" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "02121" "02121" "0234" "0231" "0143"
#> [2696] "0221" "0324" "02121" "0221" "0321" "0221" "02121" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "012" "0321" "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "02121" "0231" "02111" "02111" "02121" "02111"
#> [2828] "02121" "02111" "0223" "02121" "02121" "02111" "02121" "0231" "0223" "02121" "02121"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "02121" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221" "02111"
#> [2872] "02121" "02123" "02111" "02111" "02121" "0223" "02121" "0142" "02121" "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1000))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "0312" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "0312" "0311" "011" "0222" "011" "0311"
#> [45] "012" "012" "0311" "0312" "0322" "011" "0312" "011" "011" "012" "012"
#> [56] "02123" "02123" "0143" "011" "0313" "0322" "011" "011" "0222" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "0143" "011"
#> [78] "011" "0222" "011" "0143" "0322" "011" "0143" "011" "0222" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "0143"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "0222" "0141" "0142" "011"
#> [111] "011" "011" "011" "0143" "011" "0222" "0222" "0322" "011" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "0222" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "0222" "011" "02123" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "0222"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "011" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "0222" "0322" "011" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "0222" "0312" "011"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "011" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "011" "0312" "0312" "0312" "0222" "0312" "0313" "011" "0313" "0312"
#> [254] "0312" "0313" "0313" "011" "0312" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "02123" "011" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "0312"
#> [309] "0222" "0312" "0312" "0313" "0222" "0313" "0313" "0312" "011" "0313" "0313"
#> [320] "011" "0313" "011" "0312" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "0222" "0222" "011" "0313" "011" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "011" "011" "0222" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "0222" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "0143" "0311" "011" "0222" "011"
#> [408] "0311" "011" "011" "0311" "0311" "02123" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "0222" "011" "02123" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "0312" "0312" "0312" "0311"
#> [452] "011" "0311" "011" "011" "0312" "011" "011" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "02123" "0312" "0311" "0311" "0222" "0222" "02123" "0312" "0312" "0222" "0312"
#> [496] "011" "02123" "02113" "011" "0312" "02113" "011" "0311" "0312" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "011" "011" "0222"
#> [518] "0311" "0312" "0311" "011" "011" "011" "0312" "0312" "0313" "0312" "011"
#> [529] "011" "0222" "02123" "02123" "011" "0222" "011" "011" "011" "02123" "011"
#> [540] "0311" "011" "0222" "011" "011" "0222" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "0143" "011" "0311" "0311" "0143" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "0222" "0311" "011" "011" "0222"
#> [573] "0324" "0311" "011" "0312" "011" "011" "011" "0222" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "0312"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "0222" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "0312" "011" "011" "0222"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "0312" "0311" "0311" "0311" "0311" "0222"
#> [672] "011" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "011" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "011" "0312"
#> [694] "0311" "0222" "0311" "0312" "011" "0312" "0143" "0312" "011" "011" "0311"
#> [705] "0311" "0222" "0222" "011" "0324" "011" "0324" "02123" "011" "011" "011"
#> [716] "011" "011" "011" "0222" "0311" "0311" "0222" "0234" "011" "0222" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "0312"
#> [749] "0312" "0312" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "02123" "0222" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "0312" "011" "011" "0311" "011" "0222" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "0312" "011" "0311" "0311" "011" "011"
#> [793] "0312" "0222" "011" "0312" "011" "011" "0234" "0311" "0312" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "0143" "011" "011" "011" "02123" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "0222" "011" "0143" "0143" "0143"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "0143" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "02123" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "0222" "0222" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "0222" "011" "0143" "011" "011" "011" "0222"
#> [936] "0324" "011" "0222" "011" "011" "011" "0143" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "02123" "0143"
#> [958] "0143" "011" "02123" "0222" "011" "011" "02123" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "0222" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "0222" "0222" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "0222"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "0222" "012" "0222" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "0142" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "0312" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "0312" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "0312" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "0142" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "0141" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "0312" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "0221" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "0142" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "0223" "012" "0141" "012" "0221" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "0141" "012" "012" "013"
#> [1200] "012" "012" "0141" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "0141" "0222" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "0221" "012" "0223" "012" "012" "012" "0141" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "0141" "012" "0333" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "0221" "0221" "0221" "0223" "012" "012" "012" "012" "012" "0311"
#> [1266] "0141" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "02122" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "012" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "02122" "02122" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "0333" "012" "0313"
#> [1475] "0333" "0313" "011" "02121" "013" "0221" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "011" "0231" "0141"
#> [1497] "013" "011" "013" "0233" "012" "0141" "013" "011" "012" "0321" "013"
#> [1508] "0222" "013" "0223" "012" "013" "012" "013" "013" "012" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "012" "0223" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "0142" "02121" "0233" "013" "012" "012"
#> [1563] "0143" "0312" "0223" "011" "013" "0333" "013" "012" "013" "0223" "02121"
#> [1574] "0142" "02121" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "02121" "0233" "013" "02113" "013" "0332" "013" "02123" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "011" "02122" "012" "013"
#> [1629] "012" "013" "0141" "012" "0323" "012" "02122" "012" "012" "012" "0221"
#> [1640] "012" "012" "013" "012" "0233" "0141" "012" "0141" "012" "012" "012"
#> [1651] "0142" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "0141" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "0141" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "02121" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "0312" "012" "0221" "012" "0221" "012" "011" "012" "0142" "0312"
#> [1761] "0223" "012" "011" "012" "011" "0221" "0311" "011" "013" "0221" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "0223" "011" "012" "0313" "0223" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "02121" "0223" "0311" "0221" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "0223" "012" "012" "0323" "012" "012" "02122" "0223"
#> [1838] "012" "011" "012" "012" "012" "02121" "012" "012" "0143" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "0312" "012" "012" "0332" "0223" "0312" "0321" "0323" "02122" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "0312" "012" "012" "011" "0221"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "0312" "013" "0323" "012"
#> [1904] "0311" "012" "012" "0332" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "0312" "0223" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "0333" "012" "012" "0323" "0323" "012" "0323" "012" "0332" "0222" "0312"
#> [1948] "0323" "012" "0312" "0323" "012" "012" "02123" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "012" "0323" "02112" "0331" "0323"
#> [1970] "0312" "012" "0233" "0312" "0323" "02122" "0311" "012" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112" "0221"
#> [1992] "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "02121" "0232" "0232" "02123" "0231" "02121"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "02122" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "02122" "02122" "0223" "0222" "02122" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "02122" "02121" "02121" "0142" "0221" "02113" "0231" "02113" "02112" "02121" "0223"
#> [2058] "02122" "0321" "0223" "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122"
#> [2069] "02122" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231" "02121"
#> [2102] "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232" "02121" "02121" "0233"
#> [2124] "0232" "0142" "0223" "02121" "0142" "02112" "02112" "02122" "02121" "02112" "02112"
#> [2135] "02112" "02121" "02122" "02121" "0221" "02121" "0222" "0223" "02122" "0221" "0221"
#> [2146] "0222" "0223" "02121" "0223" "02121" "02112" "02122" "0223" "0223" "02122" "02112"
#> [2157] "02121" "0223" "0223" "02112" "0221" "0223" "0221" "02122" "0223" "0223" "0221"
#> [2168] "02121" "0223" "0223" "0223" "0221" "02121" "0321" "0221" "0221" "0221" "02111"
#> [2179] "02122" "02122" "02122" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "0312" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "02121" "02121" "02121" "0231" "0232"
#> [2212] "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121" "0223" "02123"
#> [2223] "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121" "0223" "0223" "0223"
#> [2234] "0223" "02121" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "012" "02122" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "02122" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "02121" "02112" "0221" "02121" "0221" "02121" "0234" "02121"
#> [2366] "02122" "0221" "02112" "02112" "0221" "0223" "0223" "02121" "0223" "02121" "0223"
#> [2377] "0221" "0221" "02123" "02121" "0232" "0223" "02112" "02122" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "02121" "02122" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "02122" "0221" "0231" "02111" "0223" "02122"
#> [2410] "0222" "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "012"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "012"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223" "02122" "02122" "02122"
#> [2454] "02112" "02122" "02122" "02112" "02122" "02122" "0231" "02122" "02122" "02122" "02123"
#> [2465] "02122" "02123" "02122" "02122" "0222" "0221" "0321" "0221" "0221" "0221" "02122"
#> [2476] "02122" "0223" "02122" "0223" "0221" "0223" "02122" "0223" "0223" "02112" "0223"
#> [2487] "02122" "02122" "02122" "02112" "02123" "02122" "02122" "02112" "0223" "02122" "0223"
#> [2498] "02122" "02122" "0221" "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221"
#> [2520] "02122" "02121" "02112" "0221" "0222" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121" "0321" "02122" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "02122" "02122" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221" "012"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "012" "0141" "012" "0221" "0141" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "02121" "02121" "0234" "0231" "0143"
#> [2696] "0221" "0324" "02121" "0221" "0321" "0221" "02121" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "012" "0321" "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "02121" "0231" "02111" "02111" "02121" "02111"
#> [2828] "02121" "02111" "0223" "02121" "02121" "02111" "02121" "0231" "0223" "02121" "02121"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "02121" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221" "02111"
#> [2872] "02121" "02123" "02111" "02111" "02121" "0223" "02121" "0142" "02121" "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1348))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "0312" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "0312" "0311" "011" "0222" "011" "0311"
#> [45] "012" "012" "0311" "0312" "0322" "011" "0312" "011" "011" "012" "012"
#> [56] "02123" "02123" "0143" "011" "0313" "0322" "011" "011" "0222" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "0143" "011"
#> [78] "011" "0222" "011" "0143" "0322" "011" "0143" "011" "0222" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "0143"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "0222" "0141" "0142" "011"
#> [111] "011" "011" "011" "0143" "011" "0222" "0222" "0322" "011" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "0222" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "0222" "011" "02123" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "0222"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "011" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "0222" "0322" "011" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "0222" "0312" "011"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "011" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "011" "0312" "0312" "0312" "0222" "0312" "0313" "011" "0313" "0312"
#> [254] "0312" "0313" "0313" "011" "0312" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "02123" "011" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "0312"
#> [309] "0222" "0312" "0312" "0313" "0222" "0313" "0313" "0312" "011" "0313" "0313"
#> [320] "011" "0313" "011" "0312" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "0222" "0222" "011" "0313" "011" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "011" "011" "0222" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "0222" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "0143" "0311" "011" "0222" "011"
#> [408] "0311" "011" "011" "0311" "0311" "02123" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "0222" "011" "02123" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "0312" "0312" "0312" "0311"
#> [452] "011" "0311" "011" "011" "0312" "011" "011" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "02123" "0312" "0311" "0311" "0222" "0222" "02123" "0312" "0312" "0222" "0312"
#> [496] "011" "02123" "02113" "011" "0312" "02113" "011" "0311" "0312" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "011" "011" "0222"
#> [518] "0311" "0312" "0311" "011" "011" "011" "0312" "0312" "0313" "0312" "011"
#> [529] "011" "0222" "02123" "02123" "011" "0222" "011" "011" "011" "02123" "011"
#> [540] "0311" "011" "0222" "011" "011" "0222" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "0143" "011" "0311" "0311" "0143" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "0222" "0311" "011" "011" "0222"
#> [573] "0324" "0311" "011" "0312" "011" "011" "011" "0222" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "0312"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "0222" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "0312" "011" "011" "0222"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "0312" "0311" "0311" "0311" "0311" "0222"
#> [672] "011" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "011" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "011" "0312"
#> [694] "0311" "0222" "0311" "0312" "011" "0312" "0143" "0312" "011" "011" "0311"
#> [705] "0311" "0222" "0222" "011" "0324" "011" "0324" "02123" "011" "011" "011"
#> [716] "011" "011" "011" "0222" "0311" "0311" "0222" "0234" "011" "0222" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "0312"
#> [749] "0312" "0312" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "02123" "0222" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "0312" "011" "011" "0311" "011" "0222" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "0312" "011" "0311" "0311" "011" "011"
#> [793] "0312" "0222" "011" "0312" "011" "011" "0234" "0311" "0312" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "0143" "011" "011" "011" "02123" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "0222" "011" "0143" "0143" "0143"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "0143" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "02123" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "0222" "0222" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "0222" "011" "0143" "011" "011" "011" "0222"
#> [936] "0324" "011" "0222" "011" "011" "011" "0143" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "02123" "0143"
#> [958] "0143" "011" "02123" "0222" "011" "011" "02123" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "0222" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "0222" "0222" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "0222"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "0222" "012" "0222" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "0142" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "0312" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "0312" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "0312" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "0142" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "0141" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "0312" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "0221" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "0142" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "0223" "012" "0141" "012" "0221" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "0141" "012" "012" "013"
#> [1200] "012" "012" "0141" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "0141" "0222" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "0221" "012" "0223" "012" "012" "012" "0141" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "0141" "012" "0333" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "0221" "0221" "0221" "0223" "012" "012" "012" "012" "012" "0311"
#> [1266] "0141" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "02122" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "02122" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "012" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "02122" "02122" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "0333" "012" "0313"
#> [1475] "0333" "0313" "011" "02121" "013" "0221" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "011" "0231" "0141"
#> [1497] "013" "011" "013" "0233" "012" "0141" "013" "011" "012" "0321" "013"
#> [1508] "0222" "013" "0223" "012" "013" "012" "013" "013" "012" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "012" "0223" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "0142" "02121" "0233" "013" "012" "012"
#> [1563] "0143" "0312" "0223" "011" "013" "0333" "013" "012" "013" "0223" "02121"
#> [1574] "0142" "02121" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "02121" "0233" "013" "02113" "013" "0332" "013" "02123" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "011" "02122" "012" "013"
#> [1629] "012" "013" "0141" "012" "0323" "012" "02122" "012" "012" "012" "0221"
#> [1640] "012" "012" "013" "012" "0233" "0141" "012" "0141" "012" "012" "012"
#> [1651] "0142" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "0141" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "0141" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "02121" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "0312" "012" "0221" "012" "0221" "012" "011" "012" "0142" "0312"
#> [1761] "0223" "012" "011" "012" "011" "0221" "0311" "011" "013" "0221" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "0223" "011" "012" "0313" "0223" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "02121" "0223" "0311" "0221" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "0223" "012" "012" "0323" "012" "012" "02122" "0223"
#> [1838] "012" "011" "012" "012" "012" "02121" "012" "012" "0143" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "0312" "012" "012" "0332" "0223" "0312" "0321" "0323" "02122" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "0312" "012" "012" "011" "0221"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "0312" "013" "0323" "012"
#> [1904] "0311" "012" "012" "0332" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "0312" "0223" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "0333" "012" "012" "0323" "0323" "012" "0323" "012" "0332" "0222" "0312"
#> [1948] "0323" "012" "0312" "0323" "012" "012" "02123" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "012" "0323" "02112" "0331" "0323"
#> [1970] "0312" "012" "0233" "0312" "0323" "02122" "0311" "012" "02122" "0323" "02121"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "02121" "02122" "02122" "02112" "0221"
#> [1992] "02122" "0231" "0232" "0223" "02123" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "02121" "0232" "0232" "02123" "0231" "02121"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "02122" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "02122" "02122" "0223" "0222" "02122" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "02122" "02121" "02121" "0142" "0221" "02113" "0231" "02113" "02112" "02121" "0223"
#> [2058] "02122" "0321" "0223" "02112" "0223" "0223" "02122" "0221" "0223" "02122" "02122"
#> [2069] "02122" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "02123" "02122" "0231" "02121" "02122" "02121" "0232" "02121" "0221"
#> [2091] "02121" "0223" "0223" "02122" "0223" "0223" "0223" "02121" "0223" "0231" "02121"
#> [2102] "02121" "02121" "02122" "02112" "02112" "02121" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "02123" "0231" "0232" "02121" "02121" "0233"
#> [2124] "0232" "0142" "0223" "02121" "0142" "02112" "02112" "02122" "02121" "02112" "02112"
#> [2135] "02112" "02121" "02122" "02121" "0221" "02121" "0222" "0223" "02122" "0221" "0221"
#> [2146] "0222" "0223" "02121" "0223" "02121" "02112" "02122" "0223" "0223" "02122" "02112"
#> [2157] "02121" "0223" "0223" "02112" "0221" "0223" "0221" "02122" "0223" "0223" "0221"
#> [2168] "02121" "0223" "0223" "0223" "0221" "02121" "0321" "0221" "0221" "0221" "02111"
#> [2179] "02122" "02122" "02122" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "0312" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "02121" "02121" "02121" "0231" "0232"
#> [2212] "0221" "0232" "0223" "02121" "02123" "02112" "02112" "02121" "02121" "0223" "02123"
#> [2223] "02121" "02121" "0221" "02112" "02112" "02121" "0223" "02121" "0223" "0223" "0223"
#> [2234] "0223" "02121" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "012" "02122" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "02122" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "02121" "02112" "0221" "02121" "0221" "02121" "0234" "02121"
#> [2366] "02122" "0221" "02112" "02112" "0221" "0223" "0223" "02121" "0223" "02121" "0223"
#> [2377] "0221" "0221" "02123" "02121" "0232" "0223" "02112" "02122" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "02121" "02122" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "02122" "0221" "0231" "02111" "0223" "02122"
#> [2410] "0222" "02122" "0221" "02122" "0142" "0221" "0223" "0221" "0223" "0231" "012"
#> [2421] "0223" "0221" "0321" "02121" "02121" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "012"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "02122" "0223" "02122" "02122" "02122"
#> [2454] "02112" "02122" "02122" "02112" "02122" "02122" "0231" "02122" "02122" "02122" "02123"
#> [2465] "02122" "02123" "02122" "02122" "0222" "0221" "0321" "0221" "0221" "0221" "02122"
#> [2476] "02122" "0223" "02122" "0223" "0221" "0223" "02122" "0223" "0223" "02112" "0223"
#> [2487] "02122" "02122" "02122" "02112" "02123" "02122" "02122" "02112" "0223" "02122" "0223"
#> [2498] "02122" "02122" "0221" "02122" "0223" "02121" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "02122" "02122" "02112" "02122" "02122" "02112" "02122" "0221"
#> [2520] "02122" "02121" "02112" "0221" "0222" "0221" "02122" "02112" "0221" "02122" "02113"
#> [2531] "0223" "02122" "02112" "0141" "02121" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "02121" "0321" "0221" "0141" "02112" "02121" "0321" "02122" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "02122" "02122" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "02121" "02112" "02112" "0223" "02122" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "02121" "0221" "0221" "0223" "0321" "0221" "012"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "012" "0141" "012" "0221" "0141" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "02121" "02121" "0234" "0231" "0143"
#> [2696] "0221" "0324" "02121" "0221" "0321" "0221" "02121" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "012" "0321" "02121" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "02121" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "02121" "0231" "02111" "02111" "02121" "02111"
#> [2828] "02121" "02111" "0223" "02121" "02121" "02111" "02121" "0231" "0223" "02121" "02121"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "02121" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "02123" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "02121" "02121" "0231" "0221" "02121" "0221" "02111"
#> [2872] "02121" "02123" "02111" "02111" "02121" "0223" "02121" "0142" "02121" "02121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1387))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "0312" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "0312" "0311" "011" "0222" "011" "0311"
#> [45] "012" "012" "0311" "0312" "0322" "011" "0312" "011" "011" "012" "012"
#> [56] "0212" "0212" "0143" "011" "0313" "0322" "011" "011" "0222" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "0143" "011"
#> [78] "011" "0222" "011" "0143" "0322" "011" "0143" "011" "0222" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "0143"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "0222" "0141" "0142" "011"
#> [111] "011" "011" "011" "0143" "011" "0222" "0222" "0322" "011" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "0222" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "0222" "011" "0212" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "0222"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "011" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "0222" "0322" "011" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "0222" "0312" "011"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "011" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "011" "0312" "0312" "0312" "0222" "0312" "0313" "011" "0313" "0312"
#> [254] "0312" "0313" "0313" "011" "0312" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "0212" "011" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "0312"
#> [309] "0222" "0312" "0312" "0313" "0222" "0313" "0313" "0312" "011" "0313" "0313"
#> [320] "011" "0313" "011" "0312" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "0222" "0222" "011" "0313" "011" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "011" "011" "0222" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "0222" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "0143" "0311" "011" "0222" "011"
#> [408] "0311" "011" "011" "0311" "0311" "0212" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "0222" "011" "0212" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "0312" "0312" "0312" "0311"
#> [452] "011" "0311" "011" "011" "0312" "011" "011" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "0212" "0312" "0311" "0311" "0222" "0222" "0212" "0312" "0312" "0222" "0312"
#> [496] "011" "0212" "02113" "011" "0312" "02113" "011" "0311" "0312" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "011" "011" "0222"
#> [518] "0311" "0312" "0311" "011" "011" "011" "0312" "0312" "0313" "0312" "011"
#> [529] "011" "0222" "0212" "0212" "011" "0222" "011" "011" "011" "0212" "011"
#> [540] "0311" "011" "0222" "011" "011" "0222" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "0143" "011" "0311" "0311" "0143" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "0222" "0311" "011" "011" "0222"
#> [573] "0324" "0311" "011" "0312" "011" "011" "011" "0222" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "0312"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "0222" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "0312" "011" "011" "0222"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "0312" "0311" "0311" "0311" "0311" "0222"
#> [672] "011" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "011" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "011" "0312"
#> [694] "0311" "0222" "0311" "0312" "011" "0312" "0143" "0312" "011" "011" "0311"
#> [705] "0311" "0222" "0222" "011" "0324" "011" "0324" "0212" "011" "011" "011"
#> [716] "011" "011" "011" "0222" "0311" "0311" "0222" "0234" "011" "0222" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "0312"
#> [749] "0312" "0312" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "0212" "0222" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "0312" "011" "011" "0311" "011" "0222" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "0312" "011" "0311" "0311" "011" "011"
#> [793] "0312" "0222" "011" "0312" "011" "011" "0234" "0311" "0312" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "0143" "011" "011" "011" "0212" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "0222" "011" "0143" "0143" "0143"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "0143" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "0212" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "0222" "0222" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "0222" "011" "0143" "011" "011" "011" "0222"
#> [936] "0324" "011" "0222" "011" "011" "011" "0143" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "0212" "0143"
#> [958] "0143" "011" "0212" "0222" "011" "011" "0212" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "0222" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "0222" "0222" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "0222"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "0222" "012" "0222" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "0142" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "0312" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "0312" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "0312" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "0142" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "0141" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "0312" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "0221" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "0142" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "0223" "012" "0141" "012" "0221" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "0141" "012" "012" "013"
#> [1200] "012" "012" "0141" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "0141" "0222" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "0221" "012" "0223" "012" "012" "012" "0141" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "0141" "012" "0333" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "0221" "0221" "0221" "0223" "012" "012" "012" "012" "012" "0311"
#> [1266] "0141" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "0212" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "0212" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "012" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "0212" "0212" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "0333" "012" "0313"
#> [1475] "0333" "0313" "011" "0212" "013" "0221" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "011" "0231" "0141"
#> [1497] "013" "011" "013" "0233" "012" "0141" "013" "011" "012" "0321" "013"
#> [1508] "0222" "013" "0223" "012" "013" "012" "013" "013" "012" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "012" "0223" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "0142" "0212" "0233" "013" "012" "012"
#> [1563] "0143" "0312" "0223" "011" "013" "0333" "013" "012" "013" "0223" "0212"
#> [1574] "0142" "0212" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "0212" "0233" "013" "02113" "013" "0332" "013" "0212" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "011" "0212" "012" "013"
#> [1629] "012" "013" "0141" "012" "0323" "012" "0212" "012" "012" "012" "0221"
#> [1640] "012" "012" "013" "012" "0233" "0141" "012" "0141" "012" "012" "012"
#> [1651] "0142" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "0141" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "0141" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "0212" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "0312" "012" "0221" "012" "0221" "012" "011" "012" "0142" "0312"
#> [1761] "0223" "012" "011" "012" "011" "0221" "0311" "011" "013" "0221" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "0223" "011" "012" "0313" "0223" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "0212" "0223" "0311" "0221" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "0223" "012" "012" "0323" "012" "012" "0212" "0223"
#> [1838] "012" "011" "012" "012" "012" "0212" "012" "012" "0143" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "0312" "012" "012" "0332" "0223" "0312" "0321" "0323" "0212" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "0312" "012" "012" "011" "0221"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "0312" "013" "0323" "012"
#> [1904] "0311" "012" "012" "0332" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "0312" "0223" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "0333" "012" "012" "0323" "0323" "012" "0323" "012" "0332" "0222" "0312"
#> [1948] "0323" "012" "0312" "0323" "012" "012" "0212" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "012" "0323" "02112" "0331" "0323"
#> [1970] "0312" "012" "0233" "0312" "0323" "0212" "0311" "012" "0212" "0323" "0212"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "0212" "0212" "0212" "02112" "0221"
#> [1992] "0212" "0231" "0232" "0223" "0212" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "0212" "0232" "0232" "0212" "0231" "0212"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "0212" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "0212" "0212" "0223" "0222" "0212" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "0212" "0212" "0212" "0142" "0221" "02113" "0231" "02113" "02112" "0212" "0223"
#> [2058] "0212" "0321" "0223" "02112" "0223" "0223" "0212" "0221" "0223" "0212" "0212"
#> [2069] "0212" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0232" "0212" "0221"
#> [2091] "0212" "0223" "0223" "0212" "0223" "0223" "0223" "0212" "0223" "0231" "0212"
#> [2102] "0212" "0212" "0212" "02112" "02112" "0212" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "0212" "0231" "0232" "0212" "0212" "0233"
#> [2124] "0232" "0142" "0223" "0212" "0142" "02112" "02112" "0212" "0212" "02112" "02112"
#> [2135] "02112" "0212" "0212" "0212" "0221" "0212" "0222" "0223" "0212" "0221" "0221"
#> [2146] "0222" "0223" "0212" "0223" "0212" "02112" "0212" "0223" "0223" "0212" "02112"
#> [2157] "0212" "0223" "0223" "02112" "0221" "0223" "0221" "0212" "0223" "0223" "0221"
#> [2168] "0212" "0223" "0223" "0223" "0221" "0212" "0321" "0221" "0221" "0221" "02111"
#> [2179] "0212" "0212" "0212" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "0312" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "0212" "0212" "0212" "0231" "0232"
#> [2212] "0221" "0232" "0223" "0212" "0212" "02112" "02112" "0212" "0212" "0223" "0212"
#> [2223] "0212" "0212" "0221" "02112" "02112" "0212" "0223" "0212" "0223" "0223" "0223"
#> [2234] "0223" "0212" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "012" "0212" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "0212" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "0212" "02112" "0221" "0212" "0221" "0212" "0234" "0212"
#> [2366] "0212" "0221" "02112" "02112" "0221" "0223" "0223" "0212" "0223" "0212" "0223"
#> [2377] "0221" "0221" "0212" "0212" "0232" "0223" "02112" "0212" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "0212" "0212" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "0212" "0221" "0231" "02111" "0223" "0212"
#> [2410] "0222" "0212" "0221" "0212" "0142" "0221" "0223" "0221" "0223" "0231" "012"
#> [2421] "0223" "0221" "0321" "0212" "0212" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "012"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "0212" "0223" "0212" "0212" "0212"
#> [2454] "02112" "0212" "0212" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0212"
#> [2465] "0212" "0212" "0212" "0212" "0222" "0221" "0321" "0221" "0221" "0221" "0212"
#> [2476] "0212" "0223" "0212" "0223" "0221" "0223" "0212" "0223" "0223" "02112" "0223"
#> [2487] "0212" "0212" "0212" "02112" "0212" "0212" "0212" "02112" "0223" "0212" "0223"
#> [2498] "0212" "0212" "0221" "0212" "0223" "0212" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "0212" "0212" "02112" "0212" "0212" "02112" "0212" "0221"
#> [2520] "0212" "0212" "02112" "0221" "0222" "0221" "0212" "02112" "0221" "0212" "02113"
#> [2531] "0223" "0212" "02112" "0141" "0212" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "0212" "0321" "0221" "0141" "02112" "0212" "0321" "0212" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "0212" "0212" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "0212" "02112" "02112" "0223" "0212" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "0212" "0221" "0221" "0223" "0321" "0221" "012"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "012" "0141" "012" "0221" "0141" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "0212" "0212" "0234" "0231" "0143"
#> [2696] "0221" "0324" "0212" "0221" "0321" "0221" "0212" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "012" "0321" "0212" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "0212" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "0212" "0231" "02111" "02111" "0212" "02111"
#> [2828] "0212" "02111" "0223" "0212" "0212" "02111" "0212" "0231" "0223" "0212" "0212"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "0212" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "0212" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "0212" "0212" "0231" "0221" "0212" "0221" "02111"
#> [2872] "0212" "0212" "02111" "02111" "0212" "0223" "0212" "0142" "0212" "0212"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1390))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "0312" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "0312" "0311" "011" "0222" "011" "0311"
#> [45] "012" "012" "0311" "0312" "0322" "011" "0312" "011" "011" "012" "012"
#> [56] "0212" "0212" "0143" "011" "0313" "0322" "011" "011" "0222" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "0143" "011"
#> [78] "011" "0222" "011" "0143" "0322" "011" "0143" "011" "0222" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "0143"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "0222" "0141" "0142" "011"
#> [111] "011" "011" "011" "0143" "011" "0222" "0222" "0322" "011" "0321" "0313"
#> [122] "0322" "0222" "0222" "0222" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "0222" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "0222" "011" "0212" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "0222"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "0222" "0222" "0313"
#> [177] "0313" "011" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "0222" "0322" "011" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "0222" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "0222" "0312" "011"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "011" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "011" "0312" "0312" "0312" "0222" "0312" "0313" "011" "0313" "0312"
#> [254] "0312" "0313" "0313" "011" "0312" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "0212" "011" "0313" "0313" "0313" "0222"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "0222" "0312"
#> [309] "0222" "0312" "0312" "0313" "0222" "0313" "0313" "0312" "011" "0313" "0313"
#> [320] "011" "0313" "011" "0312" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "0222" "0222" "011" "0313" "011" "0313"
#> [342] "0313" "0222" "0313" "0313" "0313" "0222" "0311" "0311" "0222" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "011" "011" "0222" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "0222" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "0143" "0311" "011" "0222" "011"
#> [408] "0311" "011" "011" "0311" "0311" "0212" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "0222" "011" "0212" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "0312" "0312" "0312" "0311"
#> [452] "011" "0311" "011" "011" "0312" "011" "011" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "0212" "0312" "0311" "0311" "0222" "0222" "0212" "0312" "0312" "0222" "0312"
#> [496] "011" "0212" "02113" "011" "0312" "02113" "011" "0311" "0312" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "0222" "0311" "0311" "011" "011" "0222"
#> [518] "0311" "0312" "0311" "011" "011" "011" "0312" "0312" "0313" "0312" "011"
#> [529] "011" "0222" "0212" "0212" "011" "0222" "011" "011" "011" "0212" "011"
#> [540] "0311" "011" "0222" "011" "011" "0222" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "0143" "011" "0311" "0311" "0143" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "0222" "0311" "011" "011" "0222"
#> [573] "0324" "0311" "011" "0312" "011" "011" "011" "0222" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "0312"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "0222" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "0312" "011" "011" "0222"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "0312" "0311" "0311" "0311" "0311" "0222"
#> [672] "011" "0222" "0311" "0313" "0234" "0311" "0311" "0222" "011" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "011" "0312"
#> [694] "0311" "0222" "0311" "0312" "011" "0312" "0143" "0312" "011" "011" "0311"
#> [705] "0311" "0222" "0222" "011" "0324" "011" "0324" "0212" "011" "011" "011"
#> [716] "011" "011" "011" "0222" "0311" "0311" "0222" "0234" "011" "0222" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "0312"
#> [749] "0312" "0312" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "0212" "0222" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "0312" "011" "011" "0311" "011" "0222" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "0312" "011" "0311" "0311" "011" "011"
#> [793] "0312" "0222" "011" "0312" "011" "011" "0234" "0311" "0312" "0222" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "0143" "011" "011" "011" "0212" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "0222" "011" "0143" "0143" "0143"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "0143" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "0212" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "0222" "0222" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "0222" "011" "0143" "011" "011" "011" "0222"
#> [936] "0324" "011" "0222" "011" "011" "011" "0143" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "0212" "0143"
#> [958] "0143" "011" "0212" "0222" "011" "011" "0212" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "0222" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "0222" "0222" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "0222"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "0222" "012" "0222" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "0142" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "0312" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "0312" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "0312" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "0142" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "0141" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "0312" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "0221" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "0142" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "0223" "012" "0141" "012" "0221" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "0141" "012" "012" "013"
#> [1200] "012" "012" "0141" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "0141" "0222" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "0221" "012" "0223" "012" "012" "012" "0141" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "0141" "012" "0333" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "0221" "0221" "0221" "0223" "012" "012" "012" "012" "012" "0311"
#> [1266] "0141" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "0212" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "0223" "0331" "0221" "013" "0333" "0212" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "012" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "0222" "013" "013" "0223" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "0223" "013" "013" "013" "013" "013" "013" "0212" "0212" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "0333" "0221"
#> [1453] "013" "013" "0221" "013" "013" "013" "0223" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "0333" "012" "0313"
#> [1475] "0333" "0313" "011" "0212" "013" "0221" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "011" "0231" "0141"
#> [1497] "013" "011" "013" "0233" "012" "0141" "013" "011" "012" "0321" "013"
#> [1508] "0222" "013" "0223" "012" "013" "012" "013" "013" "012" "013" "0221"
#> [1519] "0331" "0221" "0233" "0233" "0142" "0221" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "012" "0223" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "0142" "0212" "0233" "013" "012" "012"
#> [1563] "0143" "0312" "0223" "011" "013" "0333" "013" "012" "013" "0223" "0212"
#> [1574] "0142" "0212" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "0222" "0331"
#> [1596] "02113" "0212" "0233" "013" "02113" "013" "0332" "013" "0212" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "0222" "02113" "0233" "013" "0331" "011" "0212" "012" "013"
#> [1629] "012" "013" "0141" "012" "0323" "012" "0212" "012" "012" "012" "0221"
#> [1640] "012" "012" "013" "012" "0233" "0141" "012" "0141" "012" "012" "012"
#> [1651] "0142" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "0141" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "0141" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "0212" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "0312" "012" "0221" "012" "0221" "012" "011" "012" "0142" "0312"
#> [1761] "0223" "012" "011" "012" "011" "0221" "0311" "011" "013" "0221" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "0223" "011" "012" "0313" "0223" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "0212" "0223" "0311" "0221" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "0223" "012" "012" "0323" "012" "012" "0212" "0223"
#> [1838] "012" "011" "012" "012" "012" "0212" "012" "012" "0143" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "0312" "012" "012" "0332" "0223" "0312" "0321" "0323" "0212" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "0312" "012" "012" "011" "0221"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "0312" "013" "0323" "012"
#> [1904] "0311" "012" "012" "0332" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "0312" "0223" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "0333" "012" "012" "0323" "0323" "012" "0323" "012" "0332" "0222" "0312"
#> [1948] "0323" "012" "0312" "0323" "012" "012" "0212" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "0221" "0323" "0323" "0222" "012" "0323" "02112" "0331" "0323"
#> [1970] "0312" "012" "0233" "0312" "0323" "0212" "0311" "012" "0212" "0323" "0212"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "0212" "0212" "0212" "02112" "0221"
#> [1992] "0212" "0231" "0232" "0223" "0212" "0231" "0231" "02112" "0231" "0223" "02113"
#> [2003] "02112" "0232" "02112" "0222" "0221" "0212" "0232" "0232" "0212" "0231" "0212"
#> [2014] "0231" "0142" "0221" "0231" "0321" "0223" "02112" "0212" "0222" "0223" "0221"
#> [2025] "0222" "0321" "0223" "0212" "0212" "0223" "0222" "0212" "0223" "0232" "0221"
#> [2036] "02113" "0221" "02112" "0223" "0223" "0221" "0321" "02112" "0233" "0232" "02113"
#> [2047] "0212" "0212" "0212" "0142" "0221" "02113" "0231" "02113" "02112" "0212" "0223"
#> [2058] "0212" "0321" "0223" "02112" "0223" "0223" "0212" "0221" "0223" "0212" "0212"
#> [2069] "0212" "02112" "02112" "0223" "0232" "0222" "02113" "0233" "02112" "0222" "02112"
#> [2080] "02112" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0232" "0212" "0221"
#> [2091] "0212" "0223" "0223" "0212" "0223" "0223" "0223" "0212" "0223" "0231" "0212"
#> [2102] "0212" "0212" "0212" "02112" "02112" "0212" "02112" "0231" "02112" "0231" "0223"
#> [2113] "02112" "02112" "02112" "02112" "0223" "0212" "0231" "0232" "0212" "0212" "0233"
#> [2124] "0232" "0142" "0223" "0212" "0142" "02112" "02112" "0212" "0212" "02112" "02112"
#> [2135] "02112" "0212" "0212" "0212" "0221" "0212" "0222" "0223" "0212" "0221" "0221"
#> [2146] "0222" "0223" "0212" "0223" "0212" "02112" "0212" "0223" "0223" "0212" "02112"
#> [2157] "0212" "0223" "0223" "02112" "0221" "0223" "0221" "0212" "0223" "0223" "0221"
#> [2168] "0212" "0223" "0223" "0223" "0221" "0212" "0321" "0221" "0221" "0221" "02111"
#> [2179] "0212" "0212" "0212" "0223" "0234" "0222" "0223" "0221" "0221" "0221" "0221"
#> [2190] "0143" "0221" "0142" "0221" "0312" "0221" "0321" "0221" "02113" "02112" "0221"
#> [2201] "0232" "0231" "0223" "0232" "0232" "0222" "0212" "0212" "0212" "0231" "0232"
#> [2212] "0221" "0232" "0223" "0212" "0212" "02112" "02112" "0212" "0212" "0223" "0212"
#> [2223] "0212" "0212" "0221" "02112" "02112" "0212" "0223" "0212" "0223" "0223" "0223"
#> [2234] "0223" "0212" "0221" "0321" "0222" "0221" "0321" "0221" "0321" "0223" "0221"
#> [2245] "0223" "0223" "0223" "0231" "0231" "0221" "0222" "0321" "0222" "0221" "0231"
#> [2256] "0231" "0221" "0221" "0141" "0321" "02112" "0221" "0221" "0221" "0223" "0321"
#> [2267] "0231" "0221" "0321" "0223" "0223" "0223" "0142" "0223" "0142" "0222" "0223"
#> [2278] "0321" "0221" "0231" "0222" "0221" "0141" "0222" "0221" "0221" "0142" "0321"
#> [2289] "0321" "0221" "0221" "0321" "0221" "0221" "0142" "0221" "0221" "0221" "0141"
#> [2300] "0321" "0142" "0142" "0141" "0223" "0142" "0222" "0142" "0142" "0142" "0223"
#> [2311] "0142" "0321" "0221" "0142" "0141" "0141" "012" "0212" "0231" "0221" "0142"
#> [2322] "0221" "0223" "0321" "0221" "0221" "0221" "0221" "0221" "0221" "0223" "0221"
#> [2333] "0221" "0223" "0321" "0142" "0141" "0321" "0221" "0141" "0321" "0321" "0221"
#> [2344] "0212" "0232" "0223" "0223" "0223" "0221" "0221" "0321" "0222" "0223" "0223"
#> [2355] "0221" "0221" "0321" "0212" "02112" "0221" "0212" "0221" "0212" "0234" "0212"
#> [2366] "0212" "0221" "02112" "02112" "0221" "0223" "0223" "0212" "0223" "0212" "0223"
#> [2377] "0221" "0221" "0212" "0212" "0232" "0223" "02112" "0212" "0232" "0221" "0223"
#> [2388] "0223" "0223" "0231" "02113" "0223" "0221" "0221" "02111" "0212" "0212" "0223"
#> [2399] "0321" "0221" "0141" "0141" "0141" "0212" "0221" "0231" "02111" "0223" "0212"
#> [2410] "0222" "0212" "0221" "0212" "0142" "0221" "0223" "0221" "0223" "0231" "012"
#> [2421] "0223" "0221" "0321" "0212" "0212" "0231" "0223" "0221" "0223" "0223" "0221"
#> [2432] "0221" "0141" "0321" "0141" "0221" "0321" "0321" "0321" "0141" "0141" "012"
#> [2443] "0321" "0321" "0321" "0223" "0223" "0221" "0212" "0223" "0212" "0212" "0212"
#> [2454] "02112" "0212" "0212" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0212"
#> [2465] "0212" "0212" "0212" "0212" "0222" "0221" "0321" "0221" "0221" "0221" "0212"
#> [2476] "0212" "0223" "0212" "0223" "0221" "0223" "0212" "0223" "0223" "02112" "0223"
#> [2487] "0212" "0212" "0212" "02112" "0212" "0212" "0212" "02112" "0223" "0212" "0223"
#> [2498] "0212" "0212" "0221" "0212" "0223" "0212" "0223" "0223" "0221" "0223" "0321"
#> [2509] "0321" "0221" "0324" "0212" "0212" "02112" "0212" "0212" "02112" "0212" "0221"
#> [2520] "0212" "0212" "02112" "0221" "0222" "0221" "0212" "02112" "0221" "0212" "02113"
#> [2531] "0223" "0212" "02112" "0141" "0212" "0321" "0221" "0221" "0221" "0231" "0221"
#> [2542] "0221" "0221" "0221" "0232" "0221" "0221" "0223" "0142" "0221" "0321" "0321"
#> [2553] "0142" "0141" "0212" "0321" "0221" "0141" "02112" "0212" "0321" "0212" "0321"
#> [2564] "0223" "0221" "0321" "0221" "0221" "0221" "0221" "0223" "0142" "0141" "0141"
#> [2575] "0321" "0321" "0221" "0221" "02112" "0212" "0212" "0223" "0223" "0221" "0221"
#> [2586] "0222" "0221" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "0221" "0223" "0321" "0221" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "0222" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "0212" "02112" "02112" "0223" "0212" "0223" "0223" "0221"
#> [2641] "0221" "0321" "0221" "0221" "0212" "0221" "0221" "0223" "0321" "0221" "012"
#> [2652] "0221" "0221" "0221" "0221" "0221" "0231" "0221" "0222" "0221" "0221" "0221"
#> [2663] "0221" "0321" "0321" "0221" "0321" "0221" "0221" "0321" "0221" "0141" "0321"
#> [2674] "0221" "0321" "0221" "0221" "0324" "012" "0141" "012" "0221" "0141" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "0212" "0212" "0234" "0231" "0143"
#> [2696] "0221" "0324" "0212" "0221" "0321" "0221" "0212" "0141" "0222" "0222" "0321"
#> [2707] "0142" "0222" "0141" "0142" "0222" "0141" "0141" "0231" "0222" "0231" "0141"
#> [2718] "0142" "0231" "0141" "0223" "0222" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "012" "0321" "0212" "0221" "0321" "0221" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "0212" "0221" "0221" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "0221" "0221" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "0221" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "0223" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "0221" "0142" "0142" "0142" "0221" "0212" "0231" "02111" "02111" "0212" "02111"
#> [2828] "0212" "02111" "0223" "0212" "0212" "02111" "0212" "0231" "0223" "0212" "0212"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "0212" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "0221" "0212" "0141" "0221" "02112"
#> [2861] "0231" "0232" "0223" "0223" "0212" "0212" "0231" "0221" "0212" "0221" "02111"
#> [2872] "0212" "0212" "02111" "02111" "0212" "0223" "0212" "0142" "0212" "0212"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1908))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "0312" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "0312" "0311" "011" "022" "011" "0311"
#> [45] "012" "012" "0311" "0312" "0322" "011" "0312" "011" "011" "012" "012"
#> [56] "0212" "0212" "0143" "011" "0313" "0322" "011" "011" "022" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "0143" "011"
#> [78] "011" "022" "011" "0143" "0322" "011" "0143" "011" "022" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "0143"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "022" "0141" "0142" "011"
#> [111] "011" "011" "011" "0143" "011" "022" "022" "0322" "011" "0321" "0313"
#> [122] "0322" "022" "022" "022" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "022" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "022" "011" "0212" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "022"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "022" "022" "0313"
#> [177] "0313" "011" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "022" "0322" "011" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "022" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "022" "0312" "011"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "011" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "011" "0312" "0312" "0312" "022" "0312" "0313" "011" "0313" "0312"
#> [254] "0312" "0313" "0313" "011" "0312" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "0212" "011" "0313" "0313" "0313" "022"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "022" "0312"
#> [309] "022" "0312" "0312" "0313" "022" "0313" "0313" "0312" "011" "0313" "0313"
#> [320] "011" "0313" "011" "0312" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "022" "022" "011" "0313" "011" "0313"
#> [342] "0313" "022" "0313" "0313" "0313" "022" "0311" "0311" "022" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "011" "011" "022" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "022" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "0143" "0311" "011" "022" "011"
#> [408] "0311" "011" "011" "0311" "0311" "0212" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "022" "011" "0212" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "0312" "0312" "0312" "0311"
#> [452] "011" "0311" "011" "011" "0312" "011" "011" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "0212" "0312" "0311" "0311" "022" "022" "0212" "0312" "0312" "022" "0312"
#> [496] "011" "0212" "02113" "011" "0312" "02113" "011" "0311" "0312" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "022" "0311" "0311" "011" "011" "022"
#> [518] "0311" "0312" "0311" "011" "011" "011" "0312" "0312" "0313" "0312" "011"
#> [529] "011" "022" "0212" "0212" "011" "022" "011" "011" "011" "0212" "011"
#> [540] "0311" "011" "022" "011" "011" "022" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "0143" "011" "0311" "0311" "0143" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "022" "0311" "011" "011" "022"
#> [573] "0324" "0311" "011" "0312" "011" "011" "011" "022" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "0312"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "022" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "0312" "011" "011" "022"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "0312" "0311" "0311" "0311" "0311" "022"
#> [672] "011" "022" "0311" "0313" "0234" "0311" "0311" "022" "011" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "011" "0312"
#> [694] "0311" "022" "0311" "0312" "011" "0312" "0143" "0312" "011" "011" "0311"
#> [705] "0311" "022" "022" "011" "0324" "011" "0324" "0212" "011" "011" "011"
#> [716] "011" "011" "011" "022" "0311" "0311" "022" "0234" "011" "022" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "0312"
#> [749] "0312" "0312" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "0212" "022" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "0312" "011" "011" "0311" "011" "022" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "0312" "011" "0311" "0311" "011" "011"
#> [793] "0312" "022" "011" "0312" "011" "011" "0234" "0311" "0312" "022" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "0143" "011" "011" "011" "0212" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "022" "011" "0143" "0143" "0143"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "0143" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "0212" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "022" "022" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "022" "011" "0143" "011" "011" "011" "022"
#> [936] "0324" "011" "022" "011" "011" "011" "0143" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "0212" "0143"
#> [958] "0143" "011" "0212" "022" "011" "011" "0212" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "022" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "022" "022" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "022"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "022" "012" "022" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "0142" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "0312" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "0312" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "0312" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "0142" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "0141" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "0312" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "022" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "0142" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "022" "012" "0141" "012" "022" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "0141" "012" "012" "013"
#> [1200] "012" "012" "0141" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "0141" "022" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "022" "012" "022" "012" "012" "012" "0141" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "0141" "012" "0333" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "022" "022" "022" "022" "012" "012" "012" "012" "012" "0311"
#> [1266] "0141" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "0212" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "022" "0331" "022" "013" "0333" "0212" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "012" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "022" "013" "013" "022" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "022" "013" "013" "013" "013" "013" "013" "0212" "0212" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "0141" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "0333" "022"
#> [1453] "013" "013" "022" "013" "013" "013" "022" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "0333" "012" "0313"
#> [1475] "0333" "0313" "011" "0212" "013" "022" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "0141" "013" "0141" "013" "013" "011" "0231" "0141"
#> [1497] "013" "011" "013" "0233" "012" "0141" "013" "011" "012" "0321" "013"
#> [1508] "022" "013" "022" "012" "013" "012" "013" "013" "012" "013" "022"
#> [1519] "0331" "022" "0233" "0233" "0142" "022" "0142" "013" "0333" "013" "013"
#> [1530] "013" "0142" "013" "013" "02113" "012" "022" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "0142" "0212" "0233" "013" "012" "012"
#> [1563] "0143" "0312" "022" "011" "013" "0333" "013" "012" "013" "022" "0212"
#> [1574] "0142" "0212" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "022" "0331"
#> [1596] "02113" "0212" "0233" "013" "02113" "013" "0332" "013" "0212" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "022" "02113" "0233" "013" "0331" "011" "0212" "012" "013"
#> [1629] "012" "013" "0141" "012" "0323" "012" "0212" "012" "012" "012" "022"
#> [1640] "012" "012" "013" "012" "0233" "0141" "012" "0141" "012" "012" "012"
#> [1651] "0142" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "0141" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "0141" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "0212" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "0312" "012" "022" "012" "022" "012" "011" "012" "0142" "0312"
#> [1761] "022" "012" "011" "012" "011" "022" "0311" "011" "013" "022" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "022" "011" "012" "0313" "022" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "0212" "022" "0311" "022" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "022" "012" "012" "0323" "012" "012" "0212" "022"
#> [1838] "012" "011" "012" "012" "012" "0212" "012" "012" "0143" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "0312" "012" "012" "0332" "022" "0312" "0321" "0323" "0212" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "0312" "012" "012" "011" "022"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "0312" "013" "0323" "012"
#> [1904] "0311" "012" "012" "0332" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "0312" "022" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "0333" "012" "012" "0323" "0323" "012" "0323" "012" "0332" "022" "0312"
#> [1948] "0323" "012" "0312" "0323" "012" "012" "0212" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "022" "0323" "0323" "022" "012" "0323" "02112" "0331" "0323"
#> [1970] "0312" "012" "0233" "0312" "0323" "0212" "0311" "012" "0212" "0323" "0212"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "0212" "0212" "0212" "02112" "022"
#> [1992] "0212" "0231" "0232" "022" "0212" "0231" "0231" "02112" "0231" "022" "02113"
#> [2003] "02112" "0232" "02112" "022" "022" "0212" "0232" "0232" "0212" "0231" "0212"
#> [2014] "0231" "0142" "022" "0231" "0321" "022" "02112" "0212" "022" "022" "022"
#> [2025] "022" "0321" "022" "0212" "0212" "022" "022" "0212" "022" "0232" "022"
#> [2036] "02113" "022" "02112" "022" "022" "022" "0321" "02112" "0233" "0232" "02113"
#> [2047] "0212" "0212" "0212" "0142" "022" "02113" "0231" "02113" "02112" "0212" "022"
#> [2058] "0212" "0321" "022" "02112" "022" "022" "0212" "022" "022" "0212" "0212"
#> [2069] "0212" "02112" "02112" "022" "0232" "022" "02113" "0233" "02112" "022" "02112"
#> [2080] "02112" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0232" "0212" "022"
#> [2091] "0212" "022" "022" "0212" "022" "022" "022" "0212" "022" "0231" "0212"
#> [2102] "0212" "0212" "0212" "02112" "02112" "0212" "02112" "0231" "02112" "0231" "022"
#> [2113] "02112" "02112" "02112" "02112" "022" "0212" "0231" "0232" "0212" "0212" "0233"
#> [2124] "0232" "0142" "022" "0212" "0142" "02112" "02112" "0212" "0212" "02112" "02112"
#> [2135] "02112" "0212" "0212" "0212" "022" "0212" "022" "022" "0212" "022" "022"
#> [2146] "022" "022" "0212" "022" "0212" "02112" "0212" "022" "022" "0212" "02112"
#> [2157] "0212" "022" "022" "02112" "022" "022" "022" "0212" "022" "022" "022"
#> [2168] "0212" "022" "022" "022" "022" "0212" "0321" "022" "022" "022" "02111"
#> [2179] "0212" "0212" "0212" "022" "0234" "022" "022" "022" "022" "022" "022"
#> [2190] "0143" "022" "0142" "022" "0312" "022" "0321" "022" "02113" "02112" "022"
#> [2201] "0232" "0231" "022" "0232" "0232" "022" "0212" "0212" "0212" "0231" "0232"
#> [2212] "022" "0232" "022" "0212" "0212" "02112" "02112" "0212" "0212" "022" "0212"
#> [2223] "0212" "0212" "022" "02112" "02112" "0212" "022" "0212" "022" "022" "022"
#> [2234] "022" "0212" "022" "0321" "022" "022" "0321" "022" "0321" "022" "022"
#> [2245] "022" "022" "022" "0231" "0231" "022" "022" "0321" "022" "022" "0231"
#> [2256] "0231" "022" "022" "0141" "0321" "02112" "022" "022" "022" "022" "0321"
#> [2267] "0231" "022" "0321" "022" "022" "022" "0142" "022" "0142" "022" "022"
#> [2278] "0321" "022" "0231" "022" "022" "0141" "022" "022" "022" "0142" "0321"
#> [2289] "0321" "022" "022" "0321" "022" "022" "0142" "022" "022" "022" "0141"
#> [2300] "0321" "0142" "0142" "0141" "022" "0142" "022" "0142" "0142" "0142" "022"
#> [2311] "0142" "0321" "022" "0142" "0141" "0141" "012" "0212" "0231" "022" "0142"
#> [2322] "022" "022" "0321" "022" "022" "022" "022" "022" "022" "022" "022"
#> [2333] "022" "022" "0321" "0142" "0141" "0321" "022" "0141" "0321" "0321" "022"
#> [2344] "0212" "0232" "022" "022" "022" "022" "022" "0321" "022" "022" "022"
#> [2355] "022" "022" "0321" "0212" "02112" "022" "0212" "022" "0212" "0234" "0212"
#> [2366] "0212" "022" "02112" "02112" "022" "022" "022" "0212" "022" "0212" "022"
#> [2377] "022" "022" "0212" "0212" "0232" "022" "02112" "0212" "0232" "022" "022"
#> [2388] "022" "022" "0231" "02113" "022" "022" "022" "02111" "0212" "0212" "022"
#> [2399] "0321" "022" "0141" "0141" "0141" "0212" "022" "0231" "02111" "022" "0212"
#> [2410] "022" "0212" "022" "0212" "0142" "022" "022" "022" "022" "0231" "012"
#> [2421] "022" "022" "0321" "0212" "0212" "0231" "022" "022" "022" "022" "022"
#> [2432] "022" "0141" "0321" "0141" "022" "0321" "0321" "0321" "0141" "0141" "012"
#> [2443] "0321" "0321" "0321" "022" "022" "022" "0212" "022" "0212" "0212" "0212"
#> [2454] "02112" "0212" "0212" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0212"
#> [2465] "0212" "0212" "0212" "0212" "022" "022" "0321" "022" "022" "022" "0212"
#> [2476] "0212" "022" "0212" "022" "022" "022" "0212" "022" "022" "02112" "022"
#> [2487] "0212" "0212" "0212" "02112" "0212" "0212" "0212" "02112" "022" "0212" "022"
#> [2498] "0212" "0212" "022" "0212" "022" "0212" "022" "022" "022" "022" "0321"
#> [2509] "0321" "022" "0324" "0212" "0212" "02112" "0212" "0212" "02112" "0212" "022"
#> [2520] "0212" "0212" "02112" "022" "022" "022" "0212" "02112" "022" "0212" "02113"
#> [2531] "022" "0212" "02112" "0141" "0212" "0321" "022" "022" "022" "0231" "022"
#> [2542] "022" "022" "022" "0232" "022" "022" "022" "0142" "022" "0321" "0321"
#> [2553] "0142" "0141" "0212" "0321" "022" "0141" "02112" "0212" "0321" "0212" "0321"
#> [2564] "022" "022" "0321" "022" "022" "022" "022" "022" "0142" "0141" "0141"
#> [2575] "0321" "0321" "022" "022" "02112" "0212" "0212" "022" "022" "022" "022"
#> [2586] "022" "022" "0142" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "0142" "0232" "02112" "0231" "022" "022" "0321" "022" "0231"
#> [2619] "0231" "0234" "0233" "0232" "0142" "02112" "022" "0231" "0142" "0142" "0141"
#> [2630] "0231" "02112" "02112" "0212" "02112" "02112" "022" "0212" "022" "022" "022"
#> [2641] "022" "0321" "022" "022" "0212" "022" "022" "022" "0321" "022" "012"
#> [2652] "022" "022" "022" "022" "022" "0231" "022" "022" "022" "022" "022"
#> [2663] "022" "0321" "0321" "022" "0321" "022" "022" "0321" "022" "0141" "0321"
#> [2674] "022" "0321" "022" "022" "0324" "012" "0141" "012" "022" "0141" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "0212" "0212" "0234" "0231" "0143"
#> [2696] "022" "0324" "0212" "022" "0321" "022" "0212" "0141" "022" "022" "0321"
#> [2707] "0142" "022" "0141" "0142" "022" "0141" "0141" "0231" "022" "0231" "0141"
#> [2718] "0142" "0231" "0141" "022" "022" "0141" "02112" "0321" "0141" "0321" "0141"
#> [2729] "012" "0321" "0212" "022" "0321" "022" "0321" "0141" "0141" "0321" "0141"
#> [2740] "0321" "0141" "0141" "0212" "022" "022" "0141" "0141" "0141" "0142" "0321"
#> [2751] "0141" "0141" "022" "022" "0321" "0323" "0142" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "0142" "0142" "022" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "0142" "0142" "022" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "022" "0142" "0142" "0142" "022" "0212" "0231" "02111" "02111" "0212" "02111"
#> [2828] "0212" "02111" "022" "0212" "0212" "02111" "0212" "0231" "022" "0212" "0212"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "0212" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "0142" "022" "0212" "0141" "022" "02112"
#> [2861] "0231" "0232" "022" "022" "0212" "0212" "0231" "022" "0212" "022" "02111"
#> [2872] "0212" "0212" "02111" "02111" "0212" "022" "0212" "0142" "0212" "0212"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2292))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "0312" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "0312" "0311" "011" "022" "011" "0311"
#> [45] "012" "012" "0311" "0312" "0322" "011" "0312" "011" "011" "012" "012"
#> [56] "0212" "0212" "014" "011" "0313" "0322" "011" "011" "022" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "014" "011"
#> [78] "011" "022" "011" "014" "0322" "011" "014" "011" "022" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "014"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "022" "014" "014" "011"
#> [111] "011" "011" "011" "014" "011" "022" "022" "0322" "011" "0321" "0313"
#> [122] "0322" "022" "022" "022" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "022" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "022" "011" "0212" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "022"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "022" "022" "0313"
#> [177] "0313" "011" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "022" "0322" "011" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "022" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "022" "0312" "011"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "011" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "011" "0312" "0312" "0312" "022" "0312" "0313" "011" "0313" "0312"
#> [254] "0312" "0313" "0313" "011" "0312" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "0212" "011" "0313" "0313" "0313" "022"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "022" "0312"
#> [309] "022" "0312" "0312" "0313" "022" "0313" "0313" "0312" "011" "0313" "0313"
#> [320] "011" "0313" "011" "0312" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "022" "022" "011" "0313" "011" "0313"
#> [342] "0313" "022" "0313" "0313" "0313" "022" "0311" "0311" "022" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "011" "011" "022" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "022" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "014" "0311" "011" "022" "011"
#> [408] "0311" "011" "011" "0311" "0311" "0212" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "022" "011" "0212" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "0312" "0312" "0312" "0311"
#> [452] "011" "0311" "011" "011" "0312" "011" "011" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "0212" "0312" "0311" "0311" "022" "022" "0212" "0312" "0312" "022" "0312"
#> [496] "011" "0212" "02113" "011" "0312" "02113" "011" "0311" "0312" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "022" "0311" "0311" "011" "011" "022"
#> [518] "0311" "0312" "0311" "011" "011" "011" "0312" "0312" "0313" "0312" "011"
#> [529] "011" "022" "0212" "0212" "011" "022" "011" "011" "011" "0212" "011"
#> [540] "0311" "011" "022" "011" "011" "022" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "014" "011" "0311" "0311" "014" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "022" "0311" "011" "011" "022"
#> [573] "0324" "0311" "011" "0312" "011" "011" "011" "022" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "0312"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "022" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "0312" "011" "011" "022"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "0312" "0311" "0311" "0311" "0311" "022"
#> [672] "011" "022" "0311" "0313" "0234" "0311" "0311" "022" "011" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "011" "0312"
#> [694] "0311" "022" "0311" "0312" "011" "0312" "014" "0312" "011" "011" "0311"
#> [705] "0311" "022" "022" "011" "0324" "011" "0324" "0212" "011" "011" "011"
#> [716] "011" "011" "011" "022" "0311" "0311" "022" "0234" "011" "022" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "0312"
#> [749] "0312" "0312" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "0212" "022" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "0312" "011" "011" "0311" "011" "022" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "0312" "011" "0311" "0311" "011" "011"
#> [793] "0312" "022" "011" "0312" "011" "011" "0234" "0311" "0312" "022" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "014" "011" "011" "011" "0212" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "022" "011" "014" "014" "014"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "014" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "0212" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "022" "022" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "022" "011" "014" "011" "011" "011" "022"
#> [936] "0324" "011" "022" "011" "011" "011" "014" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "0212" "014"
#> [958] "014" "011" "0212" "022" "011" "011" "0212" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "022" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "022" "022" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "022"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "022" "012" "022" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "014" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "0312" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "0312" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "0312" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "014" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "014" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "0312" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "022" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "014" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "022" "012" "014" "012" "022" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "014" "012" "012" "013"
#> [1200] "012" "012" "014" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "014" "022" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "022" "012" "022" "012" "012" "012" "014" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "014" "012" "0333" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "022" "022" "022" "022" "012" "012" "012" "012" "012" "0311"
#> [1266] "014" "011" "011" "013" "013" "013" "013" "0331" "013" "013" "013"
#> [1277] "0333" "0212" "0332" "0332" "0331" "013" "0332" "013" "013" "013" "013"
#> [1288] "013" "0332" "0332" "013" "0331" "013" "013" "0233" "0333" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "022" "0331" "022" "013" "0333" "0212" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "0333" "012" "013" "013" "013"
#> [1332] "02113" "013" "0331" "013" "0333" "013" "013" "013" "013" "013" "013"
#> [1343] "022" "013" "013" "022" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "022" "013" "013" "013" "013" "013" "013" "0212" "0212" "013" "013"
#> [1365] "0331" "0331" "013" "0331" "013" "0331" "0331" "0331" "0332" "013" "0331"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "014" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "0333" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "0333"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "0333" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "0333" "022"
#> [1453] "013" "013" "022" "013" "013" "013" "022" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "0333" "012" "0313"
#> [1475] "0333" "0313" "011" "0212" "013" "022" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "014" "013" "014" "013" "013" "011" "0231" "014"
#> [1497] "013" "011" "013" "0233" "012" "014" "013" "011" "012" "0321" "013"
#> [1508] "022" "013" "022" "012" "013" "012" "013" "013" "012" "013" "022"
#> [1519] "0331" "022" "0233" "0233" "014" "022" "014" "013" "0333" "013" "013"
#> [1530] "013" "014" "013" "013" "02113" "012" "022" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "014" "0212" "0233" "013" "012" "012"
#> [1563] "014" "0312" "022" "011" "013" "0333" "013" "012" "013" "022" "0212"
#> [1574] "014" "0212" "0332" "0332" "02113" "0233" "0233" "0332" "02113" "0332" "0233"
#> [1585] "0332" "0332" "0331" "0332" "0331" "0332" "013" "0331" "0332" "022" "0331"
#> [1596] "02113" "0212" "0233" "013" "02113" "013" "0332" "013" "0212" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "0331" "0331" "0332" "0331" "0331" "0331" "0331"
#> [1618] "02113" "013" "022" "02113" "0233" "013" "0331" "011" "0212" "012" "013"
#> [1629] "012" "013" "014" "012" "0323" "012" "0212" "012" "012" "012" "022"
#> [1640] "012" "012" "013" "012" "0233" "014" "012" "014" "012" "012" "012"
#> [1651] "014" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "014" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "014" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "0212" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "0312" "012" "022" "012" "022" "012" "011" "012" "014" "0312"
#> [1761] "022" "012" "011" "012" "011" "022" "0311" "011" "013" "022" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "022" "011" "012" "0313" "022" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "0212" "022" "0311" "022" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "022" "012" "012" "0323" "012" "012" "0212" "022"
#> [1838] "012" "011" "012" "012" "012" "0212" "012" "012" "014" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "0312" "012" "012" "0332" "022" "0312" "0321" "0323" "0212" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "0312" "012" "012" "011" "022"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "0312" "013" "0323" "012"
#> [1904] "0311" "012" "012" "0332" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "0312" "022" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "0333" "012" "012" "0323" "0323" "012" "0323" "012" "0332" "022" "0312"
#> [1948] "0323" "012" "0312" "0323" "012" "012" "0212" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "022" "0323" "0323" "022" "012" "0323" "02112" "0331" "0323"
#> [1970] "0312" "012" "0233" "0312" "0323" "0212" "0311" "012" "0212" "0323" "0212"
#> [1981] "0323" "0323" "0332" "0232" "02112" "0232" "0212" "0212" "0212" "02112" "022"
#> [1992] "0212" "0231" "0232" "022" "0212" "0231" "0231" "02112" "0231" "022" "02113"
#> [2003] "02112" "0232" "02112" "022" "022" "0212" "0232" "0232" "0212" "0231" "0212"
#> [2014] "0231" "014" "022" "0231" "0321" "022" "02112" "0212" "022" "022" "022"
#> [2025] "022" "0321" "022" "0212" "0212" "022" "022" "0212" "022" "0232" "022"
#> [2036] "02113" "022" "02112" "022" "022" "022" "0321" "02112" "0233" "0232" "02113"
#> [2047] "0212" "0212" "0212" "014" "022" "02113" "0231" "02113" "02112" "0212" "022"
#> [2058] "0212" "0321" "022" "02112" "022" "022" "0212" "022" "022" "0212" "0212"
#> [2069] "0212" "02112" "02112" "022" "0232" "022" "02113" "0233" "02112" "022" "02112"
#> [2080] "02112" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0232" "0212" "022"
#> [2091] "0212" "022" "022" "0212" "022" "022" "022" "0212" "022" "0231" "0212"
#> [2102] "0212" "0212" "0212" "02112" "02112" "0212" "02112" "0231" "02112" "0231" "022"
#> [2113] "02112" "02112" "02112" "02112" "022" "0212" "0231" "0232" "0212" "0212" "0233"
#> [2124] "0232" "014" "022" "0212" "014" "02112" "02112" "0212" "0212" "02112" "02112"
#> [2135] "02112" "0212" "0212" "0212" "022" "0212" "022" "022" "0212" "022" "022"
#> [2146] "022" "022" "0212" "022" "0212" "02112" "0212" "022" "022" "0212" "02112"
#> [2157] "0212" "022" "022" "02112" "022" "022" "022" "0212" "022" "022" "022"
#> [2168] "0212" "022" "022" "022" "022" "0212" "0321" "022" "022" "022" "02111"
#> [2179] "0212" "0212" "0212" "022" "0234" "022" "022" "022" "022" "022" "022"
#> [2190] "014" "022" "014" "022" "0312" "022" "0321" "022" "02113" "02112" "022"
#> [2201] "0232" "0231" "022" "0232" "0232" "022" "0212" "0212" "0212" "0231" "0232"
#> [2212] "022" "0232" "022" "0212" "0212" "02112" "02112" "0212" "0212" "022" "0212"
#> [2223] "0212" "0212" "022" "02112" "02112" "0212" "022" "0212" "022" "022" "022"
#> [2234] "022" "0212" "022" "0321" "022" "022" "0321" "022" "0321" "022" "022"
#> [2245] "022" "022" "022" "0231" "0231" "022" "022" "0321" "022" "022" "0231"
#> [2256] "0231" "022" "022" "014" "0321" "02112" "022" "022" "022" "022" "0321"
#> [2267] "0231" "022" "0321" "022" "022" "022" "014" "022" "014" "022" "022"
#> [2278] "0321" "022" "0231" "022" "022" "014" "022" "022" "022" "014" "0321"
#> [2289] "0321" "022" "022" "0321" "022" "022" "014" "022" "022" "022" "014"
#> [2300] "0321" "014" "014" "014" "022" "014" "022" "014" "014" "014" "022"
#> [2311] "014" "0321" "022" "014" "014" "014" "012" "0212" "0231" "022" "014"
#> [2322] "022" "022" "0321" "022" "022" "022" "022" "022" "022" "022" "022"
#> [2333] "022" "022" "0321" "014" "014" "0321" "022" "014" "0321" "0321" "022"
#> [2344] "0212" "0232" "022" "022" "022" "022" "022" "0321" "022" "022" "022"
#> [2355] "022" "022" "0321" "0212" "02112" "022" "0212" "022" "0212" "0234" "0212"
#> [2366] "0212" "022" "02112" "02112" "022" "022" "022" "0212" "022" "0212" "022"
#> [2377] "022" "022" "0212" "0212" "0232" "022" "02112" "0212" "0232" "022" "022"
#> [2388] "022" "022" "0231" "02113" "022" "022" "022" "02111" "0212" "0212" "022"
#> [2399] "0321" "022" "014" "014" "014" "0212" "022" "0231" "02111" "022" "0212"
#> [2410] "022" "0212" "022" "0212" "014" "022" "022" "022" "022" "0231" "012"
#> [2421] "022" "022" "0321" "0212" "0212" "0231" "022" "022" "022" "022" "022"
#> [2432] "022" "014" "0321" "014" "022" "0321" "0321" "0321" "014" "014" "012"
#> [2443] "0321" "0321" "0321" "022" "022" "022" "0212" "022" "0212" "0212" "0212"
#> [2454] "02112" "0212" "0212" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0212"
#> [2465] "0212" "0212" "0212" "0212" "022" "022" "0321" "022" "022" "022" "0212"
#> [2476] "0212" "022" "0212" "022" "022" "022" "0212" "022" "022" "02112" "022"
#> [2487] "0212" "0212" "0212" "02112" "0212" "0212" "0212" "02112" "022" "0212" "022"
#> [2498] "0212" "0212" "022" "0212" "022" "0212" "022" "022" "022" "022" "0321"
#> [2509] "0321" "022" "0324" "0212" "0212" "02112" "0212" "0212" "02112" "0212" "022"
#> [2520] "0212" "0212" "02112" "022" "022" "022" "0212" "02112" "022" "0212" "02113"
#> [2531] "022" "0212" "02112" "014" "0212" "0321" "022" "022" "022" "0231" "022"
#> [2542] "022" "022" "022" "0232" "022" "022" "022" "014" "022" "0321" "0321"
#> [2553] "014" "014" "0212" "0321" "022" "014" "02112" "0212" "0321" "0212" "0321"
#> [2564] "022" "022" "0321" "022" "022" "022" "022" "022" "014" "014" "014"
#> [2575] "0321" "0321" "022" "022" "02112" "0212" "0212" "022" "022" "022" "022"
#> [2586] "022" "022" "014" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "014" "0232" "02112" "0231" "022" "022" "0321" "022" "0231"
#> [2619] "0231" "0234" "0233" "0232" "014" "02112" "022" "0231" "014" "014" "014"
#> [2630] "0231" "02112" "02112" "0212" "02112" "02112" "022" "0212" "022" "022" "022"
#> [2641] "022" "0321" "022" "022" "0212" "022" "022" "022" "0321" "022" "012"
#> [2652] "022" "022" "022" "022" "022" "0231" "022" "022" "022" "022" "022"
#> [2663] "022" "0321" "0321" "022" "0321" "022" "022" "0321" "022" "014" "0321"
#> [2674] "022" "0321" "022" "022" "0324" "012" "014" "012" "022" "014" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "0212" "0212" "0234" "0231" "014"
#> [2696] "022" "0324" "0212" "022" "0321" "022" "0212" "014" "022" "022" "0321"
#> [2707] "014" "022" "014" "014" "022" "014" "014" "0231" "022" "0231" "014"
#> [2718] "014" "0231" "014" "022" "022" "014" "02112" "0321" "014" "0321" "014"
#> [2729] "012" "0321" "0212" "022" "0321" "022" "0321" "014" "014" "0321" "014"
#> [2740] "0321" "014" "014" "0212" "022" "022" "014" "014" "014" "014" "0321"
#> [2751] "014" "014" "022" "022" "0321" "0323" "014" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "014" "014" "022" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "014" "014" "022" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "022" "014" "014" "014" "022" "0212" "0231" "02111" "02111" "0212" "02111"
#> [2828] "0212" "02111" "022" "0212" "0212" "02111" "0212" "0231" "022" "0212" "0212"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "0212" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "014" "022" "0212" "014" "022" "02112"
#> [2861] "0231" "0232" "022" "022" "0212" "0212" "0231" "022" "0212" "022" "02111"
#> [2872] "0212" "0212" "02111" "02111" "0212" "022" "0212" "014" "0212" "0212"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2797))
#> [1] "012" "012" "0231" "0322" "012" "012" "0322" "011" "012" "012" "012"
#> [12] "011" "0322" "012" "012" "012" "012" "012" "0313" "011" "0322" "012"
#> [23] "011" "0322" "012" "0322" "0322" "0322" "012" "0312" "0322" "012" "0322"
#> [34] "0322" "0322" "012" "012" "012" "0312" "0311" "011" "022" "011" "0311"
#> [45] "012" "012" "0311" "0312" "0322" "011" "0312" "011" "011" "012" "012"
#> [56] "0212" "0212" "014" "011" "0313" "0322" "011" "011" "022" "0311" "011"
#> [67] "012" "012" "0322" "012" "0322" "0311" "0322" "011" "011" "014" "011"
#> [78] "011" "022" "011" "014" "0322" "011" "014" "011" "022" "011" "011"
#> [89] "011" "012" "0322" "011" "02113" "011" "011" "011" "011" "011" "014"
#> [100] "0313" "011" "011" "011" "011" "011" "0322" "022" "014" "014" "011"
#> [111] "011" "011" "011" "014" "011" "022" "022" "0322" "011" "0321" "0313"
#> [122] "0322" "022" "022" "022" "0234" "012" "011" "011" "011" "012" "011"
#> [133] "011" "0324" "011" "022" "011" "011" "0322" "011" "011" "011" "012"
#> [144] "012" "012" "022" "011" "0212" "011" "0324" "0313" "0313" "011" "0313"
#> [155] "0322" "011" "0313" "0234" "0322" "0322" "0322" "011" "0313" "0313" "022"
#> [166] "011" "0322" "0313" "011" "011" "0322" "0313" "0313" "022" "022" "0313"
#> [177] "0313" "011" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "022" "0322" "011" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "022" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "022" "0312" "011"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "011" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "011" "0312" "0312" "0312" "022" "0312" "0313" "011" "0313" "0312"
#> [254] "0312" "0313" "0313" "011" "0312" "0313" "0313" "011" "0313" "011" "011"
#> [265] "011" "0313" "011" "011" "011" "0313" "011" "011" "0313" "0313" "011"
#> [276] "0313" "0313" "0313" "0313" "0322" "0212" "011" "0313" "0313" "0313" "022"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "022" "0312"
#> [309] "022" "0312" "0312" "0313" "022" "0313" "0313" "0312" "011" "0313" "0313"
#> [320] "011" "0313" "011" "0312" "0313" "0311" "011" "0313" "0313" "0313" "0313"
#> [331] "0313" "011" "011" "011" "011" "022" "022" "011" "0313" "011" "0313"
#> [342] "0313" "022" "0313" "0313" "0313" "022" "0311" "0311" "022" "0313" "011"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "011" "011" "022" "0311" "011"
#> [386] "011" "011" "0311" "0324" "0311" "011" "0311" "011" "0311" "022" "0311"
#> [397] "0313" "0311" "011" "011" "0311" "011" "014" "0311" "011" "022" "011"
#> [408] "0311" "011" "011" "0311" "0311" "0212" "011" "011" "011" "011" "011"
#> [419] "0311" "011" "011" "011" "011" "0313" "0234" "011" "011" "011" "011"
#> [430] "011" "011" "0234" "011" "0234" "011" "022" "011" "0212" "011" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "011" "011" "0312" "0312" "0312" "0311"
#> [452] "011" "0311" "011" "011" "0312" "011" "011" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "011"
#> [485] "0212" "0312" "0311" "0311" "022" "022" "0212" "0312" "0312" "022" "0312"
#> [496] "011" "0212" "02113" "011" "0312" "02113" "011" "0311" "0312" "0311" "02113"
#> [507] "011" "0311" "0311" "0311" "0311" "022" "0311" "0311" "011" "011" "022"
#> [518] "0311" "0312" "0311" "011" "011" "011" "0312" "0312" "0313" "0312" "011"
#> [529] "011" "022" "0212" "0212" "011" "022" "011" "011" "011" "0212" "011"
#> [540] "0311" "011" "022" "011" "011" "022" "011" "011" "011" "0311" "011"
#> [551] "011" "011" "011" "011" "011" "014" "011" "0311" "0311" "014" "0311"
#> [562] "011" "0324" "0324" "011" "011" "011" "022" "0311" "011" "011" "022"
#> [573] "0324" "0311" "011" "0312" "011" "011" "011" "022" "011" "011" "011"
#> [584] "011" "0311" "011" "0311" "011" "011" "011" "011" "0313" "011" "0312"
#> [595] "0313" "0324" "011" "0313" "0313" "011" "011" "011" "011" "0313" "011"
#> [606] "011" "022" "011" "011" "011" "011" "011" "011" "011" "011" "0234"
#> [617] "0311" "0311" "011" "0311" "0311" "0313" "011" "0311" "011" "0311" "0311"
#> [628] "0311" "0311" "0311" "011" "011" "0313" "011" "0311" "02113" "0311" "011"
#> [639] "011" "011" "011" "0311" "0311" "011" "0311" "0312" "011" "011" "022"
#> [650] "011" "011" "011" "011" "011" "0311" "011" "011" "011" "0311" "0311"
#> [661] "0311" "011" "0234" "011" "011" "0312" "0311" "0311" "0311" "0311" "022"
#> [672] "011" "022" "0311" "0313" "0234" "0311" "0311" "022" "011" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "011" "0312"
#> [694] "0311" "022" "0311" "0312" "011" "0312" "014" "0312" "011" "011" "0311"
#> [705] "0311" "022" "022" "011" "0324" "011" "0324" "0212" "011" "011" "011"
#> [716] "011" "011" "011" "022" "0311" "0311" "022" "0234" "011" "022" "0311"
#> [727] "0311" "0311" "011" "011" "0311" "011" "011" "0311" "011" "011" "011"
#> [738] "011" "011" "0311" "011" "011" "011" "0311" "0311" "011" "0311" "0312"
#> [749] "0312" "0312" "0311" "011" "0311" "011" "011" "011" "0311" "011" "0324"
#> [760] "0311" "0212" "022" "011" "011" "0311" "011" "011" "011" "011" "011"
#> [771] "0312" "011" "011" "0311" "011" "022" "011" "011" "02113" "011" "0311"
#> [782] "011" "011" "011" "011" "011" "0312" "011" "0311" "0311" "011" "011"
#> [793] "0312" "022" "011" "0312" "011" "011" "0234" "0311" "0312" "022" "0311"
#> [804] "0311" "0311" "0234" "0311" "011" "0311" "011" "011" "011" "0324" "0324"
#> [815] "012" "014" "011" "011" "011" "0212" "011" "011" "011" "011" "011"
#> [826] "011" "011" "011" "011" "011" "011" "011" "011" "012" "011" "011"
#> [837] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [848] "011" "0324" "012" "011" "0234" "0324" "022" "011" "014" "014" "014"
#> [859] "0324" "011" "011" "0324" "011" "011" "011" "011" "011" "011" "011"
#> [870] "0311" "011" "011" "011" "011" "011" "011" "014" "011" "011" "011"
#> [881] "011" "011" "011" "011" "012" "0212" "011" "0324" "011" "0324" "011"
#> [892] "0311" "011" "011" "011" "022" "022" "011" "011" "011" "011" "011"
#> [903] "0311" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [914] "011" "011" "011" "011" "011" "011" "011" "011" "011" "011" "011"
#> [925] "011" "011" "011" "011" "022" "011" "014" "011" "011" "011" "022"
#> [936] "0324" "011" "022" "011" "011" "011" "014" "011" "011" "011" "011"
#> [947] "011" "011" "011" "011" "011" "011" "011" "0311" "011" "0212" "014"
#> [958] "014" "011" "0212" "022" "011" "011" "0212" "0311" "011" "011" "011"
#> [969] "011" "0311" "011" "011" "022" "011" "012" "011" "011" "011" "011"
#> [980] "011" "0324" "0324" "022" "022" "0311" "011" "011" "011" "011" "0311"
#> [991] "0311" "011" "011" "011" "0311" "011" "011" "0324" "0324" "011" "022"
#> [1002] "013" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1013] "012" "022" "012" "022" "012" "012" "012" "012" "012" "012" "012"
#> [1024] "012" "012" "0323" "014" "012" "012" "012" "012" "012" "012" "012"
#> [1035] "012" "0311" "012" "0312" "012" "02113" "012" "011" "0322" "012" "012"
#> [1046] "012" "0312" "012" "012" "012" "011" "012" "012" "012" "012" "012"
#> [1057] "012" "012" "011" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1068] "012" "012" "012" "012" "012" "013" "012" "0322" "012" "012" "012"
#> [1079] "013" "012" "012" "012" "0312" "012" "012" "012" "012" "0311" "012"
#> [1090] "012" "012" "012" "011" "013" "012" "012" "012" "012" "012" "012"
#> [1101] "014" "012" "012" "012" "012" "012" "012" "012" "012" "0311" "012"
#> [1112] "012" "012" "012" "012" "013" "012" "0313" "012" "014" "012" "0313"
#> [1123] "012" "012" "012" "012" "012" "0312" "012" "012" "012" "011" "011"
#> [1134] "012" "0322" "012" "012" "012" "012" "012" "022" "012" "012" "011"
#> [1145] "0322" "012" "012" "011" "012" "014" "012" "012" "012" "012" "012"
#> [1156] "012" "012" "012" "012" "012" "011" "012" "011" "012" "012" "012"
#> [1167] "012" "011" "011" "011" "012" "0311" "011" "012" "011" "011" "012"
#> [1178] "012" "012" "012" "022" "012" "014" "012" "022" "011" "012" "012"
#> [1189] "011" "012" "012" "012" "012" "012" "012" "014" "012" "012" "013"
#> [1200] "012" "012" "014" "012" "012" "012" "012" "012" "011" "012" "012"
#> [1211] "013" "012" "012" "014" "022" "012" "0321" "0313" "012" "012" "011"
#> [1222] "012" "012" "022" "012" "022" "012" "012" "012" "014" "012" "012"
#> [1233] "012" "011" "012" "011" "011" "0313" "014" "012" "033" "0321" "0311"
#> [1244] "012" "012" "012" "011" "011" "012" "012" "011" "012" "011" "011"
#> [1255] "011" "022" "022" "022" "022" "012" "012" "012" "012" "012" "0311"
#> [1266] "014" "011" "011" "013" "013" "013" "013" "033" "013" "013" "013"
#> [1277] "033" "0212" "033" "033" "033" "013" "033" "013" "013" "013" "013"
#> [1288] "013" "033" "033" "013" "033" "013" "013" "0233" "033" "013" "013"
#> [1299] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1310] "013" "013" "013" "022" "033" "022" "013" "033" "0212" "013" "013"
#> [1321] "013" "013" "013" "013" "013" "013" "033" "012" "013" "013" "013"
#> [1332] "02113" "013" "033" "013" "033" "013" "013" "013" "013" "013" "013"
#> [1343] "022" "013" "013" "022" "0233" "02113" "013" "013" "013" "013" "02113"
#> [1354] "022" "013" "013" "013" "013" "013" "013" "0212" "0212" "013" "013"
#> [1365] "033" "033" "013" "033" "013" "033" "033" "033" "033" "013" "033"
#> [1376] "013" "013" "013" "013" "013" "013" "013" "013" "013" "013" "013"
#> [1387] "013" "012" "012" "012" "012" "0321" "013" "013" "0231" "014" "02113"
#> [1398] "0233" "0233" "012" "0233" "013" "013" "013" "013" "033" "0233" "013"
#> [1409] "013" "0311" "013" "013" "013" "013" "013" "013" "013" "013" "033"
#> [1420] "013" "013" "0311" "013" "013" "013" "013" "013" "013" "033" "0311"
#> [1431] "013" "011" "013" "0311" "012" "013" "013" "013" "013" "013" "013"
#> [1442] "013" "013" "013" "013" "012" "013" "013" "0233" "013" "033" "022"
#> [1453] "013" "013" "022" "013" "013" "013" "022" "013" "013" "013" "013"
#> [1464] "013" "012" "013" "011" "011" "011" "013" "013" "033" "012" "0313"
#> [1475] "033" "0313" "011" "0212" "013" "022" "012" "013" "013" "011" "013"
#> [1486] "013" "013" "0321" "014" "013" "014" "013" "013" "011" "0231" "014"
#> [1497] "013" "011" "013" "0233" "012" "014" "013" "011" "012" "0321" "013"
#> [1508] "022" "013" "022" "012" "013" "012" "013" "013" "012" "013" "022"
#> [1519] "033" "022" "0233" "0233" "014" "022" "014" "013" "033" "013" "013"
#> [1530] "013" "014" "013" "013" "02113" "012" "022" "011" "011" "013" "013"
#> [1541] "012" "013" "013" "013" "013" "013" "013" "013" "013" "013" "0311"
#> [1552] "013" "011" "013" "013" "013" "014" "0212" "0233" "013" "012" "012"
#> [1563] "014" "0312" "022" "011" "013" "033" "013" "012" "013" "022" "0212"
#> [1574] "014" "0212" "033" "033" "02113" "0233" "0233" "033" "02113" "033" "0233"
#> [1585] "033" "033" "033" "033" "033" "033" "013" "033" "033" "022" "033"
#> [1596] "02113" "0212" "0233" "013" "02113" "013" "033" "013" "0212" "02113" "013"
#> [1607] "013" "0233" "02113" "02113" "033" "033" "033" "033" "033" "033" "033"
#> [1618] "02113" "013" "022" "02113" "0233" "013" "033" "011" "0212" "012" "013"
#> [1629] "012" "013" "014" "012" "0323" "012" "0212" "012" "012" "012" "022"
#> [1640] "012" "012" "013" "012" "0233" "014" "012" "014" "012" "012" "012"
#> [1651] "014" "012" "012" "0321" "012" "011" "012" "011" "011" "012" "012"
#> [1662] "012" "012" "012" "012" "012" "012" "012" "012" "012" "012" "012"
#> [1673] "012" "012" "012" "0231" "012" "011" "011" "0233" "012" "012" "012"
#> [1684] "014" "012" "012" "011" "012" "012" "012" "012" "012" "0322" "012"
#> [1695] "012" "012" "012" "011" "012" "012" "011" "012" "0311" "012" "012"
#> [1706] "012" "0323" "012" "012" "012" "013" "0311" "012" "012" "012" "013"
#> [1717] "012" "012" "0323" "0323" "013" "012" "014" "012" "012" "012" "012"
#> [1728] "0313" "012" "0311" "0311" "012" "012" "0212" "012" "011" "012" "011"
#> [1739] "011" "011" "012" "012" "013" "012" "013" "012" "012" "012" "0323"
#> [1750] "012" "0312" "012" "022" "012" "022" "012" "011" "012" "014" "0312"
#> [1761] "022" "012" "011" "012" "011" "022" "0311" "011" "013" "022" "012"
#> [1772] "012" "012" "012" "012" "012" "011" "012" "012" "012" "0322" "011"
#> [1783] "012" "012" "011" "012" "0313" "011" "012" "0323" "012" "0313" "0313"
#> [1794] "0323" "022" "011" "012" "0313" "022" "012" "012" "012" "012" "0323"
#> [1805] "012" "012" "0233" "0212" "022" "0311" "022" "012" "012" "011" "012"
#> [1816] "012" "012" "012" "012" "012" "0313" "012" "012" "012" "012" "012"
#> [1827] "012" "012" "012" "022" "012" "012" "0323" "012" "012" "0212" "022"
#> [1838] "012" "011" "012" "012" "012" "0212" "012" "012" "014" "012" "012"
#> [1849] "012" "012" "012" "0323" "012" "012" "011" "012" "012" "011" "0322"
#> [1860] "012" "0312" "012" "012" "033" "022" "0312" "0321" "0323" "0212" "012"
#> [1871] "012" "0323" "0323" "012" "012" "012" "0231" "012" "012" "012" "02112"
#> [1882] "012" "012" "012" "012" "0323" "012" "0312" "012" "012" "011" "022"
#> [1893] "012" "012" "012" "0311" "012" "012" "012" "0312" "013" "0323" "012"
#> [1904] "0311" "012" "012" "033" "012" "012" "012" "012" "012" "012" "012"
#> [1915] "0323" "012" "012" "011" "012" "0312" "022" "012" "0323" "0323" "012"
#> [1926] "0311" "012" "012" "012" "0233" "0323" "012" "02112" "012" "0323" "0233"
#> [1937] "033" "012" "012" "0323" "0323" "012" "0323" "012" "033" "022" "0312"
#> [1948] "0323" "012" "0312" "0323" "012" "012" "0212" "012" "012" "0233" "0323"
#> [1959] "02113" "0323" "022" "0323" "0323" "022" "012" "0323" "02112" "033" "0323"
#> [1970] "0312" "012" "0233" "0312" "0323" "0212" "0311" "012" "0212" "0323" "0212"
#> [1981] "0323" "0323" "033" "0232" "02112" "0232" "0212" "0212" "0212" "02112" "022"
#> [1992] "0212" "0231" "0232" "022" "0212" "0231" "0231" "02112" "0231" "022" "02113"
#> [2003] "02112" "0232" "02112" "022" "022" "0212" "0232" "0232" "0212" "0231" "0212"
#> [2014] "0231" "014" "022" "0231" "0321" "022" "02112" "0212" "022" "022" "022"
#> [2025] "022" "0321" "022" "0212" "0212" "022" "022" "0212" "022" "0232" "022"
#> [2036] "02113" "022" "02112" "022" "022" "022" "0321" "02112" "0233" "0232" "02113"
#> [2047] "0212" "0212" "0212" "014" "022" "02113" "0231" "02113" "02112" "0212" "022"
#> [2058] "0212" "0321" "022" "02112" "022" "022" "0212" "022" "022" "0212" "0212"
#> [2069] "0212" "02112" "02112" "022" "0232" "022" "02113" "0233" "02112" "022" "02112"
#> [2080] "02112" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0232" "0212" "022"
#> [2091] "0212" "022" "022" "0212" "022" "022" "022" "0212" "022" "0231" "0212"
#> [2102] "0212" "0212" "0212" "02112" "02112" "0212" "02112" "0231" "02112" "0231" "022"
#> [2113] "02112" "02112" "02112" "02112" "022" "0212" "0231" "0232" "0212" "0212" "0233"
#> [2124] "0232" "014" "022" "0212" "014" "02112" "02112" "0212" "0212" "02112" "02112"
#> [2135] "02112" "0212" "0212" "0212" "022" "0212" "022" "022" "0212" "022" "022"
#> [2146] "022" "022" "0212" "022" "0212" "02112" "0212" "022" "022" "0212" "02112"
#> [2157] "0212" "022" "022" "02112" "022" "022" "022" "0212" "022" "022" "022"
#> [2168] "0212" "022" "022" "022" "022" "0212" "0321" "022" "022" "022" "02111"
#> [2179] "0212" "0212" "0212" "022" "0234" "022" "022" "022" "022" "022" "022"
#> [2190] "014" "022" "014" "022" "0312" "022" "0321" "022" "02113" "02112" "022"
#> [2201] "0232" "0231" "022" "0232" "0232" "022" "0212" "0212" "0212" "0231" "0232"
#> [2212] "022" "0232" "022" "0212" "0212" "02112" "02112" "0212" "0212" "022" "0212"
#> [2223] "0212" "0212" "022" "02112" "02112" "0212" "022" "0212" "022" "022" "022"
#> [2234] "022" "0212" "022" "0321" "022" "022" "0321" "022" "0321" "022" "022"
#> [2245] "022" "022" "022" "0231" "0231" "022" "022" "0321" "022" "022" "0231"
#> [2256] "0231" "022" "022" "014" "0321" "02112" "022" "022" "022" "022" "0321"
#> [2267] "0231" "022" "0321" "022" "022" "022" "014" "022" "014" "022" "022"
#> [2278] "0321" "022" "0231" "022" "022" "014" "022" "022" "022" "014" "0321"
#> [2289] "0321" "022" "022" "0321" "022" "022" "014" "022" "022" "022" "014"
#> [2300] "0321" "014" "014" "014" "022" "014" "022" "014" "014" "014" "022"
#> [2311] "014" "0321" "022" "014" "014" "014" "012" "0212" "0231" "022" "014"
#> [2322] "022" "022" "0321" "022" "022" "022" "022" "022" "022" "022" "022"
#> [2333] "022" "022" "0321" "014" "014" "0321" "022" "014" "0321" "0321" "022"
#> [2344] "0212" "0232" "022" "022" "022" "022" "022" "0321" "022" "022" "022"
#> [2355] "022" "022" "0321" "0212" "02112" "022" "0212" "022" "0212" "0234" "0212"
#> [2366] "0212" "022" "02112" "02112" "022" "022" "022" "0212" "022" "0212" "022"
#> [2377] "022" "022" "0212" "0212" "0232" "022" "02112" "0212" "0232" "022" "022"
#> [2388] "022" "022" "0231" "02113" "022" "022" "022" "02111" "0212" "0212" "022"
#> [2399] "0321" "022" "014" "014" "014" "0212" "022" "0231" "02111" "022" "0212"
#> [2410] "022" "0212" "022" "0212" "014" "022" "022" "022" "022" "0231" "012"
#> [2421] "022" "022" "0321" "0212" "0212" "0231" "022" "022" "022" "022" "022"
#> [2432] "022" "014" "0321" "014" "022" "0321" "0321" "0321" "014" "014" "012"
#> [2443] "0321" "0321" "0321" "022" "022" "022" "0212" "022" "0212" "0212" "0212"
#> [2454] "02112" "0212" "0212" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0212"
#> [2465] "0212" "0212" "0212" "0212" "022" "022" "0321" "022" "022" "022" "0212"
#> [2476] "0212" "022" "0212" "022" "022" "022" "0212" "022" "022" "02112" "022"
#> [2487] "0212" "0212" "0212" "02112" "0212" "0212" "0212" "02112" "022" "0212" "022"
#> [2498] "0212" "0212" "022" "0212" "022" "0212" "022" "022" "022" "022" "0321"
#> [2509] "0321" "022" "0324" "0212" "0212" "02112" "0212" "0212" "02112" "0212" "022"
#> [2520] "0212" "0212" "02112" "022" "022" "022" "0212" "02112" "022" "0212" "02113"
#> [2531] "022" "0212" "02112" "014" "0212" "0321" "022" "022" "022" "0231" "022"
#> [2542] "022" "022" "022" "0232" "022" "022" "022" "014" "022" "0321" "0321"
#> [2553] "014" "014" "0212" "0321" "022" "014" "02112" "0212" "0321" "0212" "0321"
#> [2564] "022" "022" "0321" "022" "022" "022" "022" "022" "014" "014" "014"
#> [2575] "0321" "0321" "022" "022" "02112" "0212" "0212" "022" "022" "022" "022"
#> [2586] "022" "022" "014" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "014" "0232" "02112" "0231" "022" "022" "0321" "022" "0231"
#> [2619] "0231" "0234" "0233" "0232" "014" "02112" "022" "0231" "014" "014" "014"
#> [2630] "0231" "02112" "02112" "0212" "02112" "02112" "022" "0212" "022" "022" "022"
#> [2641] "022" "0321" "022" "022" "0212" "022" "022" "022" "0321" "022" "012"
#> [2652] "022" "022" "022" "022" "022" "0231" "022" "022" "022" "022" "022"
#> [2663] "022" "0321" "0321" "022" "0321" "022" "022" "0321" "022" "014" "0321"
#> [2674] "022" "0321" "022" "022" "0324" "012" "014" "012" "022" "014" "012"
#> [2685] "012" "0232" "0232" "02112" "02112" "0321" "0212" "0212" "0234" "0231" "014"
#> [2696] "022" "0324" "0212" "022" "0321" "022" "0212" "014" "022" "022" "0321"
#> [2707] "014" "022" "014" "014" "022" "014" "014" "0231" "022" "0231" "014"
#> [2718] "014" "0231" "014" "022" "022" "014" "02112" "0321" "014" "0321" "014"
#> [2729] "012" "0321" "0212" "022" "0321" "022" "0321" "014" "014" "0321" "014"
#> [2740] "0321" "014" "014" "0212" "022" "022" "014" "014" "014" "014" "0321"
#> [2751] "014" "014" "022" "022" "0321" "0323" "014" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "014" "014" "022" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "014" "014" "022" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "022" "014" "014" "014" "022" "0212" "0231" "02111" "02111" "0212" "02111"
#> [2828] "0212" "02111" "022" "0212" "0212" "02111" "0212" "0231" "022" "0212" "0212"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "0212" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "014" "022" "0212" "014" "022" "02112"
#> [2861] "0231" "0232" "022" "022" "0212" "0212" "0231" "022" "0212" "022" "02111"
#> [2872] "0212" "0212" "02111" "02111" "0212" "022" "0212" "014" "0212" "0212"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2878))
#> [1] "01" "01" "0231" "0322" "01" "01" "0322" "01" "01" "01" "01"
#> [12] "01" "0322" "01" "01" "01" "01" "01" "0313" "01" "0322" "01"
#> [23] "01" "0322" "01" "0322" "0322" "0322" "01" "0312" "0322" "01" "0322"
#> [34] "0322" "0322" "01" "01" "01" "0312" "0311" "01" "022" "01" "0311"
#> [45] "01" "01" "0311" "0312" "0322" "01" "0312" "01" "01" "01" "01"
#> [56] "0212" "0212" "01" "01" "0313" "0322" "01" "01" "022" "0311" "01"
#> [67] "01" "01" "0322" "01" "0322" "0311" "0322" "01" "01" "01" "01"
#> [78] "01" "022" "01" "01" "0322" "01" "01" "01" "022" "01" "01"
#> [89] "01" "01" "0322" "01" "02113" "01" "01" "01" "01" "01" "01"
#> [100] "0313" "01" "01" "01" "01" "01" "0322" "022" "01" "01" "01"
#> [111] "01" "01" "01" "01" "01" "022" "022" "0322" "01" "0321" "0313"
#> [122] "0322" "022" "022" "022" "0234" "01" "01" "01" "01" "01" "01"
#> [133] "01" "0324" "01" "022" "01" "01" "0322" "01" "01" "01" "01"
#> [144] "01" "01" "022" "01" "0212" "01" "0324" "0313" "0313" "01" "0313"
#> [155] "0322" "01" "0313" "0234" "0322" "0322" "0322" "01" "0313" "0313" "022"
#> [166] "01" "0322" "0313" "01" "01" "0322" "0313" "0313" "022" "022" "0313"
#> [177] "0313" "01" "0313" "0313" "0312" "0313" "0322" "0313" "0322" "0313" "0313"
#> [188] "0313" "0312" "022" "0322" "01" "0313" "0312" "0313" "0322" "0312" "0312"
#> [199] "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313" "0322"
#> [210] "022" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "022" "0312" "01"
#> [221] "0312" "0313" "0312" "0313" "0312" "0313" "0312" "0312" "01" "02113" "0313"
#> [232] "0313" "0312" "0313" "02113" "0312" "0312" "0313" "0312" "0313" "0313" "0313"
#> [243] "0312" "01" "0312" "0312" "0312" "022" "0312" "0313" "01" "0313" "0312"
#> [254] "0312" "0313" "0313" "01" "0312" "0313" "0313" "01" "0313" "01" "01"
#> [265] "01" "0313" "01" "01" "01" "0313" "01" "01" "0313" "0313" "01"
#> [276] "0313" "0313" "0313" "0313" "0322" "0212" "01" "0313" "0313" "0313" "022"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312"
#> [298] "0312" "02113" "0312" "02113" "0313" "0313" "0234" "0313" "02113" "022" "0312"
#> [309] "022" "0312" "0312" "0313" "022" "0313" "0313" "0312" "01" "0313" "0313"
#> [320] "01" "0313" "01" "0312" "0313" "0311" "01" "0313" "0313" "0313" "0313"
#> [331] "0313" "01" "01" "01" "01" "022" "022" "01" "0313" "01" "0313"
#> [342] "0313" "022" "0313" "0313" "0313" "022" "0311" "0311" "022" "0313" "01"
#> [353] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313"
#> [364] "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312"
#> [375] "0312" "0312" "0312" "0312" "0312" "0313" "01" "01" "022" "0311" "01"
#> [386] "01" "01" "0311" "0324" "0311" "01" "0311" "01" "0311" "022" "0311"
#> [397] "0313" "0311" "01" "01" "0311" "01" "01" "0311" "01" "022" "01"
#> [408] "0311" "01" "01" "0311" "0311" "0212" "01" "01" "01" "01" "01"
#> [419] "0311" "01" "01" "01" "01" "0313" "0234" "01" "01" "01" "01"
#> [430] "01" "01" "0234" "01" "0234" "01" "022" "01" "0212" "01" "0234"
#> [441] "0234" "0311" "0311" "0311" "0311" "01" "01" "0312" "0312" "0312" "0311"
#> [452] "01" "0311" "01" "01" "0312" "01" "01" "0312" "0312" "0311" "0312"
#> [463] "0311" "0311" "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0312"
#> [474] "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311" "0311" "0311" "01"
#> [485] "0212" "0312" "0311" "0311" "022" "022" "0212" "0312" "0312" "022" "0312"
#> [496] "01" "0212" "02113" "01" "0312" "02113" "01" "0311" "0312" "0311" "02113"
#> [507] "01" "0311" "0311" "0311" "0311" "022" "0311" "0311" "01" "01" "022"
#> [518] "0311" "0312" "0311" "01" "01" "01" "0312" "0312" "0313" "0312" "01"
#> [529] "01" "022" "0212" "0212" "01" "022" "01" "01" "01" "0212" "01"
#> [540] "0311" "01" "022" "01" "01" "022" "01" "01" "01" "0311" "01"
#> [551] "01" "01" "01" "01" "01" "01" "01" "0311" "0311" "01" "0311"
#> [562] "01" "0324" "0324" "01" "01" "01" "022" "0311" "01" "01" "022"
#> [573] "0324" "0311" "01" "0312" "01" "01" "01" "022" "01" "01" "01"
#> [584] "01" "0311" "01" "0311" "01" "01" "01" "01" "0313" "01" "0312"
#> [595] "0313" "0324" "01" "0313" "0313" "01" "01" "01" "01" "0313" "01"
#> [606] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "0234"
#> [617] "0311" "0311" "01" "0311" "0311" "0313" "01" "0311" "01" "0311" "0311"
#> [628] "0311" "0311" "0311" "01" "01" "0313" "01" "0311" "02113" "0311" "01"
#> [639] "01" "01" "01" "0311" "0311" "01" "0311" "0312" "01" "01" "022"
#> [650] "01" "01" "01" "01" "01" "0311" "01" "01" "01" "0311" "0311"
#> [661] "0311" "01" "0234" "01" "01" "0312" "0311" "0311" "0311" "0311" "022"
#> [672] "01" "022" "0311" "0313" "0234" "0311" "0311" "022" "01" "0311" "0311"
#> [683] "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0311" "01" "0312"
#> [694] "0311" "022" "0311" "0312" "01" "0312" "01" "0312" "01" "01" "0311"
#> [705] "0311" "022" "022" "01" "0324" "01" "0324" "0212" "01" "01" "01"
#> [716] "01" "01" "01" "022" "0311" "0311" "022" "0234" "01" "022" "0311"
#> [727] "0311" "0311" "01" "01" "0311" "01" "01" "0311" "01" "01" "01"
#> [738] "01" "01" "0311" "01" "01" "01" "0311" "0311" "01" "0311" "0312"
#> [749] "0312" "0312" "0311" "01" "0311" "01" "01" "01" "0311" "01" "0324"
#> [760] "0311" "0212" "022" "01" "01" "0311" "01" "01" "01" "01" "01"
#> [771] "0312" "01" "01" "0311" "01" "022" "01" "01" "02113" "01" "0311"
#> [782] "01" "01" "01" "01" "01" "0312" "01" "0311" "0311" "01" "01"
#> [793] "0312" "022" "01" "0312" "01" "01" "0234" "0311" "0312" "022" "0311"
#> [804] "0311" "0311" "0234" "0311" "01" "0311" "01" "01" "01" "0324" "0324"
#> [815] "01" "01" "01" "01" "01" "0212" "01" "01" "01" "01" "01"
#> [826] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [837] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [848] "01" "0324" "01" "01" "0234" "0324" "022" "01" "01" "01" "01"
#> [859] "0324" "01" "01" "0324" "01" "01" "01" "01" "01" "01" "01"
#> [870] "0311" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [881] "01" "01" "01" "01" "01" "0212" "01" "0324" "01" "0324" "01"
#> [892] "0311" "01" "01" "01" "022" "022" "01" "01" "01" "01" "01"
#> [903] "0311" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [914] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [925] "01" "01" "01" "01" "022" "01" "01" "01" "01" "01" "022"
#> [936] "0324" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01"
#> [947] "01" "01" "01" "01" "01" "01" "01" "0311" "01" "0212" "01"
#> [958] "01" "01" "0212" "022" "01" "01" "0212" "0311" "01" "01" "01"
#> [969] "01" "0311" "01" "01" "022" "01" "01" "01" "01" "01" "01"
#> [980] "01" "0324" "0324" "022" "022" "0311" "01" "01" "01" "01" "0311"
#> [991] "0311" "01" "01" "01" "0311" "01" "01" "0324" "0324" "01" "022"
#> [1002] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1013] "01" "022" "01" "022" "01" "01" "01" "01" "01" "01" "01"
#> [1024] "01" "01" "0323" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1035] "01" "0311" "01" "0312" "01" "02113" "01" "01" "0322" "01" "01"
#> [1046] "01" "0312" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1057] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1068] "01" "01" "01" "01" "01" "01" "01" "0322" "01" "01" "01"
#> [1079] "01" "01" "01" "01" "0312" "01" "01" "01" "01" "0311" "01"
#> [1090] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1101] "01" "01" "01" "01" "01" "01" "01" "01" "01" "0311" "01"
#> [1112] "01" "01" "01" "01" "01" "01" "0313" "01" "01" "01" "0313"
#> [1123] "01" "01" "01" "01" "01" "0312" "01" "01" "01" "01" "01"
#> [1134] "01" "0322" "01" "01" "01" "01" "01" "022" "01" "01" "01"
#> [1145] "0322" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1156] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1167] "01" "01" "01" "01" "01" "0311" "01" "01" "01" "01" "01"
#> [1178] "01" "01" "01" "022" "01" "01" "01" "022" "01" "01" "01"
#> [1189] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1200] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1211] "01" "01" "01" "01" "022" "01" "0321" "0313" "01" "01" "01"
#> [1222] "01" "01" "022" "01" "022" "01" "01" "01" "01" "01" "01"
#> [1233] "01" "01" "01" "01" "01" "0313" "01" "01" "033" "0321" "0311"
#> [1244] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1255] "01" "022" "022" "022" "022" "01" "01" "01" "01" "01" "0311"
#> [1266] "01" "01" "01" "01" "01" "01" "01" "033" "01" "01" "01"
#> [1277] "033" "0212" "033" "033" "033" "01" "033" "01" "01" "01" "01"
#> [1288] "01" "033" "033" "01" "033" "01" "01" "0233" "033" "01" "01"
#> [1299] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1310] "01" "01" "01" "022" "033" "022" "01" "033" "0212" "01" "01"
#> [1321] "01" "01" "01" "01" "01" "01" "033" "01" "01" "01" "01"
#> [1332] "02113" "01" "033" "01" "033" "01" "01" "01" "01" "01" "01"
#> [1343] "022" "01" "01" "022" "0233" "02113" "01" "01" "01" "01" "02113"
#> [1354] "022" "01" "01" "01" "01" "01" "01" "0212" "0212" "01" "01"
#> [1365] "033" "033" "01" "033" "01" "033" "033" "033" "033" "01" "033"
#> [1376] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1387] "01" "01" "01" "01" "01" "0321" "01" "01" "0231" "01" "02113"
#> [1398] "0233" "0233" "01" "0233" "01" "01" "01" "01" "033" "0233" "01"
#> [1409] "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "033"
#> [1420] "01" "01" "0311" "01" "01" "01" "01" "01" "01" "033" "0311"
#> [1431] "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01"
#> [1442] "01" "01" "01" "01" "01" "01" "01" "0233" "01" "033" "022"
#> [1453] "01" "01" "022" "01" "01" "01" "022" "01" "01" "01" "01"
#> [1464] "01" "01" "01" "01" "01" "01" "01" "01" "033" "01" "0313"
#> [1475] "033" "0313" "01" "0212" "01" "022" "01" "01" "01" "01" "01"
#> [1486] "01" "01" "0321" "01" "01" "01" "01" "01" "01" "0231" "01"
#> [1497] "01" "01" "01" "0233" "01" "01" "01" "01" "01" "0321" "01"
#> [1508] "022" "01" "022" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1519] "033" "022" "0233" "0233" "01" "022" "01" "01" "033" "01" "01"
#> [1530] "01" "01" "01" "01" "02113" "01" "022" "01" "01" "01" "01"
#> [1541] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "0311"
#> [1552] "01" "01" "01" "01" "01" "01" "0212" "0233" "01" "01" "01"
#> [1563] "01" "0312" "022" "01" "01" "033" "01" "01" "01" "022" "0212"
#> [1574] "01" "0212" "033" "033" "02113" "0233" "0233" "033" "02113" "033" "0233"
#> [1585] "033" "033" "033" "033" "033" "033" "01" "033" "033" "022" "033"
#> [1596] "02113" "0212" "0233" "01" "02113" "01" "033" "01" "0212" "02113" "01"
#> [1607] "01" "0233" "02113" "02113" "033" "033" "033" "033" "033" "033" "033"
#> [1618] "02113" "01" "022" "02113" "0233" "01" "033" "01" "0212" "01" "01"
#> [1629] "01" "01" "01" "01" "0323" "01" "0212" "01" "01" "01" "022"
#> [1640] "01" "01" "01" "01" "0233" "01" "01" "01" "01" "01" "01"
#> [1651] "01" "01" "01" "0321" "01" "01" "01" "01" "01" "01" "01"
#> [1662] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1673] "01" "01" "01" "0231" "01" "01" "01" "0233" "01" "01" "01"
#> [1684] "01" "01" "01" "01" "01" "01" "01" "01" "01" "0322" "01"
#> [1695] "01" "01" "01" "01" "01" "01" "01" "01" "0311" "01" "01"
#> [1706] "01" "0323" "01" "01" "01" "01" "0311" "01" "01" "01" "01"
#> [1717] "01" "01" "0323" "0323" "01" "01" "01" "01" "01" "01" "01"
#> [1728] "0313" "01" "0311" "0311" "01" "01" "0212" "01" "01" "01" "01"
#> [1739] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "0323"
#> [1750] "01" "0312" "01" "022" "01" "022" "01" "01" "01" "01" "0312"
#> [1761] "022" "01" "01" "01" "01" "022" "0311" "01" "01" "022" "01"
#> [1772] "01" "01" "01" "01" "01" "01" "01" "01" "01" "0322" "01"
#> [1783] "01" "01" "01" "01" "0313" "01" "01" "0323" "01" "0313" "0313"
#> [1794] "0323" "022" "01" "01" "0313" "022" "01" "01" "01" "01" "0323"
#> [1805] "01" "01" "0233" "0212" "022" "0311" "022" "01" "01" "01" "01"
#> [1816] "01" "01" "01" "01" "01" "0313" "01" "01" "01" "01" "01"
#> [1827] "01" "01" "01" "022" "01" "01" "0323" "01" "01" "0212" "022"
#> [1838] "01" "01" "01" "01" "01" "0212" "01" "01" "01" "01" "01"
#> [1849] "01" "01" "01" "0323" "01" "01" "01" "01" "01" "01" "0322"
#> [1860] "01" "0312" "01" "01" "033" "022" "0312" "0321" "0323" "0212" "01"
#> [1871] "01" "0323" "0323" "01" "01" "01" "0231" "01" "01" "01" "02112"
#> [1882] "01" "01" "01" "01" "0323" "01" "0312" "01" "01" "01" "022"
#> [1893] "01" "01" "01" "0311" "01" "01" "01" "0312" "01" "0323" "01"
#> [1904] "0311" "01" "01" "033" "01" "01" "01" "01" "01" "01" "01"
#> [1915] "0323" "01" "01" "01" "01" "0312" "022" "01" "0323" "0323" "01"
#> [1926] "0311" "01" "01" "01" "0233" "0323" "01" "02112" "01" "0323" "0233"
#> [1937] "033" "01" "01" "0323" "0323" "01" "0323" "01" "033" "022" "0312"
#> [1948] "0323" "01" "0312" "0323" "01" "01" "0212" "01" "01" "0233" "0323"
#> [1959] "02113" "0323" "022" "0323" "0323" "022" "01" "0323" "02112" "033" "0323"
#> [1970] "0312" "01" "0233" "0312" "0323" "0212" "0311" "01" "0212" "0323" "0212"
#> [1981] "0323" "0323" "033" "0232" "02112" "0232" "0212" "0212" "0212" "02112" "022"
#> [1992] "0212" "0231" "0232" "022" "0212" "0231" "0231" "02112" "0231" "022" "02113"
#> [2003] "02112" "0232" "02112" "022" "022" "0212" "0232" "0232" "0212" "0231" "0212"
#> [2014] "0231" "01" "022" "0231" "0321" "022" "02112" "0212" "022" "022" "022"
#> [2025] "022" "0321" "022" "0212" "0212" "022" "022" "0212" "022" "0232" "022"
#> [2036] "02113" "022" "02112" "022" "022" "022" "0321" "02112" "0233" "0232" "02113"
#> [2047] "0212" "0212" "0212" "01" "022" "02113" "0231" "02113" "02112" "0212" "022"
#> [2058] "0212" "0321" "022" "02112" "022" "022" "0212" "022" "022" "0212" "0212"
#> [2069] "0212" "02112" "02112" "022" "0232" "022" "02113" "0233" "02112" "022" "02112"
#> [2080] "02112" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0232" "0212" "022"
#> [2091] "0212" "022" "022" "0212" "022" "022" "022" "0212" "022" "0231" "0212"
#> [2102] "0212" "0212" "0212" "02112" "02112" "0212" "02112" "0231" "02112" "0231" "022"
#> [2113] "02112" "02112" "02112" "02112" "022" "0212" "0231" "0232" "0212" "0212" "0233"
#> [2124] "0232" "01" "022" "0212" "01" "02112" "02112" "0212" "0212" "02112" "02112"
#> [2135] "02112" "0212" "0212" "0212" "022" "0212" "022" "022" "0212" "022" "022"
#> [2146] "022" "022" "0212" "022" "0212" "02112" "0212" "022" "022" "0212" "02112"
#> [2157] "0212" "022" "022" "02112" "022" "022" "022" "0212" "022" "022" "022"
#> [2168] "0212" "022" "022" "022" "022" "0212" "0321" "022" "022" "022" "02111"
#> [2179] "0212" "0212" "0212" "022" "0234" "022" "022" "022" "022" "022" "022"
#> [2190] "01" "022" "01" "022" "0312" "022" "0321" "022" "02113" "02112" "022"
#> [2201] "0232" "0231" "022" "0232" "0232" "022" "0212" "0212" "0212" "0231" "0232"
#> [2212] "022" "0232" "022" "0212" "0212" "02112" "02112" "0212" "0212" "022" "0212"
#> [2223] "0212" "0212" "022" "02112" "02112" "0212" "022" "0212" "022" "022" "022"
#> [2234] "022" "0212" "022" "0321" "022" "022" "0321" "022" "0321" "022" "022"
#> [2245] "022" "022" "022" "0231" "0231" "022" "022" "0321" "022" "022" "0231"
#> [2256] "0231" "022" "022" "01" "0321" "02112" "022" "022" "022" "022" "0321"
#> [2267] "0231" "022" "0321" "022" "022" "022" "01" "022" "01" "022" "022"
#> [2278] "0321" "022" "0231" "022" "022" "01" "022" "022" "022" "01" "0321"
#> [2289] "0321" "022" "022" "0321" "022" "022" "01" "022" "022" "022" "01"
#> [2300] "0321" "01" "01" "01" "022" "01" "022" "01" "01" "01" "022"
#> [2311] "01" "0321" "022" "01" "01" "01" "01" "0212" "0231" "022" "01"
#> [2322] "022" "022" "0321" "022" "022" "022" "022" "022" "022" "022" "022"
#> [2333] "022" "022" "0321" "01" "01" "0321" "022" "01" "0321" "0321" "022"
#> [2344] "0212" "0232" "022" "022" "022" "022" "022" "0321" "022" "022" "022"
#> [2355] "022" "022" "0321" "0212" "02112" "022" "0212" "022" "0212" "0234" "0212"
#> [2366] "0212" "022" "02112" "02112" "022" "022" "022" "0212" "022" "0212" "022"
#> [2377] "022" "022" "0212" "0212" "0232" "022" "02112" "0212" "0232" "022" "022"
#> [2388] "022" "022" "0231" "02113" "022" "022" "022" "02111" "0212" "0212" "022"
#> [2399] "0321" "022" "01" "01" "01" "0212" "022" "0231" "02111" "022" "0212"
#> [2410] "022" "0212" "022" "0212" "01" "022" "022" "022" "022" "0231" "01"
#> [2421] "022" "022" "0321" "0212" "0212" "0231" "022" "022" "022" "022" "022"
#> [2432] "022" "01" "0321" "01" "022" "0321" "0321" "0321" "01" "01" "01"
#> [2443] "0321" "0321" "0321" "022" "022" "022" "0212" "022" "0212" "0212" "0212"
#> [2454] "02112" "0212" "0212" "02112" "0212" "0212" "0231" "0212" "0212" "0212" "0212"
#> [2465] "0212" "0212" "0212" "0212" "022" "022" "0321" "022" "022" "022" "0212"
#> [2476] "0212" "022" "0212" "022" "022" "022" "0212" "022" "022" "02112" "022"
#> [2487] "0212" "0212" "0212" "02112" "0212" "0212" "0212" "02112" "022" "0212" "022"
#> [2498] "0212" "0212" "022" "0212" "022" "0212" "022" "022" "022" "022" "0321"
#> [2509] "0321" "022" "0324" "0212" "0212" "02112" "0212" "0212" "02112" "0212" "022"
#> [2520] "0212" "0212" "02112" "022" "022" "022" "0212" "02112" "022" "0212" "02113"
#> [2531] "022" "0212" "02112" "01" "0212" "0321" "022" "022" "022" "0231" "022"
#> [2542] "022" "022" "022" "0232" "022" "022" "022" "01" "022" "0321" "0321"
#> [2553] "01" "01" "0212" "0321" "022" "01" "02112" "0212" "0321" "0212" "0321"
#> [2564] "022" "022" "0321" "022" "022" "022" "022" "022" "01" "01" "01"
#> [2575] "0321" "0321" "022" "022" "02112" "0212" "0212" "022" "022" "022" "022"
#> [2586] "022" "022" "01" "02111" "0232" "0234" "0232" "02113" "02113" "02111" "02113"
#> [2597] "02113" "02111" "0231" "02113" "02111" "02111" "0232" "02113" "0232" "0231" "0234"
#> [2608] "0232" "0323" "01" "0232" "02112" "0231" "022" "022" "0321" "022" "0231"
#> [2619] "0231" "0234" "0233" "0232" "01" "02112" "022" "0231" "01" "01" "01"
#> [2630] "0231" "02112" "02112" "0212" "02112" "02112" "022" "0212" "022" "022" "022"
#> [2641] "022" "0321" "022" "022" "0212" "022" "022" "022" "0321" "022" "01"
#> [2652] "022" "022" "022" "022" "022" "0231" "022" "022" "022" "022" "022"
#> [2663] "022" "0321" "0321" "022" "0321" "022" "022" "0321" "022" "01" "0321"
#> [2674] "022" "0321" "022" "022" "0324" "01" "01" "01" "022" "01" "01"
#> [2685] "01" "0232" "0232" "02112" "02112" "0321" "0212" "0212" "0234" "0231" "01"
#> [2696] "022" "0324" "0212" "022" "0321" "022" "0212" "01" "022" "022" "0321"
#> [2707] "01" "022" "01" "01" "022" "01" "01" "0231" "022" "0231" "01"
#> [2718] "01" "0231" "01" "022" "022" "01" "02112" "0321" "01" "0321" "01"
#> [2729] "01" "0321" "0212" "022" "0321" "022" "0321" "01" "01" "0321" "01"
#> [2740] "0321" "01" "01" "0212" "022" "022" "01" "01" "01" "01" "0321"
#> [2751] "01" "01" "022" "022" "0321" "0323" "01" "02111" "02111" "02111" "02111"
#> [2762] "02111" "02111" "02111" "0232" "01" "01" "022" "02111" "02113" "02111" "02111"
#> [2773] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "0231" "02111"
#> [2784] "02111" "02111" "0232" "02111" "0232" "02111" "01" "01" "022" "0231" "0231"
#> [2795] "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02113" "0233" "02111" "02113"
#> [2806] "02111" "02111" "0232" "02111" "02111" "02111" "02111" "02111" "02111" "02111" "02111"
#> [2817] "022" "01" "01" "01" "022" "0212" "0231" "02111" "02111" "0212" "02111"
#> [2828] "0212" "02111" "022" "0212" "0212" "02111" "0212" "0231" "022" "0212" "0212"
#> [2839] "0232" "0231" "02111" "02111" "02112" "02112" "0212" "02111" "02112" "02111" "02111"
#> [2850] "02112" "02111" "02111" "0321" "0231" "01" "022" "0212" "01" "022" "02112"
#> [2861] "0231" "0232" "022" "022" "0212" "0212" "0231" "022" "0212" "022" "02111"
#> [2872] "0212" "0212" "02111" "02111" "0212" "022" "0212" "01" "0212" "0212"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3262))
#> [1] "01" "01" "0231" "0322" "01" "01" "0322" "01" "01" "01" "01" "01" "0322"
#> [14] "01" "01" "01" "01" "01" "0313" "01" "0322" "01" "01" "0322" "01" "0322"
#> [27] "0322" "0322" "01" "0312" "0322" "01" "0322" "0322" "0322" "01" "01" "01" "0312"
#> [40] "0311" "01" "022" "01" "0311" "01" "01" "0311" "0312" "0322" "01" "0312" "01"
#> [53] "01" "01" "01" "021" "021" "01" "01" "0313" "0322" "01" "01" "022" "0311"
#> [66] "01" "01" "01" "0322" "01" "0322" "0311" "0322" "01" "01" "01" "01" "01"
#> [79] "022" "01" "01" "0322" "01" "01" "01" "022" "01" "01" "01" "01" "0322"
#> [92] "01" "021" "01" "01" "01" "01" "01" "01" "0313" "01" "01" "01" "01"
#> [105] "01" "0322" "022" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022"
#> [118] "0322" "01" "0321" "0313" "0322" "022" "022" "022" "0234" "01" "01" "01" "01"
#> [131] "01" "01" "01" "0324" "01" "022" "01" "01" "0322" "01" "01" "01" "01"
#> [144] "01" "01" "022" "01" "021" "01" "0324" "0313" "0313" "01" "0313" "0322" "01"
#> [157] "0313" "0234" "0322" "0322" "0322" "01" "0313" "0313" "022" "01" "0322" "0313" "01"
#> [170] "01" "0322" "0313" "0313" "022" "022" "0313" "0313" "01" "0313" "0313" "0312" "0313"
#> [183] "0322" "0313" "0322" "0313" "0313" "0313" "0312" "022" "0322" "01" "0313" "0312" "0313"
#> [196] "0322" "0312" "0312" "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313"
#> [209] "0322" "022" "0313" "0234" "0313" "0312" "0313" "0313" "0322" "022" "0312" "01" "0312"
#> [222] "0313" "0312" "0313" "0312" "0313" "0312" "0312" "01" "021" "0313" "0313" "0312" "0313"
#> [235] "021" "0312" "0312" "0313" "0312" "0313" "0313" "0313" "0312" "01" "0312" "0312" "0312"
#> [248] "022" "0312" "0313" "01" "0313" "0312" "0312" "0313" "0313" "01" "0312" "0313" "0313"
#> [261] "01" "0313" "01" "01" "01" "0313" "01" "01" "01" "0313" "01" "01" "0313"
#> [274] "0313" "01" "0313" "0313" "0313" "0313" "0322" "021" "01" "0313" "0313" "0313" "022"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "021"
#> [300] "0312" "021" "0313" "0313" "0234" "0313" "021" "022" "0312" "022" "0312" "0312" "0313"
#> [313] "022" "0313" "0313" "0312" "01" "0313" "0313" "01" "0313" "01" "0312" "0313" "0311"
#> [326] "01" "0313" "0313" "0313" "0313" "0313" "01" "01" "01" "01" "022" "022" "01"
#> [339] "0313" "01" "0313" "0313" "022" "0313" "0313" "0313" "022" "0311" "0311" "022" "0313"
#> [352] "01" "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313" "0313"
#> [365] "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312" "0312" "0312" "0312"
#> [378] "0312" "0312" "0313" "01" "01" "022" "0311" "01" "01" "01" "0311" "0324" "0311"
#> [391] "01" "0311" "01" "0311" "022" "0311" "0313" "0311" "01" "01" "0311" "01" "01"
#> [404] "0311" "01" "022" "01" "0311" "01" "01" "0311" "0311" "021" "01" "01" "01"
#> [417] "01" "01" "0311" "01" "01" "01" "01" "0313" "0234" "01" "01" "01" "01"
#> [430] "01" "01" "0234" "01" "0234" "01" "022" "01" "021" "01" "0234" "0234" "0311"
#> [443] "0311" "0311" "0311" "01" "01" "0312" "0312" "0312" "0311" "01" "0311" "01" "01"
#> [456] "0312" "01" "01" "0312" "0312" "0311" "0312" "0311" "0311" "0312" "0312" "0312" "0311"
#> [469] "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311"
#> [482] "0311" "0311" "01" "021" "0312" "0311" "0311" "022" "022" "021" "0312" "0312" "022"
#> [495] "0312" "01" "021" "021" "01" "0312" "021" "01" "0311" "0312" "0311" "021" "01"
#> [508] "0311" "0311" "0311" "0311" "022" "0311" "0311" "01" "01" "022" "0311" "0312" "0311"
#> [521] "01" "01" "01" "0312" "0312" "0313" "0312" "01" "01" "022" "021" "021" "01"
#> [534] "022" "01" "01" "01" "021" "01" "0311" "01" "022" "01" "01" "022" "01"
#> [547] "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "0311" "0311"
#> [560] "01" "0311" "01" "0324" "0324" "01" "01" "01" "022" "0311" "01" "01" "022"
#> [573] "0324" "0311" "01" "0312" "01" "01" "01" "022" "01" "01" "01" "01" "0311"
#> [586] "01" "0311" "01" "01" "01" "01" "0313" "01" "0312" "0313" "0324" "01" "0313"
#> [599] "0313" "01" "01" "01" "01" "0313" "01" "01" "022" "01" "01" "01" "01"
#> [612] "01" "01" "01" "01" "0234" "0311" "0311" "01" "0311" "0311" "0313" "01" "0311"
#> [625] "01" "0311" "0311" "0311" "0311" "0311" "01" "01" "0313" "01" "0311" "021" "0311"
#> [638] "01" "01" "01" "01" "0311" "0311" "01" "0311" "0312" "01" "01" "022" "01"
#> [651] "01" "01" "01" "01" "0311" "01" "01" "01" "0311" "0311" "0311" "01" "0234"
#> [664] "01" "01" "0312" "0311" "0311" "0311" "0311" "022" "01" "022" "0311" "0313" "0234"
#> [677] "0311" "0311" "022" "01" "0311" "0311" "0312" "0312" "0311" "0312" "0312" "0312" "0311"
#> [690] "0311" "0311" "01" "0312" "0311" "022" "0311" "0312" "01" "0312" "01" "0312" "01"
#> [703] "01" "0311" "0311" "022" "022" "01" "0324" "01" "0324" "021" "01" "01" "01"
#> [716] "01" "01" "01" "022" "0311" "0311" "022" "0234" "01" "022" "0311" "0311" "0311"
#> [729] "01" "01" "0311" "01" "01" "0311" "01" "01" "01" "01" "01" "0311" "01"
#> [742] "01" "01" "0311" "0311" "01" "0311" "0312" "0312" "0312" "0311" "01" "0311" "01"
#> [755] "01" "01" "0311" "01" "0324" "0311" "021" "022" "01" "01" "0311" "01" "01"
#> [768] "01" "01" "01" "0312" "01" "01" "0311" "01" "022" "01" "01" "021" "01"
#> [781] "0311" "01" "01" "01" "01" "01" "0312" "01" "0311" "0311" "01" "01" "0312"
#> [794] "022" "01" "0312" "01" "01" "0234" "0311" "0312" "022" "0311" "0311" "0311" "0234"
#> [807] "0311" "01" "0311" "01" "01" "01" "0324" "0324" "01" "01" "01" "01" "01"
#> [820] "021" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [833] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [846] "01" "01" "01" "0324" "01" "01" "0234" "0324" "022" "01" "01" "01" "01"
#> [859] "0324" "01" "01" "0324" "01" "01" "01" "01" "01" "01" "01" "0311" "01"
#> [872] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [885] "01" "021" "01" "0324" "01" "0324" "01" "0311" "01" "01" "01" "022" "022"
#> [898] "01" "01" "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01"
#> [911] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [924] "01" "01" "01" "01" "01" "022" "01" "01" "01" "01" "01" "022" "0324"
#> [937] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [950] "01" "01" "01" "01" "0311" "01" "021" "01" "01" "01" "021" "022" "01"
#> [963] "01" "021" "0311" "01" "01" "01" "01" "0311" "01" "01" "022" "01" "01"
#> [976] "01" "01" "01" "01" "01" "0324" "0324" "022" "022" "0311" "01" "01" "01"
#> [989] "01" "0311" "0311" "01" "01" "01" "0311" "01" "01" "0324" "0324" "01" "022"
#> [1002] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1015] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "0323" "01"
#> [1028] "01" "01" "01" "01" "01" "01" "01" "01" "0311" "01" "0312" "01" "021"
#> [1041] "01" "01" "0322" "01" "01" "01" "0312" "01" "01" "01" "01" "01" "01"
#> [1054] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1067] "01" "01" "01" "01" "01" "01" "01" "01" "0322" "01" "01" "01" "01"
#> [1080] "01" "01" "01" "0312" "01" "01" "01" "01" "0311" "01" "01" "01" "01"
#> [1093] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1106] "01" "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "0313"
#> [1119] "01" "01" "01" "0313" "01" "01" "01" "01" "01" "0312" "01" "01" "01"
#> [1132] "01" "01" "01" "0322" "01" "01" "01" "01" "01" "022" "01" "01" "01"
#> [1145] "0322" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1158] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1171] "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "01"
#> [1184] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1197] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1210] "01" "01" "01" "01" "01" "022" "01" "0321" "0313" "01" "01" "01" "01"
#> [1223] "01" "022" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1236] "01" "01" "0313" "01" "01" "033" "0321" "0311" "01" "01" "01" "01" "01"
#> [1249] "01" "01" "01" "01" "01" "01" "01" "022" "022" "022" "022" "01" "01"
#> [1262] "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "033" "01"
#> [1275] "01" "01" "033" "021" "033" "033" "033" "01" "033" "01" "01" "01" "01"
#> [1288] "01" "033" "033" "01" "033" "01" "01" "0233" "033" "01" "01" "01" "01"
#> [1301] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1314] "033" "022" "01" "033" "021" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1327] "033" "01" "01" "01" "01" "021" "01" "033" "01" "033" "01" "01" "01"
#> [1340] "01" "01" "01" "022" "01" "01" "022" "0233" "021" "01" "01" "01" "01"
#> [1353] "021" "022" "01" "01" "01" "01" "01" "01" "021" "021" "01" "01" "033"
#> [1366] "033" "01" "033" "01" "033" "033" "033" "033" "01" "033" "01" "01" "01"
#> [1379] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1392] "0321" "01" "01" "0231" "01" "021" "0233" "0233" "01" "0233" "01" "01" "01"
#> [1405] "01" "033" "0233" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01"
#> [1418] "01" "033" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "033" "0311"
#> [1431] "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1444] "01" "01" "01" "01" "01" "0233" "01" "033" "022" "01" "01" "022" "01"
#> [1457] "01" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1470] "01" "01" "033" "01" "0313" "033" "0313" "01" "021" "01" "022" "01" "01"
#> [1483] "01" "01" "01" "01" "01" "0321" "01" "01" "01" "01" "01" "01" "0231"
#> [1496] "01" "01" "01" "01" "0233" "01" "01" "01" "01" "01" "0321" "01" "022"
#> [1509] "01" "022" "01" "01" "01" "01" "01" "01" "01" "022" "033" "022" "0233"
#> [1522] "0233" "01" "022" "01" "01" "033" "01" "01" "01" "01" "01" "01" "021"
#> [1535] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1548] "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "021" "0233" "01"
#> [1561] "01" "01" "01" "0312" "022" "01" "01" "033" "01" "01" "01" "022" "021"
#> [1574] "01" "021" "033" "033" "021" "0233" "0233" "033" "021" "033" "0233" "033" "033"
#> [1587] "033" "033" "033" "033" "01" "033" "033" "022" "033" "021" "021" "0233" "01"
#> [1600] "021" "01" "033" "01" "021" "021" "01" "01" "0233" "021" "021" "033" "033"
#> [1613] "033" "033" "033" "033" "033" "021" "01" "022" "021" "0233" "01" "033" "01"
#> [1626] "021" "01" "01" "01" "01" "01" "01" "0323" "01" "021" "01" "01" "01"
#> [1639] "022" "01" "01" "01" "01" "0233" "01" "01" "01" "01" "01" "01" "01"
#> [1652] "01" "01" "0321" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1665] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "0231" "01"
#> [1678] "01" "01" "0233" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1691] "01" "01" "0322" "01" "01" "01" "01" "01" "01" "01" "01" "01" "0311"
#> [1704] "01" "01" "01" "0323" "01" "01" "01" "01" "0311" "01" "01" "01" "01"
#> [1717] "01" "01" "0323" "0323" "01" "01" "01" "01" "01" "01" "01" "0313" "01"
#> [1730] "0311" "0311" "01" "01" "021" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1743] "01" "01" "01" "01" "01" "01" "0323" "01" "0312" "01" "022" "01" "022"
#> [1756] "01" "01" "01" "01" "0312" "022" "01" "01" "01" "01" "022" "0311" "01"
#> [1769] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "0322"
#> [1782] "01" "01" "01" "01" "01" "0313" "01" "01" "0323" "01" "0313" "0313" "0323"
#> [1795] "022" "01" "01" "0313" "022" "01" "01" "01" "01" "0323" "01" "01" "0233"
#> [1808] "021" "022" "0311" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1821] "0313" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "01" "0323"
#> [1834] "01" "01" "021" "022" "01" "01" "01" "01" "01" "021" "01" "01" "01"
#> [1847] "01" "01" "01" "01" "01" "0323" "01" "01" "01" "01" "01" "01" "0322"
#> [1860] "01" "0312" "01" "01" "033" "022" "0312" "0321" "0323" "021" "01" "01" "0323"
#> [1873] "0323" "01" "01" "01" "0231" "01" "01" "01" "021" "01" "01" "01" "01"
#> [1886] "0323" "01" "0312" "01" "01" "01" "022" "01" "01" "01" "0311" "01" "01"
#> [1899] "01" "0312" "01" "0323" "01" "0311" "01" "01" "033" "01" "01" "01" "01"
#> [1912] "01" "01" "01" "0323" "01" "01" "01" "01" "0312" "022" "01" "0323" "0323"
#> [1925] "01" "0311" "01" "01" "01" "0233" "0323" "01" "021" "01" "0323" "0233" "033"
#> [1938] "01" "01" "0323" "0323" "01" "0323" "01" "033" "022" "0312" "0323" "01" "0312"
#> [1951] "0323" "01" "01" "021" "01" "01" "0233" "0323" "021" "0323" "022" "0323" "0323"
#> [1964] "022" "01" "0323" "021" "033" "0323" "0312" "01" "0233" "0312" "0323" "021" "0311"
#> [1977] "01" "021" "0323" "021" "0323" "0323" "033" "0232" "021" "0232" "021" "021" "021"
#> [1990] "021" "022" "021" "0231" "0232" "022" "021" "0231" "0231" "021" "0231" "022" "021"
#> [2003] "021" "0232" "021" "022" "022" "021" "0232" "0232" "021" "0231" "021" "0231" "01"
#> [2016] "022" "0231" "0321" "022" "021" "021" "022" "022" "022" "022" "0321" "022" "021"
#> [2029] "021" "022" "022" "021" "022" "0232" "022" "021" "022" "021" "022" "022" "022"
#> [2042] "0321" "021" "0233" "0232" "021" "021" "021" "021" "01" "022" "021" "0231" "021"
#> [2055] "021" "021" "022" "021" "0321" "022" "021" "022" "022" "021" "022" "022" "021"
#> [2068] "021" "021" "021" "021" "022" "0232" "022" "021" "0233" "021" "022" "021" "021"
#> [2081] "021" "021" "021" "0231" "021" "021" "021" "0232" "021" "022" "021" "022" "022"
#> [2094] "021" "022" "022" "022" "021" "022" "0231" "021" "021" "021" "021" "021" "021"
#> [2107] "021" "021" "0231" "021" "0231" "022" "021" "021" "021" "021" "022" "021" "0231"
#> [2120] "0232" "021" "021" "0233" "0232" "01" "022" "021" "01" "021" "021" "021" "021"
#> [2133] "021" "021" "021" "021" "021" "021" "022" "021" "022" "022" "021" "022" "022"
#> [2146] "022" "022" "021" "022" "021" "021" "021" "022" "022" "021" "021" "021" "022"
#> [2159] "022" "021" "022" "022" "022" "021" "022" "022" "022" "021" "022" "022" "022"
#> [2172] "022" "021" "0321" "022" "022" "022" "021" "021" "021" "021" "022" "0234" "022"
#> [2185] "022" "022" "022" "022" "022" "01" "022" "01" "022" "0312" "022" "0321" "022"
#> [2198] "021" "021" "022" "0232" "0231" "022" "0232" "0232" "022" "021" "021" "021" "0231"
#> [2211] "0232" "022" "0232" "022" "021" "021" "021" "021" "021" "021" "022" "021" "021"
#> [2224] "021" "022" "021" "021" "021" "022" "021" "022" "022" "022" "022" "021" "022"
#> [2237] "0321" "022" "022" "0321" "022" "0321" "022" "022" "022" "022" "022" "0231" "0231"
#> [2250] "022" "022" "0321" "022" "022" "0231" "0231" "022" "022" "01" "0321" "021" "022"
#> [2263] "022" "022" "022" "0321" "0231" "022" "0321" "022" "022" "022" "01" "022" "01"
#> [2276] "022" "022" "0321" "022" "0231" "022" "022" "01" "022" "022" "022" "01" "0321"
#> [2289] "0321" "022" "022" "0321" "022" "022" "01" "022" "022" "022" "01" "0321" "01"
#> [2302] "01" "01" "022" "01" "022" "01" "01" "01" "022" "01" "0321" "022" "01"
#> [2315] "01" "01" "01" "021" "0231" "022" "01" "022" "022" "0321" "022" "022" "022"
#> [2328] "022" "022" "022" "022" "022" "022" "022" "0321" "01" "01" "0321" "022" "01"
#> [2341] "0321" "0321" "022" "021" "0232" "022" "022" "022" "022" "022" "0321" "022" "022"
#> [2354] "022" "022" "022" "0321" "021" "021" "022" "021" "022" "021" "0234" "021" "021"
#> [2367] "022" "021" "021" "022" "022" "022" "021" "022" "021" "022" "022" "022" "021"
#> [2380] "021" "0232" "022" "021" "021" "0232" "022" "022" "022" "022" "0231" "021" "022"
#> [2393] "022" "022" "021" "021" "021" "022" "0321" "022" "01" "01" "01" "021" "022"
#> [2406] "0231" "021" "022" "021" "022" "021" "022" "021" "01" "022" "022" "022" "022"
#> [2419] "0231" "01" "022" "022" "0321" "021" "021" "0231" "022" "022" "022" "022" "022"
#> [2432] "022" "01" "0321" "01" "022" "0321" "0321" "0321" "01" "01" "01" "0321" "0321"
#> [2445] "0321" "022" "022" "022" "021" "022" "021" "021" "021" "021" "021" "021" "021"
#> [2458] "021" "021" "0231" "021" "021" "021" "021" "021" "021" "021" "021" "022" "022"
#> [2471] "0321" "022" "022" "022" "021" "021" "022" "021" "022" "022" "022" "021" "022"
#> [2484] "022" "021" "022" "021" "021" "021" "021" "021" "021" "021" "021" "022" "021"
#> [2497] "022" "021" "021" "022" "021" "022" "021" "022" "022" "022" "022" "0321" "0321"
#> [2510] "022" "0324" "021" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021"
#> [2523] "022" "022" "022" "021" "021" "022" "021" "021" "022" "021" "021" "01" "021"
#> [2536] "0321" "022" "022" "022" "0231" "022" "022" "022" "022" "0232" "022" "022" "022"
#> [2549] "01" "022" "0321" "0321" "01" "01" "021" "0321" "022" "01" "021" "021" "0321"
#> [2562] "021" "0321" "022" "022" "0321" "022" "022" "022" "022" "022" "01" "01" "01"
#> [2575] "0321" "0321" "022" "022" "021" "021" "021" "022" "022" "022" "022" "022" "022"
#> [2588] "01" "021" "0232" "0234" "0232" "021" "021" "021" "021" "021" "021" "0231" "021"
#> [2601] "021" "021" "0232" "021" "0232" "0231" "0234" "0232" "0323" "01" "0232" "021" "0231"
#> [2614] "022" "022" "0321" "022" "0231" "0231" "0234" "0233" "0232" "01" "021" "022" "0231"
#> [2627] "01" "01" "01" "0231" "021" "021" "021" "021" "021" "022" "021" "022" "022"
#> [2640] "022" "022" "0321" "022" "022" "021" "022" "022" "022" "0321" "022" "01" "022"
#> [2653] "022" "022" "022" "022" "0231" "022" "022" "022" "022" "022" "022" "0321" "0321"
#> [2666] "022" "0321" "022" "022" "0321" "022" "01" "0321" "022" "0321" "022" "022" "0324"
#> [2679] "01" "01" "01" "022" "01" "01" "01" "0232" "0232" "021" "021" "0321" "021"
#> [2692] "021" "0234" "0231" "01" "022" "0324" "021" "022" "0321" "022" "021" "01" "022"
#> [2705] "022" "0321" "01" "022" "01" "01" "022" "01" "01" "0231" "022" "0231" "01"
#> [2718] "01" "0231" "01" "022" "022" "01" "021" "0321" "01" "0321" "01" "01" "0321"
#> [2731] "021" "022" "0321" "022" "0321" "01" "01" "0321" "01" "0321" "01" "01" "021"
#> [2744] "022" "022" "01" "01" "01" "01" "0321" "01" "01" "022" "022" "0321" "0323"
#> [2757] "01" "021" "021" "021" "021" "021" "021" "021" "0232" "01" "01" "022" "021"
#> [2770] "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "0231"
#> [2783] "021" "021" "021" "0232" "021" "0232" "021" "01" "01" "022" "0231" "0231" "021"
#> [2796] "021" "021" "021" "021" "021" "021" "021" "0233" "021" "021" "021" "021" "0232"
#> [2809] "021" "021" "021" "021" "021" "021" "021" "021" "022" "01" "01" "01" "022"
#> [2822] "021" "0231" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021" "021"
#> [2835] "0231" "022" "021" "021" "0232" "0231" "021" "021" "021" "021" "021" "021" "021"
#> [2848] "021" "021" "021" "021" "021" "0321" "0231" "01" "022" "021" "01" "022" "021"
#> [2861] "0231" "0232" "022" "022" "021" "021" "0231" "022" "021" "022" "021" "021" "021"
#> [2874] "021" "021" "021" "022" "021" "01" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3273))
#> [1] "01" "01" "023" "0322" "01" "01" "0322" "01" "01" "01" "01" "01" "0322"
#> [14] "01" "01" "01" "01" "01" "0313" "01" "0322" "01" "01" "0322" "01" "0322"
#> [27] "0322" "0322" "01" "0312" "0322" "01" "0322" "0322" "0322" "01" "01" "01" "0312"
#> [40] "0311" "01" "022" "01" "0311" "01" "01" "0311" "0312" "0322" "01" "0312" "01"
#> [53] "01" "01" "01" "021" "021" "01" "01" "0313" "0322" "01" "01" "022" "0311"
#> [66] "01" "01" "01" "0322" "01" "0322" "0311" "0322" "01" "01" "01" "01" "01"
#> [79] "022" "01" "01" "0322" "01" "01" "01" "022" "01" "01" "01" "01" "0322"
#> [92] "01" "021" "01" "01" "01" "01" "01" "01" "0313" "01" "01" "01" "01"
#> [105] "01" "0322" "022" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022"
#> [118] "0322" "01" "0321" "0313" "0322" "022" "022" "022" "023" "01" "01" "01" "01"
#> [131] "01" "01" "01" "0324" "01" "022" "01" "01" "0322" "01" "01" "01" "01"
#> [144] "01" "01" "022" "01" "021" "01" "0324" "0313" "0313" "01" "0313" "0322" "01"
#> [157] "0313" "023" "0322" "0322" "0322" "01" "0313" "0313" "022" "01" "0322" "0313" "01"
#> [170] "01" "0322" "0313" "0313" "022" "022" "0313" "0313" "01" "0313" "0313" "0312" "0313"
#> [183] "0322" "0313" "0322" "0313" "0313" "0313" "0312" "022" "0322" "01" "0313" "0312" "0313"
#> [196] "0322" "0312" "0312" "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "0322" "0313"
#> [209] "0322" "022" "0313" "023" "0313" "0312" "0313" "0313" "0322" "022" "0312" "01" "0312"
#> [222] "0313" "0312" "0313" "0312" "0313" "0312" "0312" "01" "021" "0313" "0313" "0312" "0313"
#> [235] "021" "0312" "0312" "0313" "0312" "0313" "0313" "0313" "0312" "01" "0312" "0312" "0312"
#> [248] "022" "0312" "0313" "01" "0313" "0312" "0312" "0313" "0313" "01" "0312" "0313" "0313"
#> [261] "01" "0313" "01" "01" "01" "0313" "01" "01" "01" "0313" "01" "01" "0313"
#> [274] "0313" "01" "0313" "0313" "0313" "0313" "0322" "021" "01" "0313" "0313" "0313" "022"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "021"
#> [300] "0312" "021" "0313" "0313" "023" "0313" "021" "022" "0312" "022" "0312" "0312" "0313"
#> [313] "022" "0313" "0313" "0312" "01" "0313" "0313" "01" "0313" "01" "0312" "0313" "0311"
#> [326] "01" "0313" "0313" "0313" "0313" "0313" "01" "01" "01" "01" "022" "022" "01"
#> [339] "0313" "01" "0313" "0313" "022" "0313" "0313" "0313" "022" "0311" "0311" "022" "0313"
#> [352] "01" "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313" "0313"
#> [365] "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312" "0312" "0312" "0312"
#> [378] "0312" "0312" "0313" "01" "01" "022" "0311" "01" "01" "01" "0311" "0324" "0311"
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#> [534] "022" "01" "01" "01" "021" "01" "0311" "01" "022" "01" "01" "022" "01"
#> [547] "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "0311" "0311"
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#> [729] "01" "01" "0311" "01" "01" "0311" "01" "01" "01" "01" "01" "0311" "01"
#> [742] "01" "01" "0311" "0311" "01" "0311" "0312" "0312" "0312" "0311" "01" "0311" "01"
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#> [768] "01" "01" "01" "0312" "01" "01" "0311" "01" "022" "01" "01" "021" "01"
#> [781] "0311" "01" "01" "01" "01" "01" "0312" "01" "0311" "0311" "01" "01" "0312"
#> [794] "022" "01" "0312" "01" "01" "023" "0311" "0312" "022" "0311" "0311" "0311" "023"
#> [807] "0311" "01" "0311" "01" "01" "01" "0324" "0324" "01" "01" "01" "01" "01"
#> [820] "021" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [833] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [846] "01" "01" "01" "0324" "01" "01" "023" "0324" "022" "01" "01" "01" "01"
#> [859] "0324" "01" "01" "0324" "01" "01" "01" "01" "01" "01" "01" "0311" "01"
#> [872] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [885] "01" "021" "01" "0324" "01" "0324" "01" "0311" "01" "01" "01" "022" "022"
#> [898] "01" "01" "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01"
#> [911] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [924] "01" "01" "01" "01" "01" "022" "01" "01" "01" "01" "01" "022" "0324"
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#> [950] "01" "01" "01" "01" "0311" "01" "021" "01" "01" "01" "021" "022" "01"
#> [963] "01" "021" "0311" "01" "01" "01" "01" "0311" "01" "01" "022" "01" "01"
#> [976] "01" "01" "01" "01" "01" "0324" "0324" "022" "022" "0311" "01" "01" "01"
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#> [1028] "01" "01" "01" "01" "01" "01" "01" "01" "0311" "01" "0312" "01" "021"
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#> [1054] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1067] "01" "01" "01" "01" "01" "01" "01" "01" "0322" "01" "01" "01" "01"
#> [1080] "01" "01" "01" "0312" "01" "01" "01" "01" "0311" "01" "01" "01" "01"
#> [1093] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1106] "01" "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "0313"
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#> [1145] "0322" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1158] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
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#> [1197] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
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#> [1301] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022"
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#> [1899] "01" "0312" "01" "0323" "01" "0311" "01" "01" "033" "01" "01" "01" "01"
#> [1912] "01" "01" "01" "0323" "01" "01" "01" "01" "0312" "022" "01" "0323" "0323"
#> [1925] "01" "0311" "01" "01" "01" "023" "0323" "01" "021" "01" "0323" "023" "033"
#> [1938] "01" "01" "0323" "0323" "01" "0323" "01" "033" "022" "0312" "0323" "01" "0312"
#> [1951] "0323" "01" "01" "021" "01" "01" "023" "0323" "021" "0323" "022" "0323" "0323"
#> [1964] "022" "01" "0323" "021" "033" "0323" "0312" "01" "023" "0312" "0323" "021" "0311"
#> [1977] "01" "021" "0323" "021" "0323" "0323" "033" "023" "021" "023" "021" "021" "021"
#> [1990] "021" "022" "021" "023" "023" "022" "021" "023" "023" "021" "023" "022" "021"
#> [2003] "021" "023" "021" "022" "022" "021" "023" "023" "021" "023" "021" "023" "01"
#> [2016] "022" "023" "0321" "022" "021" "021" "022" "022" "022" "022" "0321" "022" "021"
#> [2029] "021" "022" "022" "021" "022" "023" "022" "021" "022" "021" "022" "022" "022"
#> [2042] "0321" "021" "023" "023" "021" "021" "021" "021" "01" "022" "021" "023" "021"
#> [2055] "021" "021" "022" "021" "0321" "022" "021" "022" "022" "021" "022" "022" "021"
#> [2068] "021" "021" "021" "021" "022" "023" "022" "021" "023" "021" "022" "021" "021"
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#> [2107] "021" "021" "023" "021" "023" "022" "021" "021" "021" "021" "022" "021" "023"
#> [2120] "023" "021" "021" "023" "023" "01" "022" "021" "01" "021" "021" "021" "021"
#> [2133] "021" "021" "021" "021" "021" "021" "022" "021" "022" "022" "021" "022" "022"
#> [2146] "022" "022" "021" "022" "021" "021" "021" "022" "022" "021" "021" "021" "022"
#> [2159] "022" "021" "022" "022" "022" "021" "022" "022" "022" "021" "022" "022" "022"
#> [2172] "022" "021" "0321" "022" "022" "022" "021" "021" "021" "021" "022" "023" "022"
#> [2185] "022" "022" "022" "022" "022" "01" "022" "01" "022" "0312" "022" "0321" "022"
#> [2198] "021" "021" "022" "023" "023" "022" "023" "023" "022" "021" "021" "021" "023"
#> [2211] "023" "022" "023" "022" "021" "021" "021" "021" "021" "021" "022" "021" "021"
#> [2224] "021" "022" "021" "021" "021" "022" "021" "022" "022" "022" "022" "021" "022"
#> [2237] "0321" "022" "022" "0321" "022" "0321" "022" "022" "022" "022" "022" "023" "023"
#> [2250] "022" "022" "0321" "022" "022" "023" "023" "022" "022" "01" "0321" "021" "022"
#> [2263] "022" "022" "022" "0321" "023" "022" "0321" "022" "022" "022" "01" "022" "01"
#> [2276] "022" "022" "0321" "022" "023" "022" "022" "01" "022" "022" "022" "01" "0321"
#> [2289] "0321" "022" "022" "0321" "022" "022" "01" "022" "022" "022" "01" "0321" "01"
#> [2302] "01" "01" "022" "01" "022" "01" "01" "01" "022" "01" "0321" "022" "01"
#> [2315] "01" "01" "01" "021" "023" "022" "01" "022" "022" "0321" "022" "022" "022"
#> [2328] "022" "022" "022" "022" "022" "022" "022" "0321" "01" "01" "0321" "022" "01"
#> [2341] "0321" "0321" "022" "021" "023" "022" "022" "022" "022" "022" "0321" "022" "022"
#> [2354] "022" "022" "022" "0321" "021" "021" "022" "021" "022" "021" "023" "021" "021"
#> [2367] "022" "021" "021" "022" "022" "022" "021" "022" "021" "022" "022" "022" "021"
#> [2380] "021" "023" "022" "021" "021" "023" "022" "022" "022" "022" "023" "021" "022"
#> [2393] "022" "022" "021" "021" "021" "022" "0321" "022" "01" "01" "01" "021" "022"
#> [2406] "023" "021" "022" "021" "022" "021" "022" "021" "01" "022" "022" "022" "022"
#> [2419] "023" "01" "022" "022" "0321" "021" "021" "023" "022" "022" "022" "022" "022"
#> [2432] "022" "01" "0321" "01" "022" "0321" "0321" "0321" "01" "01" "01" "0321" "0321"
#> [2445] "0321" "022" "022" "022" "021" "022" "021" "021" "021" "021" "021" "021" "021"
#> [2458] "021" "021" "023" "021" "021" "021" "021" "021" "021" "021" "021" "022" "022"
#> [2471] "0321" "022" "022" "022" "021" "021" "022" "021" "022" "022" "022" "021" "022"
#> [2484] "022" "021" "022" "021" "021" "021" "021" "021" "021" "021" "021" "022" "021"
#> [2497] "022" "021" "021" "022" "021" "022" "021" "022" "022" "022" "022" "0321" "0321"
#> [2510] "022" "0324" "021" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021"
#> [2523] "022" "022" "022" "021" "021" "022" "021" "021" "022" "021" "021" "01" "021"
#> [2536] "0321" "022" "022" "022" "023" "022" "022" "022" "022" "023" "022" "022" "022"
#> [2549] "01" "022" "0321" "0321" "01" "01" "021" "0321" "022" "01" "021" "021" "0321"
#> [2562] "021" "0321" "022" "022" "0321" "022" "022" "022" "022" "022" "01" "01" "01"
#> [2575] "0321" "0321" "022" "022" "021" "021" "021" "022" "022" "022" "022" "022" "022"
#> [2588] "01" "021" "023" "023" "023" "021" "021" "021" "021" "021" "021" "023" "021"
#> [2601] "021" "021" "023" "021" "023" "023" "023" "023" "0323" "01" "023" "021" "023"
#> [2614] "022" "022" "0321" "022" "023" "023" "023" "023" "023" "01" "021" "022" "023"
#> [2627] "01" "01" "01" "023" "021" "021" "021" "021" "021" "022" "021" "022" "022"
#> [2640] "022" "022" "0321" "022" "022" "021" "022" "022" "022" "0321" "022" "01" "022"
#> [2653] "022" "022" "022" "022" "023" "022" "022" "022" "022" "022" "022" "0321" "0321"
#> [2666] "022" "0321" "022" "022" "0321" "022" "01" "0321" "022" "0321" "022" "022" "0324"
#> [2679] "01" "01" "01" "022" "01" "01" "01" "023" "023" "021" "021" "0321" "021"
#> [2692] "021" "023" "023" "01" "022" "0324" "021" "022" "0321" "022" "021" "01" "022"
#> [2705] "022" "0321" "01" "022" "01" "01" "022" "01" "01" "023" "022" "023" "01"
#> [2718] "01" "023" "01" "022" "022" "01" "021" "0321" "01" "0321" "01" "01" "0321"
#> [2731] "021" "022" "0321" "022" "0321" "01" "01" "0321" "01" "0321" "01" "01" "021"
#> [2744] "022" "022" "01" "01" "01" "01" "0321" "01" "01" "022" "022" "0321" "0323"
#> [2757] "01" "021" "021" "021" "021" "021" "021" "021" "023" "01" "01" "022" "021"
#> [2770] "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "023"
#> [2783] "021" "021" "021" "023" "021" "023" "021" "01" "01" "022" "023" "023" "021"
#> [2796] "021" "021" "021" "021" "021" "021" "021" "023" "021" "021" "021" "021" "023"
#> [2809] "021" "021" "021" "021" "021" "021" "021" "021" "022" "01" "01" "01" "022"
#> [2822] "021" "023" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021" "021"
#> [2835] "023" "022" "021" "021" "023" "023" "021" "021" "021" "021" "021" "021" "021"
#> [2848] "021" "021" "021" "021" "021" "0321" "023" "01" "022" "021" "01" "022" "021"
#> [2861] "023" "023" "022" "022" "021" "021" "023" "022" "021" "022" "021" "021" "021"
#> [2874] "021" "021" "021" "022" "021" "01" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3301))
#> [1] "01" "01" "023" "032" "01" "01" "032" "01" "01" "01" "01" "01" "032"
#> [14] "01" "01" "01" "01" "01" "0313" "01" "032" "01" "01" "032" "01" "032"
#> [27] "032" "032" "01" "0312" "032" "01" "032" "032" "032" "01" "01" "01" "0312"
#> [40] "0311" "01" "022" "01" "0311" "01" "01" "0311" "0312" "032" "01" "0312" "01"
#> [53] "01" "01" "01" "021" "021" "01" "01" "0313" "032" "01" "01" "022" "0311"
#> [66] "01" "01" "01" "032" "01" "032" "0311" "032" "01" "01" "01" "01" "01"
#> [79] "022" "01" "01" "032" "01" "01" "01" "022" "01" "01" "01" "01" "032"
#> [92] "01" "021" "01" "01" "01" "01" "01" "01" "0313" "01" "01" "01" "01"
#> [105] "01" "032" "022" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022"
#> [118] "032" "01" "032" "0313" "032" "022" "022" "022" "023" "01" "01" "01" "01"
#> [131] "01" "01" "01" "032" "01" "022" "01" "01" "032" "01" "01" "01" "01"
#> [144] "01" "01" "022" "01" "021" "01" "032" "0313" "0313" "01" "0313" "032" "01"
#> [157] "0313" "023" "032" "032" "032" "01" "0313" "0313" "022" "01" "032" "0313" "01"
#> [170] "01" "032" "0313" "0313" "022" "022" "0313" "0313" "01" "0313" "0313" "0312" "0313"
#> [183] "032" "0313" "032" "0313" "0313" "0313" "0312" "022" "032" "01" "0313" "0312" "0313"
#> [196] "032" "0312" "0312" "0312" "0312" "0312" "0313" "0312" "0313" "0312" "0312" "032" "0313"
#> [209] "032" "022" "0313" "023" "0313" "0312" "0313" "0313" "032" "022" "0312" "01" "0312"
#> [222] "0313" "0312" "0313" "0312" "0313" "0312" "0312" "01" "021" "0313" "0313" "0312" "0313"
#> [235] "021" "0312" "0312" "0313" "0312" "0313" "0313" "0313" "0312" "01" "0312" "0312" "0312"
#> [248] "022" "0312" "0313" "01" "0313" "0312" "0312" "0313" "0313" "01" "0312" "0313" "0313"
#> [261] "01" "0313" "01" "01" "01" "0313" "01" "01" "01" "0313" "01" "01" "0313"
#> [274] "0313" "01" "0313" "0313" "0313" "0313" "032" "021" "01" "0313" "0313" "0313" "022"
#> [287] "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "0312" "021"
#> [300] "0312" "021" "0313" "0313" "023" "0313" "021" "022" "0312" "022" "0312" "0312" "0313"
#> [313] "022" "0313" "0313" "0312" "01" "0313" "0313" "01" "0313" "01" "0312" "0313" "0311"
#> [326] "01" "0313" "0313" "0313" "0313" "0313" "01" "01" "01" "01" "022" "022" "01"
#> [339] "0313" "01" "0313" "0313" "022" "0313" "0313" "0313" "022" "0311" "0311" "022" "0313"
#> [352] "01" "0313" "0313" "0313" "0313" "0312" "0312" "0312" "0312" "0312" "0313" "0313" "0313"
#> [365] "0313" "0313" "0313" "0312" "0312" "0312" "0313" "0312" "0312" "0312" "0312" "0312" "0312"
#> [378] "0312" "0312" "0313" "01" "01" "022" "0311" "01" "01" "01" "0311" "032" "0311"
#> [391] "01" "0311" "01" "0311" "022" "0311" "0313" "0311" "01" "01" "0311" "01" "01"
#> [404] "0311" "01" "022" "01" "0311" "01" "01" "0311" "0311" "021" "01" "01" "01"
#> [417] "01" "01" "0311" "01" "01" "01" "01" "0313" "023" "01" "01" "01" "01"
#> [430] "01" "01" "023" "01" "023" "01" "022" "01" "021" "01" "023" "023" "0311"
#> [443] "0311" "0311" "0311" "01" "01" "0312" "0312" "0312" "0311" "01" "0311" "01" "01"
#> [456] "0312" "01" "01" "0312" "0312" "0311" "0312" "0311" "0311" "0312" "0312" "0312" "0311"
#> [469] "0312" "0312" "0312" "0311" "0312" "0312" "0312" "0311" "0311" "0312" "0311" "0311" "0311"
#> [482] "0311" "0311" "01" "021" "0312" "0311" "0311" "022" "022" "021" "0312" "0312" "022"
#> [495] "0312" "01" "021" "021" "01" "0312" "021" "01" "0311" "0312" "0311" "021" "01"
#> [508] "0311" "0311" "0311" "0311" "022" "0311" "0311" "01" "01" "022" "0311" "0312" "0311"
#> [521] "01" "01" "01" "0312" "0312" "0313" "0312" "01" "01" "022" "021" "021" "01"
#> [534] "022" "01" "01" "01" "021" "01" "0311" "01" "022" "01" "01" "022" "01"
#> [547] "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "0311" "0311"
#> [560] "01" "0311" "01" "032" "032" "01" "01" "01" "022" "0311" "01" "01" "022"
#> [573] "032" "0311" "01" "0312" "01" "01" "01" "022" "01" "01" "01" "01" "0311"
#> [586] "01" "0311" "01" "01" "01" "01" "0313" "01" "0312" "0313" "032" "01" "0313"
#> [599] "0313" "01" "01" "01" "01" "0313" "01" "01" "022" "01" "01" "01" "01"
#> [612] "01" "01" "01" "01" "023" "0311" "0311" "01" "0311" "0311" "0313" "01" "0311"
#> [625] "01" "0311" "0311" "0311" "0311" "0311" "01" "01" "0313" "01" "0311" "021" "0311"
#> [638] "01" "01" "01" "01" "0311" "0311" "01" "0311" "0312" "01" "01" "022" "01"
#> [651] "01" "01" "01" "01" "0311" "01" "01" "01" "0311" "0311" "0311" "01" "023"
#> [664] "01" "01" "0312" "0311" "0311" "0311" "0311" "022" "01" "022" "0311" "0313" "023"
#> [677] "0311" "0311" "022" "01" "0311" "0311" "0312" "0312" "0311" "0312" "0312" "0312" "0311"
#> [690] "0311" "0311" "01" "0312" "0311" "022" "0311" "0312" "01" "0312" "01" "0312" "01"
#> [703] "01" "0311" "0311" "022" "022" "01" "032" "01" "032" "021" "01" "01" "01"
#> [716] "01" "01" "01" "022" "0311" "0311" "022" "023" "01" "022" "0311" "0311" "0311"
#> [729] "01" "01" "0311" "01" "01" "0311" "01" "01" "01" "01" "01" "0311" "01"
#> [742] "01" "01" "0311" "0311" "01" "0311" "0312" "0312" "0312" "0311" "01" "0311" "01"
#> [755] "01" "01" "0311" "01" "032" "0311" "021" "022" "01" "01" "0311" "01" "01"
#> [768] "01" "01" "01" "0312" "01" "01" "0311" "01" "022" "01" "01" "021" "01"
#> [781] "0311" "01" "01" "01" "01" "01" "0312" "01" "0311" "0311" "01" "01" "0312"
#> [794] "022" "01" "0312" "01" "01" "023" "0311" "0312" "022" "0311" "0311" "0311" "023"
#> [807] "0311" "01" "0311" "01" "01" "01" "032" "032" "01" "01" "01" "01" "01"
#> [820] "021" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [833] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [846] "01" "01" "01" "032" "01" "01" "023" "032" "022" "01" "01" "01" "01"
#> [859] "032" "01" "01" "032" "01" "01" "01" "01" "01" "01" "01" "0311" "01"
#> [872] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [885] "01" "021" "01" "032" "01" "032" "01" "0311" "01" "01" "01" "022" "022"
#> [898] "01" "01" "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01"
#> [911] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [924] "01" "01" "01" "01" "01" "022" "01" "01" "01" "01" "01" "022" "032"
#> [937] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [950] "01" "01" "01" "01" "0311" "01" "021" "01" "01" "01" "021" "022" "01"
#> [963] "01" "021" "0311" "01" "01" "01" "01" "0311" "01" "01" "022" "01" "01"
#> [976] "01" "01" "01" "01" "01" "032" "032" "022" "022" "0311" "01" "01" "01"
#> [989] "01" "0311" "0311" "01" "01" "01" "0311" "01" "01" "032" "032" "01" "022"
#> [1002] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1015] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "032" "01"
#> [1028] "01" "01" "01" "01" "01" "01" "01" "01" "0311" "01" "0312" "01" "021"
#> [1041] "01" "01" "032" "01" "01" "01" "0312" "01" "01" "01" "01" "01" "01"
#> [1054] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1067] "01" "01" "01" "01" "01" "01" "01" "01" "032" "01" "01" "01" "01"
#> [1080] "01" "01" "01" "0312" "01" "01" "01" "01" "0311" "01" "01" "01" "01"
#> [1093] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1106] "01" "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "0313"
#> [1119] "01" "01" "01" "0313" "01" "01" "01" "01" "01" "0312" "01" "01" "01"
#> [1132] "01" "01" "01" "032" "01" "01" "01" "01" "01" "022" "01" "01" "01"
#> [1145] "032" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1158] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1171] "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "01"
#> [1184] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1197] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1210] "01" "01" "01" "01" "01" "022" "01" "032" "0313" "01" "01" "01" "01"
#> [1223] "01" "022" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1236] "01" "01" "0313" "01" "01" "033" "032" "0311" "01" "01" "01" "01" "01"
#> [1249] "01" "01" "01" "01" "01" "01" "01" "022" "022" "022" "022" "01" "01"
#> [1262] "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "033" "01"
#> [1275] "01" "01" "033" "021" "033" "033" "033" "01" "033" "01" "01" "01" "01"
#> [1288] "01" "033" "033" "01" "033" "01" "01" "023" "033" "01" "01" "01" "01"
#> [1301] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1314] "033" "022" "01" "033" "021" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1327] "033" "01" "01" "01" "01" "021" "01" "033" "01" "033" "01" "01" "01"
#> [1340] "01" "01" "01" "022" "01" "01" "022" "023" "021" "01" "01" "01" "01"
#> [1353] "021" "022" "01" "01" "01" "01" "01" "01" "021" "021" "01" "01" "033"
#> [1366] "033" "01" "033" "01" "033" "033" "033" "033" "01" "033" "01" "01" "01"
#> [1379] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1392] "032" "01" "01" "023" "01" "021" "023" "023" "01" "023" "01" "01" "01"
#> [1405] "01" "033" "023" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01"
#> [1418] "01" "033" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "033" "0311"
#> [1431] "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1444] "01" "01" "01" "01" "01" "023" "01" "033" "022" "01" "01" "022" "01"
#> [1457] "01" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1470] "01" "01" "033" "01" "0313" "033" "0313" "01" "021" "01" "022" "01" "01"
#> [1483] "01" "01" "01" "01" "01" "032" "01" "01" "01" "01" "01" "01" "023"
#> [1496] "01" "01" "01" "01" "023" "01" "01" "01" "01" "01" "032" "01" "022"
#> [1509] "01" "022" "01" "01" "01" "01" "01" "01" "01" "022" "033" "022" "023"
#> [1522] "023" "01" "022" "01" "01" "033" "01" "01" "01" "01" "01" "01" "021"
#> [1535] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1548] "01" "01" "01" "0311" "01" "01" "01" "01" "01" "01" "021" "023" "01"
#> [1561] "01" "01" "01" "0312" "022" "01" "01" "033" "01" "01" "01" "022" "021"
#> [1574] "01" "021" "033" "033" "021" "023" "023" "033" "021" "033" "023" "033" "033"
#> [1587] "033" "033" "033" "033" "01" "033" "033" "022" "033" "021" "021" "023" "01"
#> [1600] "021" "01" "033" "01" "021" "021" "01" "01" "023" "021" "021" "033" "033"
#> [1613] "033" "033" "033" "033" "033" "021" "01" "022" "021" "023" "01" "033" "01"
#> [1626] "021" "01" "01" "01" "01" "01" "01" "032" "01" "021" "01" "01" "01"
#> [1639] "022" "01" "01" "01" "01" "023" "01" "01" "01" "01" "01" "01" "01"
#> [1652] "01" "01" "032" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1665] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "023" "01"
#> [1678] "01" "01" "023" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1691] "01" "01" "032" "01" "01" "01" "01" "01" "01" "01" "01" "01" "0311"
#> [1704] "01" "01" "01" "032" "01" "01" "01" "01" "0311" "01" "01" "01" "01"
#> [1717] "01" "01" "032" "032" "01" "01" "01" "01" "01" "01" "01" "0313" "01"
#> [1730] "0311" "0311" "01" "01" "021" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1743] "01" "01" "01" "01" "01" "01" "032" "01" "0312" "01" "022" "01" "022"
#> [1756] "01" "01" "01" "01" "0312" "022" "01" "01" "01" "01" "022" "0311" "01"
#> [1769] "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "032"
#> [1782] "01" "01" "01" "01" "01" "0313" "01" "01" "032" "01" "0313" "0313" "032"
#> [1795] "022" "01" "01" "0313" "022" "01" "01" "01" "01" "032" "01" "01" "023"
#> [1808] "021" "022" "0311" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1821] "0313" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "01" "032"
#> [1834] "01" "01" "021" "022" "01" "01" "01" "01" "01" "021" "01" "01" "01"
#> [1847] "01" "01" "01" "01" "01" "032" "01" "01" "01" "01" "01" "01" "032"
#> [1860] "01" "0312" "01" "01" "033" "022" "0312" "032" "032" "021" "01" "01" "032"
#> [1873] "032" "01" "01" "01" "023" "01" "01" "01" "021" "01" "01" "01" "01"
#> [1886] "032" "01" "0312" "01" "01" "01" "022" "01" "01" "01" "0311" "01" "01"
#> [1899] "01" "0312" "01" "032" "01" "0311" "01" "01" "033" "01" "01" "01" "01"
#> [1912] "01" "01" "01" "032" "01" "01" "01" "01" "0312" "022" "01" "032" "032"
#> [1925] "01" "0311" "01" "01" "01" "023" "032" "01" "021" "01" "032" "023" "033"
#> [1938] "01" "01" "032" "032" "01" "032" "01" "033" "022" "0312" "032" "01" "0312"
#> [1951] "032" "01" "01" "021" "01" "01" "023" "032" "021" "032" "022" "032" "032"
#> [1964] "022" "01" "032" "021" "033" "032" "0312" "01" "023" "0312" "032" "021" "0311"
#> [1977] "01" "021" "032" "021" "032" "032" "033" "023" "021" "023" "021" "021" "021"
#> [1990] "021" "022" "021" "023" "023" "022" "021" "023" "023" "021" "023" "022" "021"
#> [2003] "021" "023" "021" "022" "022" "021" "023" "023" "021" "023" "021" "023" "01"
#> [2016] "022" "023" "032" "022" "021" "021" "022" "022" "022" "022" "032" "022" "021"
#> [2029] "021" "022" "022" "021" "022" "023" "022" "021" "022" "021" "022" "022" "022"
#> [2042] "032" "021" "023" "023" "021" "021" "021" "021" "01" "022" "021" "023" "021"
#> [2055] "021" "021" "022" "021" "032" "022" "021" "022" "022" "021" "022" "022" "021"
#> [2068] "021" "021" "021" "021" "022" "023" "022" "021" "023" "021" "022" "021" "021"
#> [2081] "021" "021" "021" "023" "021" "021" "021" "023" "021" "022" "021" "022" "022"
#> [2094] "021" "022" "022" "022" "021" "022" "023" "021" "021" "021" "021" "021" "021"
#> [2107] "021" "021" "023" "021" "023" "022" "021" "021" "021" "021" "022" "021" "023"
#> [2120] "023" "021" "021" "023" "023" "01" "022" "021" "01" "021" "021" "021" "021"
#> [2133] "021" "021" "021" "021" "021" "021" "022" "021" "022" "022" "021" "022" "022"
#> [2146] "022" "022" "021" "022" "021" "021" "021" "022" "022" "021" "021" "021" "022"
#> [2159] "022" "021" "022" "022" "022" "021" "022" "022" "022" "021" "022" "022" "022"
#> [2172] "022" "021" "032" "022" "022" "022" "021" "021" "021" "021" "022" "023" "022"
#> [2185] "022" "022" "022" "022" "022" "01" "022" "01" "022" "0312" "022" "032" "022"
#> [2198] "021" "021" "022" "023" "023" "022" "023" "023" "022" "021" "021" "021" "023"
#> [2211] "023" "022" "023" "022" "021" "021" "021" "021" "021" "021" "022" "021" "021"
#> [2224] "021" "022" "021" "021" "021" "022" "021" "022" "022" "022" "022" "021" "022"
#> [2237] "032" "022" "022" "032" "022" "032" "022" "022" "022" "022" "022" "023" "023"
#> [2250] "022" "022" "032" "022" "022" "023" "023" "022" "022" "01" "032" "021" "022"
#> [2263] "022" "022" "022" "032" "023" "022" "032" "022" "022" "022" "01" "022" "01"
#> [2276] "022" "022" "032" "022" "023" "022" "022" "01" "022" "022" "022" "01" "032"
#> [2289] "032" "022" "022" "032" "022" "022" "01" "022" "022" "022" "01" "032" "01"
#> [2302] "01" "01" "022" "01" "022" "01" "01" "01" "022" "01" "032" "022" "01"
#> [2315] "01" "01" "01" "021" "023" "022" "01" "022" "022" "032" "022" "022" "022"
#> [2328] "022" "022" "022" "022" "022" "022" "022" "032" "01" "01" "032" "022" "01"
#> [2341] "032" "032" "022" "021" "023" "022" "022" "022" "022" "022" "032" "022" "022"
#> [2354] "022" "022" "022" "032" "021" "021" "022" "021" "022" "021" "023" "021" "021"
#> [2367] "022" "021" "021" "022" "022" "022" "021" "022" "021" "022" "022" "022" "021"
#> [2380] "021" "023" "022" "021" "021" "023" "022" "022" "022" "022" "023" "021" "022"
#> [2393] "022" "022" "021" "021" "021" "022" "032" "022" "01" "01" "01" "021" "022"
#> [2406] "023" "021" "022" "021" "022" "021" "022" "021" "01" "022" "022" "022" "022"
#> [2419] "023" "01" "022" "022" "032" "021" "021" "023" "022" "022" "022" "022" "022"
#> [2432] "022" "01" "032" "01" "022" "032" "032" "032" "01" "01" "01" "032" "032"
#> [2445] "032" "022" "022" "022" "021" "022" "021" "021" "021" "021" "021" "021" "021"
#> [2458] "021" "021" "023" "021" "021" "021" "021" "021" "021" "021" "021" "022" "022"
#> [2471] "032" "022" "022" "022" "021" "021" "022" "021" "022" "022" "022" "021" "022"
#> [2484] "022" "021" "022" "021" "021" "021" "021" "021" "021" "021" "021" "022" "021"
#> [2497] "022" "021" "021" "022" "021" "022" "021" "022" "022" "022" "022" "032" "032"
#> [2510] "022" "032" "021" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021"
#> [2523] "022" "022" "022" "021" "021" "022" "021" "021" "022" "021" "021" "01" "021"
#> [2536] "032" "022" "022" "022" "023" "022" "022" "022" "022" "023" "022" "022" "022"
#> [2549] "01" "022" "032" "032" "01" "01" "021" "032" "022" "01" "021" "021" "032"
#> [2562] "021" "032" "022" "022" "032" "022" "022" "022" "022" "022" "01" "01" "01"
#> [2575] "032" "032" "022" "022" "021" "021" "021" "022" "022" "022" "022" "022" "022"
#> [2588] "01" "021" "023" "023" "023" "021" "021" "021" "021" "021" "021" "023" "021"
#> [2601] "021" "021" "023" "021" "023" "023" "023" "023" "032" "01" "023" "021" "023"
#> [2614] "022" "022" "032" "022" "023" "023" "023" "023" "023" "01" "021" "022" "023"
#> [2627] "01" "01" "01" "023" "021" "021" "021" "021" "021" "022" "021" "022" "022"
#> [2640] "022" "022" "032" "022" "022" "021" "022" "022" "022" "032" "022" "01" "022"
#> [2653] "022" "022" "022" "022" "023" "022" "022" "022" "022" "022" "022" "032" "032"
#> [2666] "022" "032" "022" "022" "032" "022" "01" "032" "022" "032" "022" "022" "032"
#> [2679] "01" "01" "01" "022" "01" "01" "01" "023" "023" "021" "021" "032" "021"
#> [2692] "021" "023" "023" "01" "022" "032" "021" "022" "032" "022" "021" "01" "022"
#> [2705] "022" "032" "01" "022" "01" "01" "022" "01" "01" "023" "022" "023" "01"
#> [2718] "01" "023" "01" "022" "022" "01" "021" "032" "01" "032" "01" "01" "032"
#> [2731] "021" "022" "032" "022" "032" "01" "01" "032" "01" "032" "01" "01" "021"
#> [2744] "022" "022" "01" "01" "01" "01" "032" "01" "01" "022" "022" "032" "032"
#> [2757] "01" "021" "021" "021" "021" "021" "021" "021" "023" "01" "01" "022" "021"
#> [2770] "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021" "023"
#> [2783] "021" "021" "021" "023" "021" "023" "021" "01" "01" "022" "023" "023" "021"
#> [2796] "021" "021" "021" "021" "021" "021" "021" "023" "021" "021" "021" "021" "023"
#> [2809] "021" "021" "021" "021" "021" "021" "021" "021" "022" "01" "01" "01" "022"
#> [2822] "021" "023" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021" "021"
#> [2835] "023" "022" "021" "021" "023" "023" "021" "021" "021" "021" "021" "021" "021"
#> [2848] "021" "021" "021" "021" "021" "032" "023" "01" "022" "021" "01" "022" "021"
#> [2861] "023" "023" "022" "022" "021" "021" "023" "022" "021" "022" "021" "021" "021"
#> [2874] "021" "021" "021" "022" "021" "01" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3324))
#> [1] "01" "01" "023" "032" "01" "01" "032" "01" "01" "01" "01" "01" "032" "01" "01"
#> [16] "01" "01" "01" "031" "01" "032" "01" "01" "032" "01" "032" "032" "032" "01" "031"
#> [31] "032" "01" "032" "032" "032" "01" "01" "01" "031" "031" "01" "022" "01" "031" "01"
#> [46] "01" "031" "031" "032" "01" "031" "01" "01" "01" "01" "021" "021" "01" "01" "031"
#> [61] "032" "01" "01" "022" "031" "01" "01" "01" "032" "01" "032" "031" "032" "01" "01"
#> [76] "01" "01" "01" "022" "01" "01" "032" "01" "01" "01" "022" "01" "01" "01" "01"
#> [91] "032" "01" "021" "01" "01" "01" "01" "01" "01" "031" "01" "01" "01" "01" "01"
#> [106] "032" "022" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022" "032" "01" "032"
#> [121] "031" "032" "022" "022" "022" "023" "01" "01" "01" "01" "01" "01" "01" "032" "01"
#> [136] "022" "01" "01" "032" "01" "01" "01" "01" "01" "01" "022" "01" "021" "01" "032"
#> [151] "031" "031" "01" "031" "032" "01" "031" "023" "032" "032" "032" "01" "031" "031" "022"
#> [166] "01" "032" "031" "01" "01" "032" "031" "031" "022" "022" "031" "031" "01" "031" "031"
#> [181] "031" "031" "032" "031" "032" "031" "031" "031" "031" "022" "032" "01" "031" "031" "031"
#> [196] "032" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "032" "031" "032" "022"
#> [211] "031" "023" "031" "031" "031" "031" "032" "022" "031" "01" "031" "031" "031" "031" "031"
#> [226] "031" "031" "031" "01" "021" "031" "031" "031" "031" "021" "031" "031" "031" "031" "031"
#> [241] "031" "031" "031" "01" "031" "031" "031" "022" "031" "031" "01" "031" "031" "031" "031"
#> [256] "031" "01" "031" "031" "031" "01" "031" "01" "01" "01" "031" "01" "01" "01" "031"
#> [271] "01" "01" "031" "031" "01" "031" "031" "031" "031" "032" "021" "01" "031" "031" "031"
#> [286] "022" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "021" "031"
#> [301] "021" "031" "031" "023" "031" "021" "022" "031" "022" "031" "031" "031" "022" "031" "031"
#> [316] "031" "01" "031" "031" "01" "031" "01" "031" "031" "031" "01" "031" "031" "031" "031"
#> [331] "031" "01" "01" "01" "01" "022" "022" "01" "031" "01" "031" "031" "022" "031" "031"
#> [346] "031" "022" "031" "031" "022" "031" "01" "031" "031" "031" "031" "031" "031" "031" "031"
#> [361] "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031"
#> [376] "031" "031" "031" "031" "031" "01" "01" "022" "031" "01" "01" "01" "031" "032" "031"
#> [391] "01" "031" "01" "031" "022" "031" "031" "031" "01" "01" "031" "01" "01" "031" "01"
#> [406] "022" "01" "031" "01" "01" "031" "031" "021" "01" "01" "01" "01" "01" "031" "01"
#> [421] "01" "01" "01" "031" "023" "01" "01" "01" "01" "01" "01" "023" "01" "023" "01"
#> [436] "022" "01" "021" "01" "023" "023" "031" "031" "031" "031" "01" "01" "031" "031" "031"
#> [451] "031" "01" "031" "01" "01" "031" "01" "01" "031" "031" "031" "031" "031" "031" "031"
#> [466] "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031"
#> [481] "031" "031" "031" "01" "021" "031" "031" "031" "022" "022" "021" "031" "031" "022" "031"
#> [496] "01" "021" "021" "01" "031" "021" "01" "031" "031" "031" "021" "01" "031" "031" "031"
#> [511] "031" "022" "031" "031" "01" "01" "022" "031" "031" "031" "01" "01" "01" "031" "031"
#> [526] "031" "031" "01" "01" "022" "021" "021" "01" "022" "01" "01" "01" "021" "01" "031"
#> [541] "01" "022" "01" "01" "022" "01" "01" "01" "031" "01" "01" "01" "01" "01" "01"
#> [556] "01" "01" "031" "031" "01" "031" "01" "032" "032" "01" "01" "01" "022" "031" "01"
#> [571] "01" "022" "032" "031" "01" "031" "01" "01" "01" "022" "01" "01" "01" "01" "031"
#> [586] "01" "031" "01" "01" "01" "01" "031" "01" "031" "031" "032" "01" "031" "031" "01"
#> [601] "01" "01" "01" "031" "01" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01"
#> [616] "023" "031" "031" "01" "031" "031" "031" "01" "031" "01" "031" "031" "031" "031" "031"
#> [631] "01" "01" "031" "01" "031" "021" "031" "01" "01" "01" "01" "031" "031" "01" "031"
#> [646] "031" "01" "01" "022" "01" "01" "01" "01" "01" "031" "01" "01" "01" "031" "031"
#> [661] "031" "01" "023" "01" "01" "031" "031" "031" "031" "031" "022" "01" "022" "031" "031"
#> [676] "023" "031" "031" "022" "01" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031"
#> [691] "031" "01" "031" "031" "022" "031" "031" "01" "031" "01" "031" "01" "01" "031" "031"
#> [706] "022" "022" "01" "032" "01" "032" "021" "01" "01" "01" "01" "01" "01" "022" "031"
#> [721] "031" "022" "023" "01" "022" "031" "031" "031" "01" "01" "031" "01" "01" "031" "01"
#> [736] "01" "01" "01" "01" "031" "01" "01" "01" "031" "031" "01" "031" "031" "031" "031"
#> [751] "031" "01" "031" "01" "01" "01" "031" "01" "032" "031" "021" "022" "01" "01" "031"
#> [766] "01" "01" "01" "01" "01" "031" "01" "01" "031" "01" "022" "01" "01" "021" "01"
#> [781] "031" "01" "01" "01" "01" "01" "031" "01" "031" "031" "01" "01" "031" "022" "01"
#> [796] "031" "01" "01" "023" "031" "031" "022" "031" "031" "031" "023" "031" "01" "031" "01"
#> [811] "01" "01" "032" "032" "01" "01" "01" "01" "01" "021" "01" "01" "01" "01" "01"
#> [826] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [841] "01" "01" "01" "01" "01" "01" "01" "01" "032" "01" "01" "023" "032" "022" "01"
#> [856] "01" "01" "01" "032" "01" "01" "032" "01" "01" "01" "01" "01" "01" "01" "031"
#> [871] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [886] "021" "01" "032" "01" "032" "01" "031" "01" "01" "01" "022" "022" "01" "01" "01"
#> [901] "01" "01" "031" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [916] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01"
#> [931] "01" "01" "01" "01" "022" "032" "01" "022" "01" "01" "01" "01" "01" "01" "01"
#> [946] "01" "01" "01" "01" "01" "01" "01" "01" "031" "01" "021" "01" "01" "01" "021"
#> [961] "022" "01" "01" "021" "031" "01" "01" "01" "01" "031" "01" "01" "022" "01" "01"
#> [976] "01" "01" "01" "01" "01" "032" "032" "022" "022" "031" "01" "01" "01" "01" "031"
#> [991] "031" "01" "01" "01" "031" "01" "01" "032" "032" "01" "022" "01" "01" "01" "01"
#> [1006] "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "022" "01" "01" "01" "01"
#> [1021] "01" "01" "01" "01" "01" "032" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1036] "031" "01" "031" "01" "021" "01" "01" "032" "01" "01" "01" "031" "01" "01" "01"
#> [1051] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1066] "01" "01" "01" "01" "01" "01" "01" "01" "01" "032" "01" "01" "01" "01" "01"
#> [1081] "01" "01" "031" "01" "01" "01" "01" "031" "01" "01" "01" "01" "01" "01" "01"
#> [1096] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "031"
#> [1111] "01" "01" "01" "01" "01" "01" "01" "031" "01" "01" "01" "031" "01" "01" "01"
#> [1126] "01" "01" "031" "01" "01" "01" "01" "01" "01" "032" "01" "01" "01" "01" "01"
#> [1141] "022" "01" "01" "01" "032" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1156] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1171] "01" "031" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "01" "01" "022"
#> [1186] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1201] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1216] "01" "032" "031" "01" "01" "01" "01" "01" "022" "01" "022" "01" "01" "01" "01"
#> [1231] "01" "01" "01" "01" "01" "01" "01" "031" "01" "01" "033" "032" "031" "01" "01"
#> [1246] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022" "022" "022" "01"
#> [1261] "01" "01" "01" "01" "031" "01" "01" "01" "01" "01" "01" "01" "033" "01" "01"
#> [1276] "01" "033" "021" "033" "033" "033" "01" "033" "01" "01" "01" "01" "01" "033" "033"
#> [1291] "01" "033" "01" "01" "023" "033" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1306] "01" "01" "01" "01" "01" "01" "01" "022" "033" "022" "01" "033" "021" "01" "01"
#> [1321] "01" "01" "01" "01" "01" "01" "033" "01" "01" "01" "01" "021" "01" "033" "01"
#> [1336] "033" "01" "01" "01" "01" "01" "01" "022" "01" "01" "022" "023" "021" "01" "01"
#> [1351] "01" "01" "021" "022" "01" "01" "01" "01" "01" "01" "021" "021" "01" "01" "033"
#> [1366] "033" "01" "033" "01" "033" "033" "033" "033" "01" "033" "01" "01" "01" "01" "01"
#> [1381] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "032" "01" "01" "023"
#> [1396] "01" "021" "023" "023" "01" "023" "01" "01" "01" "01" "033" "023" "01" "01" "031"
#> [1411] "01" "01" "01" "01" "01" "01" "01" "01" "033" "01" "01" "031" "01" "01" "01"
#> [1426] "01" "01" "01" "033" "031" "01" "01" "01" "031" "01" "01" "01" "01" "01" "01"
#> [1441] "01" "01" "01" "01" "01" "01" "01" "01" "023" "01" "033" "022" "01" "01" "022"
#> [1456] "01" "01" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1471] "01" "033" "01" "031" "033" "031" "01" "021" "01" "022" "01" "01" "01" "01" "01"
#> [1486] "01" "01" "032" "01" "01" "01" "01" "01" "01" "023" "01" "01" "01" "01" "023"
#> [1501] "01" "01" "01" "01" "01" "032" "01" "022" "01" "022" "01" "01" "01" "01" "01"
#> [1516] "01" "01" "022" "033" "022" "023" "023" "01" "022" "01" "01" "033" "01" "01" "01"
#> [1531] "01" "01" "01" "021" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1546] "01" "01" "01" "01" "01" "031" "01" "01" "01" "01" "01" "01" "021" "023" "01"
#> [1561] "01" "01" "01" "031" "022" "01" "01" "033" "01" "01" "01" "022" "021" "01" "021"
#> [1576] "033" "033" "021" "023" "023" "033" "021" "033" "023" "033" "033" "033" "033" "033" "033"
#> [1591] "01" "033" "033" "022" "033" "021" "021" "023" "01" "021" "01" "033" "01" "021" "021"
#> [1606] "01" "01" "023" "021" "021" "033" "033" "033" "033" "033" "033" "033" "021" "01" "022"
#> [1621] "021" "023" "01" "033" "01" "021" "01" "01" "01" "01" "01" "01" "032" "01" "021"
#> [1636] "01" "01" "01" "022" "01" "01" "01" "01" "023" "01" "01" "01" "01" "01" "01"
#> [1651] "01" "01" "01" "032" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1666] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "023" "01" "01" "01" "023"
#> [1681] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "032" "01" "01"
#> [1696] "01" "01" "01" "01" "01" "01" "01" "031" "01" "01" "01" "032" "01" "01" "01"
#> [1711] "01" "031" "01" "01" "01" "01" "01" "01" "032" "032" "01" "01" "01" "01" "01"
#> [1726] "01" "01" "031" "01" "031" "031" "01" "01" "021" "01" "01" "01" "01" "01" "01"
#> [1741] "01" "01" "01" "01" "01" "01" "01" "01" "032" "01" "031" "01" "022" "01" "022"
#> [1756] "01" "01" "01" "01" "031" "022" "01" "01" "01" "01" "022" "031" "01" "01" "022"
#> [1771] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "032" "01" "01" "01" "01"
#> [1786] "01" "031" "01" "01" "032" "01" "031" "031" "032" "022" "01" "01" "031" "022" "01"
#> [1801] "01" "01" "01" "032" "01" "01" "023" "021" "022" "031" "022" "01" "01" "01" "01"
#> [1816] "01" "01" "01" "01" "01" "031" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1831] "01" "01" "032" "01" "01" "021" "022" "01" "01" "01" "01" "01" "021" "01" "01"
#> [1846] "01" "01" "01" "01" "01" "01" "032" "01" "01" "01" "01" "01" "01" "032" "01"
#> [1861] "031" "01" "01" "033" "022" "031" "032" "032" "021" "01" "01" "032" "032" "01" "01"
#> [1876] "01" "023" "01" "01" "01" "021" "01" "01" "01" "01" "032" "01" "031" "01" "01"
#> [1891] "01" "022" "01" "01" "01" "031" "01" "01" "01" "031" "01" "032" "01" "031" "01"
#> [1906] "01" "033" "01" "01" "01" "01" "01" "01" "01" "032" "01" "01" "01" "01" "031"
#> [1921] "022" "01" "032" "032" "01" "031" "01" "01" "01" "023" "032" "01" "021" "01" "032"
#> [1936] "023" "033" "01" "01" "032" "032" "01" "032" "01" "033" "022" "031" "032" "01" "031"
#> [1951] "032" "01" "01" "021" "01" "01" "023" "032" "021" "032" "022" "032" "032" "022" "01"
#> [1966] "032" "021" "033" "032" "031" "01" "023" "031" "032" "021" "031" "01" "021" "032" "021"
#> [1981] "032" "032" "033" "023" "021" "023" "021" "021" "021" "021" "022" "021" "023" "023" "022"
#> [1996] "021" "023" "023" "021" "023" "022" "021" "021" "023" "021" "022" "022" "021" "023" "023"
#> [2011] "021" "023" "021" "023" "01" "022" "023" "032" "022" "021" "021" "022" "022" "022" "022"
#> [2026] "032" "022" "021" "021" "022" "022" "021" "022" "023" "022" "021" "022" "021" "022" "022"
#> [2041] "022" "032" "021" "023" "023" "021" "021" "021" "021" "01" "022" "021" "023" "021" "021"
#> [2056] "021" "022" "021" "032" "022" "021" "022" "022" "021" "022" "022" "021" "021" "021" "021"
#> [2071] "021" "022" "023" "022" "021" "023" "021" "022" "021" "021" "021" "021" "021" "023" "021"
#> [2086] "021" "021" "023" "021" "022" "021" "022" "022" "021" "022" "022" "022" "021" "022" "023"
#> [2101] "021" "021" "021" "021" "021" "021" "021" "021" "023" "021" "023" "022" "021" "021" "021"
#> [2116] "021" "022" "021" "023" "023" "021" "021" "023" "023" "01" "022" "021" "01" "021" "021"
#> [2131] "021" "021" "021" "021" "021" "021" "021" "021" "022" "021" "022" "022" "021" "022" "022"
#> [2146] "022" "022" "021" "022" "021" "021" "021" "022" "022" "021" "021" "021" "022" "022" "021"
#> [2161] "022" "022" "022" "021" "022" "022" "022" "021" "022" "022" "022" "022" "021" "032" "022"
#> [2176] "022" "022" "021" "021" "021" "021" "022" "023" "022" "022" "022" "022" "022" "022" "01"
#> [2191] "022" "01" "022" "031" "022" "032" "022" "021" "021" "022" "023" "023" "022" "023" "023"
#> [2206] "022" "021" "021" "021" "023" "023" "022" "023" "022" "021" "021" "021" "021" "021" "021"
#> [2221] "022" "021" "021" "021" "022" "021" "021" "021" "022" "021" "022" "022" "022" "022" "021"
#> [2236] "022" "032" "022" "022" "032" "022" "032" "022" "022" "022" "022" "022" "023" "023" "022"
#> [2251] "022" "032" "022" "022" "023" "023" "022" "022" "01" "032" "021" "022" "022" "022" "022"
#> [2266] "032" "023" "022" "032" "022" "022" "022" "01" "022" "01" "022" "022" "032" "022" "023"
#> [2281] "022" "022" "01" "022" "022" "022" "01" "032" "032" "022" "022" "032" "022" "022" "01"
#> [2296] "022" "022" "022" "01" "032" "01" "01" "01" "022" "01" "022" "01" "01" "01" "022"
#> [2311] "01" "032" "022" "01" "01" "01" "01" "021" "023" "022" "01" "022" "022" "032" "022"
#> [2326] "022" "022" "022" "022" "022" "022" "022" "022" "022" "032" "01" "01" "032" "022" "01"
#> [2341] "032" "032" "022" "021" "023" "022" "022" "022" "022" "022" "032" "022" "022" "022" "022"
#> [2356] "022" "032" "021" "021" "022" "021" "022" "021" "023" "021" "021" "022" "021" "021" "022"
#> [2371] "022" "022" "021" "022" "021" "022" "022" "022" "021" "021" "023" "022" "021" "021" "023"
#> [2386] "022" "022" "022" "022" "023" "021" "022" "022" "022" "021" "021" "021" "022" "032" "022"
#> [2401] "01" "01" "01" "021" "022" "023" "021" "022" "021" "022" "021" "022" "021" "01" "022"
#> [2416] "022" "022" "022" "023" "01" "022" "022" "032" "021" "021" "023" "022" "022" "022" "022"
#> [2431] "022" "022" "01" "032" "01" "022" "032" "032" "032" "01" "01" "01" "032" "032" "032"
#> [2446] "022" "022" "022" "021" "022" "021" "021" "021" "021" "021" "021" "021" "021" "021" "023"
#> [2461] "021" "021" "021" "021" "021" "021" "021" "021" "022" "022" "032" "022" "022" "022" "021"
#> [2476] "021" "022" "021" "022" "022" "022" "021" "022" "022" "021" "022" "021" "021" "021" "021"
#> [2491] "021" "021" "021" "021" "022" "021" "022" "021" "021" "022" "021" "022" "021" "022" "022"
#> [2506] "022" "022" "032" "032" "022" "032" "021" "021" "021" "021" "021" "021" "021" "022" "021"
#> [2521] "021" "021" "022" "022" "022" "021" "021" "022" "021" "021" "022" "021" "021" "01" "021"
#> [2536] "032" "022" "022" "022" "023" "022" "022" "022" "022" "023" "022" "022" "022" "01" "022"
#> [2551] "032" "032" "01" "01" "021" "032" "022" "01" "021" "021" "032" "021" "032" "022" "022"
#> [2566] "032" "022" "022" "022" "022" "022" "01" "01" "01" "032" "032" "022" "022" "021" "021"
#> [2581] "021" "022" "022" "022" "022" "022" "022" "01" "021" "023" "023" "023" "021" "021" "021"
#> [2596] "021" "021" "021" "023" "021" "021" "021" "023" "021" "023" "023" "023" "023" "032" "01"
#> [2611] "023" "021" "023" "022" "022" "032" "022" "023" "023" "023" "023" "023" "01" "021" "022"
#> [2626] "023" "01" "01" "01" "023" "021" "021" "021" "021" "021" "022" "021" "022" "022" "022"
#> [2641] "022" "032" "022" "022" "021" "022" "022" "022" "032" "022" "01" "022" "022" "022" "022"
#> [2656] "022" "023" "022" "022" "022" "022" "022" "022" "032" "032" "022" "032" "022" "022" "032"
#> [2671] "022" "01" "032" "022" "032" "022" "022" "032" "01" "01" "01" "022" "01" "01" "01"
#> [2686] "023" "023" "021" "021" "032" "021" "021" "023" "023" "01" "022" "032" "021" "022" "032"
#> [2701] "022" "021" "01" "022" "022" "032" "01" "022" "01" "01" "022" "01" "01" "023" "022"
#> [2716] "023" "01" "01" "023" "01" "022" "022" "01" "021" "032" "01" "032" "01" "01" "032"
#> [2731] "021" "022" "032" "022" "032" "01" "01" "032" "01" "032" "01" "01" "021" "022" "022"
#> [2746] "01" "01" "01" "01" "032" "01" "01" "022" "022" "032" "032" "01" "021" "021" "021"
#> [2761] "021" "021" "021" "021" "023" "01" "01" "022" "021" "021" "021" "021" "021" "021" "021"
#> [2776] "021" "021" "021" "021" "021" "021" "023" "021" "021" "021" "023" "021" "023" "021" "01"
#> [2791] "01" "022" "023" "023" "021" "021" "021" "021" "021" "021" "021" "021" "023" "021" "021"
#> [2806] "021" "021" "023" "021" "021" "021" "021" "021" "021" "021" "021" "022" "01" "01" "01"
#> [2821] "022" "021" "023" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021" "021" "023"
#> [2836] "022" "021" "021" "023" "023" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021"
#> [2851] "021" "021" "032" "023" "01" "022" "021" "01" "022" "021" "023" "023" "022" "022" "021"
#> [2866] "021" "023" "022" "021" "022" "021" "021" "021" "021" "021" "021" "022" "021" "01" "021"
#> [2881] "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 4249))
#> [1] "01" "01" "023" "032" "01" "01" "032" "01" "01" "01" "01" "01" "032" "01" "01"
#> [16] "01" "01" "01" "031" "01" "032" "01" "01" "032" "01" "032" "032" "032" "01" "031"
#> [31] "032" "01" "032" "032" "032" "01" "01" "01" "031" "031" "01" "022" "01" "031" "01"
#> [46] "01" "031" "031" "032" "01" "031" "01" "01" "01" "01" "021" "021" "01" "01" "031"
#> [61] "032" "01" "01" "022" "031" "01" "01" "01" "032" "01" "032" "031" "032" "01" "01"
#> [76] "01" "01" "01" "022" "01" "01" "032" "01" "01" "01" "022" "01" "01" "01" "01"
#> [91] "032" "01" "021" "01" "01" "01" "01" "01" "01" "031" "01" "01" "01" "01" "01"
#> [106] "032" "022" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022" "032" "01" "032"
#> [121] "031" "032" "022" "022" "022" "023" "01" "01" "01" "01" "01" "01" "01" "032" "01"
#> [136] "022" "01" "01" "032" "01" "01" "01" "01" "01" "01" "022" "01" "021" "01" "032"
#> [151] "031" "031" "01" "031" "032" "01" "031" "023" "032" "032" "032" "01" "031" "031" "022"
#> [166] "01" "032" "031" "01" "01" "032" "031" "031" "022" "022" "031" "031" "01" "031" "031"
#> [181] "031" "031" "032" "031" "032" "031" "031" "031" "031" "022" "032" "01" "031" "031" "031"
#> [196] "032" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "032" "031" "032" "022"
#> [211] "031" "023" "031" "031" "031" "031" "032" "022" "031" "01" "031" "031" "031" "031" "031"
#> [226] "031" "031" "031" "01" "021" "031" "031" "031" "031" "021" "031" "031" "031" "031" "031"
#> [241] "031" "031" "031" "01" "031" "031" "031" "022" "031" "031" "01" "031" "031" "031" "031"
#> [256] "031" "01" "031" "031" "031" "01" "031" "01" "01" "01" "031" "01" "01" "01" "031"
#> [271] "01" "01" "031" "031" "01" "031" "031" "031" "031" "032" "021" "01" "031" "031" "031"
#> [286] "022" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "021" "031"
#> [301] "021" "031" "031" "023" "031" "021" "022" "031" "022" "031" "031" "031" "022" "031" "031"
#> [316] "031" "01" "031" "031" "01" "031" "01" "031" "031" "031" "01" "031" "031" "031" "031"
#> [331] "031" "01" "01" "01" "01" "022" "022" "01" "031" "01" "031" "031" "022" "031" "031"
#> [346] "031" "022" "031" "031" "022" "031" "01" "031" "031" "031" "031" "031" "031" "031" "031"
#> [361] "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031"
#> [376] "031" "031" "031" "031" "031" "01" "01" "022" "031" "01" "01" "01" "031" "032" "031"
#> [391] "01" "031" "01" "031" "022" "031" "031" "031" "01" "01" "031" "01" "01" "031" "01"
#> [406] "022" "01" "031" "01" "01" "031" "031" "021" "01" "01" "01" "01" "01" "031" "01"
#> [421] "01" "01" "01" "031" "023" "01" "01" "01" "01" "01" "01" "023" "01" "023" "01"
#> [436] "022" "01" "021" "01" "023" "023" "031" "031" "031" "031" "01" "01" "031" "031" "031"
#> [451] "031" "01" "031" "01" "01" "031" "01" "01" "031" "031" "031" "031" "031" "031" "031"
#> [466] "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031" "031"
#> [481] "031" "031" "031" "01" "021" "031" "031" "031" "022" "022" "021" "031" "031" "022" "031"
#> [496] "01" "021" "021" "01" "031" "021" "01" "031" "031" "031" "021" "01" "031" "031" "031"
#> [511] "031" "022" "031" "031" "01" "01" "022" "031" "031" "031" "01" "01" "01" "031" "031"
#> [526] "031" "031" "01" "01" "022" "021" "021" "01" "022" "01" "01" "01" "021" "01" "031"
#> [541] "01" "022" "01" "01" "022" "01" "01" "01" "031" "01" "01" "01" "01" "01" "01"
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#> [2821] "022" "021" "023" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021" "021" "023"
#> [2836] "022" "021" "021" "023" "023" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021"
#> [2851] "021" "021" "032" "023" "01" "022" "021" "01" "022" "021" "023" "023" "022" "022" "021"
#> [2866] "021" "023" "022" "021" "022" "021" "021" "021" "021" "021" "021" "022" "021" "01" "021"
#> [2881] "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 5466))
#> [1] "01" "01" "023" "03" "01" "01" "03" "01" "01" "01" "01" "01" "03" "01" "01"
#> [16] "01" "01" "01" "03" "01" "03" "01" "01" "03" "01" "03" "03" "03" "01" "03"
#> [31] "03" "01" "03" "03" "03" "01" "01" "01" "03" "03" "01" "022" "01" "03" "01"
#> [46] "01" "03" "03" "03" "01" "03" "01" "01" "01" "01" "021" "021" "01" "01" "03"
#> [61] "03" "01" "01" "022" "03" "01" "01" "01" "03" "01" "03" "03" "03" "01" "01"
#> [76] "01" "01" "01" "022" "01" "01" "03" "01" "01" "01" "022" "01" "01" "01" "01"
#> [91] "03" "01" "021" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01"
#> [106] "03" "022" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022" "03" "01" "03"
#> [121] "03" "03" "022" "022" "022" "023" "01" "01" "01" "01" "01" "01" "01" "03" "01"
#> [136] "022" "01" "01" "03" "01" "01" "01" "01" "01" "01" "022" "01" "021" "01" "03"
#> [151] "03" "03" "01" "03" "03" "01" "03" "023" "03" "03" "03" "01" "03" "03" "022"
#> [166] "01" "03" "03" "01" "01" "03" "03" "03" "022" "022" "03" "03" "01" "03" "03"
#> [181] "03" "03" "03" "03" "03" "03" "03" "03" "03" "022" "03" "01" "03" "03" "03"
#> [196] "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "022"
#> [211] "03" "023" "03" "03" "03" "03" "03" "022" "03" "01" "03" "03" "03" "03" "03"
#> [226] "03" "03" "03" "01" "021" "03" "03" "03" "03" "021" "03" "03" "03" "03" "03"
#> [241] "03" "03" "03" "01" "03" "03" "03" "022" "03" "03" "01" "03" "03" "03" "03"
#> [256] "03" "01" "03" "03" "03" "01" "03" "01" "01" "01" "03" "01" "01" "01" "03"
#> [271] "01" "01" "03" "03" "01" "03" "03" "03" "03" "03" "021" "01" "03" "03" "03"
#> [286] "022" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "021" "03"
#> [301] "021" "03" "03" "023" "03" "021" "022" "03" "022" "03" "03" "03" "022" "03" "03"
#> [316] "03" "01" "03" "03" "01" "03" "01" "03" "03" "03" "01" "03" "03" "03" "03"
#> [331] "03" "01" "01" "01" "01" "022" "022" "01" "03" "01" "03" "03" "022" "03" "03"
#> [346] "03" "022" "03" "03" "022" "03" "01" "03" "03" "03" "03" "03" "03" "03" "03"
#> [361] "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03"
#> [376] "03" "03" "03" "03" "03" "01" "01" "022" "03" "01" "01" "01" "03" "03" "03"
#> [391] "01" "03" "01" "03" "022" "03" "03" "03" "01" "01" "03" "01" "01" "03" "01"
#> [406] "022" "01" "03" "01" "01" "03" "03" "021" "01" "01" "01" "01" "01" "03" "01"
#> [421] "01" "01" "01" "03" "023" "01" "01" "01" "01" "01" "01" "023" "01" "023" "01"
#> [436] "022" "01" "021" "01" "023" "023" "03" "03" "03" "03" "01" "01" "03" "03" "03"
#> [451] "03" "01" "03" "01" "01" "03" "01" "01" "03" "03" "03" "03" "03" "03" "03"
#> [466] "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03"
#> [481] "03" "03" "03" "01" "021" "03" "03" "03" "022" "022" "021" "03" "03" "022" "03"
#> [496] "01" "021" "021" "01" "03" "021" "01" "03" "03" "03" "021" "01" "03" "03" "03"
#> [511] "03" "022" "03" "03" "01" "01" "022" "03" "03" "03" "01" "01" "01" "03" "03"
#> [526] "03" "03" "01" "01" "022" "021" "021" "01" "022" "01" "01" "01" "021" "01" "03"
#> [541] "01" "022" "01" "01" "022" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01"
#> [556] "01" "01" "03" "03" "01" "03" "01" "03" "03" "01" "01" "01" "022" "03" "01"
#> [571] "01" "022" "03" "03" "01" "03" "01" "01" "01" "022" "01" "01" "01" "01" "03"
#> [586] "01" "03" "01" "01" "01" "01" "03" "01" "03" "03" "03" "01" "03" "03" "01"
#> [601] "01" "01" "01" "03" "01" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01"
#> [616] "023" "03" "03" "01" "03" "03" "03" "01" "03" "01" "03" "03" "03" "03" "03"
#> [631] "01" "01" "03" "01" "03" "021" "03" "01" "01" "01" "01" "03" "03" "01" "03"
#> [646] "03" "01" "01" "022" "01" "01" "01" "01" "01" "03" "01" "01" "01" "03" "03"
#> [661] "03" "01" "023" "01" "01" "03" "03" "03" "03" "03" "022" "01" "022" "03" "03"
#> [676] "023" "03" "03" "022" "01" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03"
#> [691] "03" "01" "03" "03" "022" "03" "03" "01" "03" "01" "03" "01" "01" "03" "03"
#> [706] "022" "022" "01" "03" "01" "03" "021" "01" "01" "01" "01" "01" "01" "022" "03"
#> [721] "03" "022" "023" "01" "022" "03" "03" "03" "01" "01" "03" "01" "01" "03" "01"
#> [736] "01" "01" "01" "01" "03" "01" "01" "01" "03" "03" "01" "03" "03" "03" "03"
#> [751] "03" "01" "03" "01" "01" "01" "03" "01" "03" "03" "021" "022" "01" "01" "03"
#> [766] "01" "01" "01" "01" "01" "03" "01" "01" "03" "01" "022" "01" "01" "021" "01"
#> [781] "03" "01" "01" "01" "01" "01" "03" "01" "03" "03" "01" "01" "03" "022" "01"
#> [796] "03" "01" "01" "023" "03" "03" "022" "03" "03" "03" "023" "03" "01" "03" "01"
#> [811] "01" "01" "03" "03" "01" "01" "01" "01" "01" "021" "01" "01" "01" "01" "01"
#> [826] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [841] "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "023" "03" "022" "01"
#> [856] "01" "01" "01" "03" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "03"
#> [871] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [886] "021" "01" "03" "01" "03" "01" "03" "01" "01" "01" "022" "022" "01" "01" "01"
#> [901] "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [916] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01"
#> [931] "01" "01" "01" "01" "022" "03" "01" "022" "01" "01" "01" "01" "01" "01" "01"
#> [946] "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "021" "01" "01" "01" "021"
#> [961] "022" "01" "01" "021" "03" "01" "01" "01" "01" "03" "01" "01" "022" "01" "01"
#> [976] "01" "01" "01" "01" "01" "03" "03" "022" "022" "03" "01" "01" "01" "01" "03"
#> [991] "03" "01" "01" "01" "03" "01" "01" "03" "03" "01" "022" "01" "01" "01" "01"
#> [1006] "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "022" "01" "01" "01" "01"
#> [1021] "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1036] "03" "01" "03" "01" "021" "01" "01" "03" "01" "01" "01" "03" "01" "01" "01"
#> [1051] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1066] "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01"
#> [1081] "01" "01" "03" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01"
#> [1096] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03"
#> [1111] "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "03" "01" "01" "01"
#> [1126] "01" "01" "03" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01"
#> [1141] "022" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1156] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1171] "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "022" "01" "01" "01" "022"
#> [1186] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1201] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1216] "01" "03" "03" "01" "01" "01" "01" "01" "022" "01" "022" "01" "01" "01" "01"
#> [1231] "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "03" "03" "03" "01" "01"
#> [1246] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "022" "022" "022" "022" "01"
#> [1261] "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01"
#> [1276] "01" "03" "021" "03" "03" "03" "01" "03" "01" "01" "01" "01" "01" "03" "03"
#> [1291] "01" "03" "01" "01" "023" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1306] "01" "01" "01" "01" "01" "01" "01" "022" "03" "022" "01" "03" "021" "01" "01"
#> [1321] "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "021" "01" "03" "01"
#> [1336] "03" "01" "01" "01" "01" "01" "01" "022" "01" "01" "022" "023" "021" "01" "01"
#> [1351] "01" "01" "021" "022" "01" "01" "01" "01" "01" "01" "021" "021" "01" "01" "03"
#> [1366] "03" "01" "03" "01" "03" "03" "03" "03" "01" "03" "01" "01" "01" "01" "01"
#> [1381] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "023"
#> [1396] "01" "021" "023" "023" "01" "023" "01" "01" "01" "01" "03" "023" "01" "01" "03"
#> [1411] "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "03" "01" "01" "01"
#> [1426] "01" "01" "01" "03" "03" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01"
#> [1441] "01" "01" "01" "01" "01" "01" "01" "01" "023" "01" "03" "022" "01" "01" "022"
#> [1456] "01" "01" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1471] "01" "03" "01" "03" "03" "03" "01" "021" "01" "022" "01" "01" "01" "01" "01"
#> [1486] "01" "01" "03" "01" "01" "01" "01" "01" "01" "023" "01" "01" "01" "01" "023"
#> [1501] "01" "01" "01" "01" "01" "03" "01" "022" "01" "022" "01" "01" "01" "01" "01"
#> [1516] "01" "01" "022" "03" "022" "023" "023" "01" "022" "01" "01" "03" "01" "01" "01"
#> [1531] "01" "01" "01" "021" "01" "022" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1546] "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "021" "023" "01"
#> [1561] "01" "01" "01" "03" "022" "01" "01" "03" "01" "01" "01" "022" "021" "01" "021"
#> [1576] "03" "03" "021" "023" "023" "03" "021" "03" "023" "03" "03" "03" "03" "03" "03"
#> [1591] "01" "03" "03" "022" "03" "021" "021" "023" "01" "021" "01" "03" "01" "021" "021"
#> [1606] "01" "01" "023" "021" "021" "03" "03" "03" "03" "03" "03" "03" "021" "01" "022"
#> [1621] "021" "023" "01" "03" "01" "021" "01" "01" "01" "01" "01" "01" "03" "01" "021"
#> [1636] "01" "01" "01" "022" "01" "01" "01" "01" "023" "01" "01" "01" "01" "01" "01"
#> [1651] "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1666] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "023" "01" "01" "01" "023"
#> [1681] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01"
#> [1696] "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "03" "01" "01" "01"
#> [1711] "01" "03" "01" "01" "01" "01" "01" "01" "03" "03" "01" "01" "01" "01" "01"
#> [1726] "01" "01" "03" "01" "03" "03" "01" "01" "021" "01" "01" "01" "01" "01" "01"
#> [1741] "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "03" "01" "022" "01" "022"
#> [1756] "01" "01" "01" "01" "03" "022" "01" "01" "01" "01" "022" "03" "01" "01" "022"
#> [1771] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01"
#> [1786] "01" "03" "01" "01" "03" "01" "03" "03" "03" "022" "01" "01" "03" "022" "01"
#> [1801] "01" "01" "01" "03" "01" "01" "023" "021" "022" "03" "022" "01" "01" "01" "01"
#> [1816] "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "022"
#> [1831] "01" "01" "03" "01" "01" "021" "022" "01" "01" "01" "01" "01" "021" "01" "01"
#> [1846] "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "03" "01"
#> [1861] "03" "01" "01" "03" "022" "03" "03" "03" "021" "01" "01" "03" "03" "01" "01"
#> [1876] "01" "023" "01" "01" "01" "021" "01" "01" "01" "01" "03" "01" "03" "01" "01"
#> [1891] "01" "022" "01" "01" "01" "03" "01" "01" "01" "03" "01" "03" "01" "03" "01"
#> [1906] "01" "03" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "03"
#> [1921] "022" "01" "03" "03" "01" "03" "01" "01" "01" "023" "03" "01" "021" "01" "03"
#> [1936] "023" "03" "01" "01" "03" "03" "01" "03" "01" "03" "022" "03" "03" "01" "03"
#> [1951] "03" "01" "01" "021" "01" "01" "023" "03" "021" "03" "022" "03" "03" "022" "01"
#> [1966] "03" "021" "03" "03" "03" "01" "023" "03" "03" "021" "03" "01" "021" "03" "021"
#> [1981] "03" "03" "03" "023" "021" "023" "021" "021" "021" "021" "022" "021" "023" "023" "022"
#> [1996] "021" "023" "023" "021" "023" "022" "021" "021" "023" "021" "022" "022" "021" "023" "023"
#> [2011] "021" "023" "021" "023" "01" "022" "023" "03" "022" "021" "021" "022" "022" "022" "022"
#> [2026] "03" "022" "021" "021" "022" "022" "021" "022" "023" "022" "021" "022" "021" "022" "022"
#> [2041] "022" "03" "021" "023" "023" "021" "021" "021" "021" "01" "022" "021" "023" "021" "021"
#> [2056] "021" "022" "021" "03" "022" "021" "022" "022" "021" "022" "022" "021" "021" "021" "021"
#> [2071] "021" "022" "023" "022" "021" "023" "021" "022" "021" "021" "021" "021" "021" "023" "021"
#> [2086] "021" "021" "023" "021" "022" "021" "022" "022" "021" "022" "022" "022" "021" "022" "023"
#> [2101] "021" "021" "021" "021" "021" "021" "021" "021" "023" "021" "023" "022" "021" "021" "021"
#> [2116] "021" "022" "021" "023" "023" "021" "021" "023" "023" "01" "022" "021" "01" "021" "021"
#> [2131] "021" "021" "021" "021" "021" "021" "021" "021" "022" "021" "022" "022" "021" "022" "022"
#> [2146] "022" "022" "021" "022" "021" "021" "021" "022" "022" "021" "021" "021" "022" "022" "021"
#> [2161] "022" "022" "022" "021" "022" "022" "022" "021" "022" "022" "022" "022" "021" "03" "022"
#> [2176] "022" "022" "021" "021" "021" "021" "022" "023" "022" "022" "022" "022" "022" "022" "01"
#> [2191] "022" "01" "022" "03" "022" "03" "022" "021" "021" "022" "023" "023" "022" "023" "023"
#> [2206] "022" "021" "021" "021" "023" "023" "022" "023" "022" "021" "021" "021" "021" "021" "021"
#> [2221] "022" "021" "021" "021" "022" "021" "021" "021" "022" "021" "022" "022" "022" "022" "021"
#> [2236] "022" "03" "022" "022" "03" "022" "03" "022" "022" "022" "022" "022" "023" "023" "022"
#> [2251] "022" "03" "022" "022" "023" "023" "022" "022" "01" "03" "021" "022" "022" "022" "022"
#> [2266] "03" "023" "022" "03" "022" "022" "022" "01" "022" "01" "022" "022" "03" "022" "023"
#> [2281] "022" "022" "01" "022" "022" "022" "01" "03" "03" "022" "022" "03" "022" "022" "01"
#> [2296] "022" "022" "022" "01" "03" "01" "01" "01" "022" "01" "022" "01" "01" "01" "022"
#> [2311] "01" "03" "022" "01" "01" "01" "01" "021" "023" "022" "01" "022" "022" "03" "022"
#> [2326] "022" "022" "022" "022" "022" "022" "022" "022" "022" "03" "01" "01" "03" "022" "01"
#> [2341] "03" "03" "022" "021" "023" "022" "022" "022" "022" "022" "03" "022" "022" "022" "022"
#> [2356] "022" "03" "021" "021" "022" "021" "022" "021" "023" "021" "021" "022" "021" "021" "022"
#> [2371] "022" "022" "021" "022" "021" "022" "022" "022" "021" "021" "023" "022" "021" "021" "023"
#> [2386] "022" "022" "022" "022" "023" "021" "022" "022" "022" "021" "021" "021" "022" "03" "022"
#> [2401] "01" "01" "01" "021" "022" "023" "021" "022" "021" "022" "021" "022" "021" "01" "022"
#> [2416] "022" "022" "022" "023" "01" "022" "022" "03" "021" "021" "023" "022" "022" "022" "022"
#> [2431] "022" "022" "01" "03" "01" "022" "03" "03" "03" "01" "01" "01" "03" "03" "03"
#> [2446] "022" "022" "022" "021" "022" "021" "021" "021" "021" "021" "021" "021" "021" "021" "023"
#> [2461] "021" "021" "021" "021" "021" "021" "021" "021" "022" "022" "03" "022" "022" "022" "021"
#> [2476] "021" "022" "021" "022" "022" "022" "021" "022" "022" "021" "022" "021" "021" "021" "021"
#> [2491] "021" "021" "021" "021" "022" "021" "022" "021" "021" "022" "021" "022" "021" "022" "022"
#> [2506] "022" "022" "03" "03" "022" "03" "021" "021" "021" "021" "021" "021" "021" "022" "021"
#> [2521] "021" "021" "022" "022" "022" "021" "021" "022" "021" "021" "022" "021" "021" "01" "021"
#> [2536] "03" "022" "022" "022" "023" "022" "022" "022" "022" "023" "022" "022" "022" "01" "022"
#> [2551] "03" "03" "01" "01" "021" "03" "022" "01" "021" "021" "03" "021" "03" "022" "022"
#> [2566] "03" "022" "022" "022" "022" "022" "01" "01" "01" "03" "03" "022" "022" "021" "021"
#> [2581] "021" "022" "022" "022" "022" "022" "022" "01" "021" "023" "023" "023" "021" "021" "021"
#> [2596] "021" "021" "021" "023" "021" "021" "021" "023" "021" "023" "023" "023" "023" "03" "01"
#> [2611] "023" "021" "023" "022" "022" "03" "022" "023" "023" "023" "023" "023" "01" "021" "022"
#> [2626] "023" "01" "01" "01" "023" "021" "021" "021" "021" "021" "022" "021" "022" "022" "022"
#> [2641] "022" "03" "022" "022" "021" "022" "022" "022" "03" "022" "01" "022" "022" "022" "022"
#> [2656] "022" "023" "022" "022" "022" "022" "022" "022" "03" "03" "022" "03" "022" "022" "03"
#> [2671] "022" "01" "03" "022" "03" "022" "022" "03" "01" "01" "01" "022" "01" "01" "01"
#> [2686] "023" "023" "021" "021" "03" "021" "021" "023" "023" "01" "022" "03" "021" "022" "03"
#> [2701] "022" "021" "01" "022" "022" "03" "01" "022" "01" "01" "022" "01" "01" "023" "022"
#> [2716] "023" "01" "01" "023" "01" "022" "022" "01" "021" "03" "01" "03" "01" "01" "03"
#> [2731] "021" "022" "03" "022" "03" "01" "01" "03" "01" "03" "01" "01" "021" "022" "022"
#> [2746] "01" "01" "01" "01" "03" "01" "01" "022" "022" "03" "03" "01" "021" "021" "021"
#> [2761] "021" "021" "021" "021" "023" "01" "01" "022" "021" "021" "021" "021" "021" "021" "021"
#> [2776] "021" "021" "021" "021" "021" "021" "023" "021" "021" "021" "023" "021" "023" "021" "01"
#> [2791] "01" "022" "023" "023" "021" "021" "021" "021" "021" "021" "021" "021" "023" "021" "021"
#> [2806] "021" "021" "023" "021" "021" "021" "021" "021" "021" "021" "021" "022" "01" "01" "01"
#> [2821] "022" "021" "023" "021" "021" "021" "021" "021" "021" "022" "021" "021" "021" "021" "023"
#> [2836] "022" "021" "021" "023" "023" "021" "021" "021" "021" "021" "021" "021" "021" "021" "021"
#> [2851] "021" "021" "03" "023" "01" "022" "021" "01" "022" "021" "023" "023" "022" "022" "021"
#> [2866] "021" "023" "022" "021" "022" "021" "021" "021" "021" "021" "021" "022" "021" "01" "021"
#> [2881] "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 6708))
#> [1] "01" "01" "02" "03" "01" "01" "03" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01"
#> [19] "03" "01" "03" "01" "01" "03" "01" "03" "03" "03" "01" "03" "03" "01" "03" "03" "03" "01"
#> [37] "01" "01" "03" "03" "01" "02" "01" "03" "01" "01" "03" "03" "03" "01" "03" "01" "01" "01"
#> [55] "01" "02" "02" "01" "01" "03" "03" "01" "01" "02" "03" "01" "01" "01" "03" "01" "03" "03"
#> [73] "03" "01" "01" "01" "01" "01" "02" "01" "01" "03" "01" "01" "01" "02" "01" "01" "01" "01"
#> [91] "03" "01" "02" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "03" "02" "01"
#> [109] "01" "01" "01" "01" "01" "01" "01" "02" "02" "03" "01" "03" "03" "03" "02" "02" "02" "02"
#> [127] "01" "01" "01" "01" "01" "01" "01" "03" "01" "02" "01" "01" "03" "01" "01" "01" "01" "01"
#> [145] "01" "02" "01" "02" "01" "03" "03" "03" "01" "03" "03" "01" "03" "02" "03" "03" "03" "01"
#> [163] "03" "03" "02" "01" "03" "03" "01" "01" "03" "03" "03" "02" "02" "03" "03" "01" "03" "03"
#> [181] "03" "03" "03" "03" "03" "03" "03" "03" "03" "02" "03" "01" "03" "03" "03" "03" "03" "03"
#> [199] "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "02" "03" "02" "03" "03" "03" "03"
#> [217] "03" "02" "03" "01" "03" "03" "03" "03" "03" "03" "03" "03" "01" "02" "03" "03" "03" "03"
#> [235] "02" "03" "03" "03" "03" "03" "03" "03" "03" "01" "03" "03" "03" "02" "03" "03" "01" "03"
#> [253] "03" "03" "03" "03" "01" "03" "03" "03" "01" "03" "01" "01" "01" "03" "01" "01" "01" "03"
#> [271] "01" "01" "03" "03" "01" "03" "03" "03" "03" "03" "02" "01" "03" "03" "03" "02" "03" "03"
#> [289] "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "02" "03" "02" "03" "03" "02" "03" "02"
#> [307] "02" "03" "02" "03" "03" "03" "02" "03" "03" "03" "01" "03" "03" "01" "03" "01" "03" "03"
#> [325] "03" "01" "03" "03" "03" "03" "03" "01" "01" "01" "01" "02" "02" "01" "03" "01" "03" "03"
#> [343] "02" "03" "03" "03" "02" "03" "03" "02" "03" "01" "03" "03" "03" "03" "03" "03" "03" "03"
#> [361] "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03"
#> [379] "03" "03" "01" "01" "02" "03" "01" "01" "01" "03" "03" "03" "01" "03" "01" "03" "02" "03"
#> [397] "03" "03" "01" "01" "03" "01" "01" "03" "01" "02" "01" "03" "01" "01" "03" "03" "02" "01"
#> [415] "01" "01" "01" "01" "03" "01" "01" "01" "01" "03" "02" "01" "01" "01" "01" "01" "01" "02"
#> [433] "01" "02" "01" "02" "01" "02" "01" "02" "02" "03" "03" "03" "03" "01" "01" "03" "03" "03"
#> [451] "03" "01" "03" "01" "01" "03" "01" "01" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03"
#> [469] "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "03" "01" "02" "03"
#> [487] "03" "03" "02" "02" "02" "03" "03" "02" "03" "01" "02" "02" "01" "03" "02" "01" "03" "03"
#> [505] "03" "02" "01" "03" "03" "03" "03" "02" "03" "03" "01" "01" "02" "03" "03" "03" "01" "01"
#> [523] "01" "03" "03" "03" "03" "01" "01" "02" "02" "02" "01" "02" "01" "01" "01" "02" "01" "03"
#> [541] "01" "02" "01" "01" "02" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "03"
#> [559] "03" "01" "03" "01" "03" "03" "01" "01" "01" "02" "03" "01" "01" "02" "03" "03" "01" "03"
#> [577] "01" "01" "01" "02" "01" "01" "01" "01" "03" "01" "03" "01" "01" "01" "01" "03" "01" "03"
#> [595] "03" "03" "01" "03" "03" "01" "01" "01" "01" "03" "01" "01" "02" "01" "01" "01" "01" "01"
#> [613] "01" "01" "01" "02" "03" "03" "01" "03" "03" "03" "01" "03" "01" "03" "03" "03" "03" "03"
#> [631] "01" "01" "03" "01" "03" "02" "03" "01" "01" "01" "01" "03" "03" "01" "03" "03" "01" "01"
#> [649] "02" "01" "01" "01" "01" "01" "03" "01" "01" "01" "03" "03" "03" "01" "02" "01" "01" "03"
#> [667] "03" "03" "03" "03" "02" "01" "02" "03" "03" "02" "03" "03" "02" "01" "03" "03" "03" "03"
#> [685] "03" "03" "03" "03" "03" "03" "03" "01" "03" "03" "02" "03" "03" "01" "03" "01" "03" "01"
#> [703] "01" "03" "03" "02" "02" "01" "03" "01" "03" "02" "01" "01" "01" "01" "01" "01" "02" "03"
#> [721] "03" "02" "02" "01" "02" "03" "03" "03" "01" "01" "03" "01" "01" "03" "01" "01" "01" "01"
#> [739] "01" "03" "01" "01" "01" "03" "03" "01" "03" "03" "03" "03" "03" "01" "03" "01" "01" "01"
#> [757] "03" "01" "03" "03" "02" "02" "01" "01" "03" "01" "01" "01" "01" "01" "03" "01" "01" "03"
#> [775] "01" "02" "01" "01" "02" "01" "03" "01" "01" "01" "01" "01" "03" "01" "03" "03" "01" "01"
#> [793] "03" "02" "01" "03" "01" "01" "02" "03" "03" "02" "03" "03" "03" "02" "03" "01" "03" "01"
#> [811] "01" "01" "03" "03" "01" "01" "01" "01" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01"
#> [829] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [847] "01" "01" "03" "01" "01" "02" "03" "02" "01" "01" "01" "01" "03" "01" "01" "03" "01" "01"
#> [865] "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [883] "01" "01" "01" "02" "01" "03" "01" "03" "01" "03" "01" "01" "01" "02" "02" "01" "01" "01"
#> [901] "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [919] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "02" "01" "01" "01" "01" "01" "02" "03"
#> [937] "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03"
#> [955] "01" "02" "01" "01" "01" "02" "02" "01" "01" "02" "03" "01" "01" "01" "01" "03" "01" "01"
#> [973] "02" "01" "01" "01" "01" "01" "01" "01" "03" "03" "02" "02" "03" "01" "01" "01" "01" "03"
#> [991] "03" "01" "01" "01" "03" "01" "01" "03" "03" "01" "02" "01" "01" "01" "01" "01" "01" "01"
#> [1009] "01" "01" "01" "01" "01" "02" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03"
#> [1027] "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "03" "01" "02" "01" "01" "03" "01"
#> [1045] "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1063] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01"
#> [1081] "01" "01" "03" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1099] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01"
#> [1117] "01" "03" "01" "01" "01" "03" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01"
#> [1135] "03" "01" "01" "01" "01" "01" "02" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01"
#> [1153] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1171] "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "02" "01" "01" "01" "02" "01" "01" "01"
#> [1189] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1207] "01" "01" "01" "01" "01" "01" "01" "01" "02" "01" "03" "03" "01" "01" "01" "01" "01" "02"
#> [1225] "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "03" "03"
#> [1243] "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "02" "02" "02" "02" "01"
#> [1261] "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "03" "02"
#> [1279] "03" "03" "03" "01" "03" "01" "01" "01" "01" "01" "03" "03" "01" "03" "01" "01" "02" "03"
#> [1297] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "02" "03"
#> [1315] "02" "01" "03" "02" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "02"
#> [1333] "01" "03" "01" "03" "01" "01" "01" "01" "01" "01" "02" "01" "01" "02" "02" "02" "01" "01"
#> [1351] "01" "01" "02" "02" "01" "01" "01" "01" "01" "01" "02" "02" "01" "01" "03" "03" "01" "03"
#> [1369] "01" "03" "03" "03" "03" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1387] "01" "01" "01" "01" "01" "03" "01" "01" "02" "01" "02" "02" "02" "01" "02" "01" "01" "01"
#> [1405] "01" "03" "02" "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "03"
#> [1423] "01" "01" "01" "01" "01" "01" "03" "03" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01"
#> [1441] "01" "01" "01" "01" "01" "01" "01" "01" "02" "01" "03" "02" "01" "01" "02" "01" "01" "01"
#> [1459] "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "03" "03" "03"
#> [1477] "01" "02" "01" "02" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "01" "01"
#> [1495] "02" "01" "01" "01" "01" "02" "01" "01" "01" "01" "01" "03" "01" "02" "01" "02" "01" "01"
#> [1513] "01" "01" "01" "01" "01" "02" "03" "02" "02" "02" "01" "02" "01" "01" "03" "01" "01" "01"
#> [1531] "01" "01" "01" "02" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1549] "01" "01" "03" "01" "01" "01" "01" "01" "01" "02" "02" "01" "01" "01" "01" "03" "02" "01"
#> [1567] "01" "03" "01" "01" "01" "02" "02" "01" "02" "03" "03" "02" "02" "02" "03" "02" "03" "02"
#> [1585] "03" "03" "03" "03" "03" "03" "01" "03" "03" "02" "03" "02" "02" "02" "01" "02" "01" "03"
#> [1603] "01" "02" "02" "01" "01" "02" "02" "02" "03" "03" "03" "03" "03" "03" "03" "02" "01" "02"
#> [1621] "02" "02" "01" "03" "01" "02" "01" "01" "01" "01" "01" "01" "03" "01" "02" "01" "01" "01"
#> [1639] "02" "01" "01" "01" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01"
#> [1657] "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1675] "01" "02" "01" "01" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1693] "03" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "03" "01" "01" "01"
#> [1711] "01" "03" "01" "01" "01" "01" "01" "01" "03" "03" "01" "01" "01" "01" "01" "01" "01" "03"
#> [1729] "01" "03" "03" "01" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01"
#> [1747] "01" "01" "03" "01" "03" "01" "02" "01" "02" "01" "01" "01" "01" "03" "02" "01" "01" "01"
#> [1765] "01" "02" "03" "01" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01"
#> [1783] "01" "01" "01" "01" "03" "01" "01" "03" "01" "03" "03" "03" "02" "01" "01" "03" "02" "01"
#> [1801] "01" "01" "01" "03" "01" "01" "02" "02" "02" "03" "02" "01" "01" "01" "01" "01" "01" "01"
#> [1819] "01" "01" "03" "01" "01" "01" "01" "01" "01" "01" "01" "02" "01" "01" "03" "01" "01" "02"
#> [1837] "02" "01" "01" "01" "01" "01" "02" "01" "01" "01" "01" "01" "01" "01" "01" "03" "01" "01"
#> [1855] "01" "01" "01" "01" "03" "01" "03" "01" "01" "03" "02" "03" "03" "03" "02" "01" "01" "03"
#> [1873] "03" "01" "01" "01" "02" "01" "01" "01" "02" "01" "01" "01" "01" "03" "01" "03" "01" "01"
#> [1891] "01" "02" "01" "01" "01" "03" "01" "01" "01" "03" "01" "03" "01" "03" "01" "01" "03" "01"
#> [1909] "01" "01" "01" "01" "01" "01" "03" "01" "01" "01" "01" "03" "02" "01" "03" "03" "01" "03"
#> [1927] "01" "01" "01" "02" "03" "01" "02" "01" "03" "02" "03" "01" "01" "03" "03" "01" "03" "01"
#> [1945] "03" "02" "03" "03" "01" "03" "03" "01" "01" "02" "01" "01" "02" "03" "02" "03" "02" "03"
#> [1963] "03" "02" "01" "03" "02" "03" "03" "03" "01" "02" "03" "03" "02" "03" "01" "02" "03" "02"
#> [1981] "03" "03" "03" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [1999] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "01" "02"
#> [2017] "02" "03" "02" "02" "02" "02" "02" "02" "02" "03" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2035] "02" "02" "02" "02" "02" "02" "02" "03" "02" "02" "02" "02" "02" "02" "02" "01" "02" "02"
#> [2053] "02" "02" "02" "02" "02" "02" "03" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2071] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2089] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2107] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2125] "01" "02" "02" "01" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2143] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2161] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "03" "02" "02" "02" "02"
#> [2179] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "01" "02" "01" "02" "03" "02" "03"
#> [2197] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2215] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2233] "02" "02" "02" "02" "03" "02" "02" "03" "02" "03" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2251] "02" "03" "02" "02" "02" "02" "02" "02" "01" "03" "02" "02" "02" "02" "02" "03" "02" "02"
#> [2269] "03" "02" "02" "02" "01" "02" "01" "02" "02" "03" "02" "02" "02" "02" "01" "02" "02" "02"
#> [2287] "01" "03" "03" "02" "02" "03" "02" "02" "01" "02" "02" "02" "01" "03" "01" "01" "01" "02"
#> [2305] "01" "02" "01" "01" "01" "02" "01" "03" "02" "01" "01" "01" "01" "02" "02" "02" "01" "02"
#> [2323] "02" "03" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "03" "01" "01" "03" "02" "01"
#> [2341] "03" "03" "02" "02" "02" "02" "02" "02" "02" "02" "03" "02" "02" "02" "02" "02" "03" "02"
#> [2359] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2377] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2395] "02" "02" "02" "02" "03" "02" "01" "01" "01" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2413] "02" "01" "02" "02" "02" "02" "02" "01" "02" "02" "03" "02" "02" "02" "02" "02" "02" "02"
#> [2431] "02" "02" "01" "03" "01" "02" "03" "03" "03" "01" "01" "01" "03" "03" "03" "02" "02" "02"
#> [2449] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2467] "02" "02" "02" "02" "03" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2485] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2503] "02" "02" "02" "02" "02" "03" "03" "02" "03" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2521] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "01" "02" "03" "02" "02"
#> [2539] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "01" "02" "03" "03" "01" "01" "02" "03"
#> [2557] "02" "01" "02" "02" "03" "02" "03" "02" "02" "03" "02" "02" "02" "02" "02" "01" "01" "01"
#> [2575] "03" "03" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "01" "02" "02" "02" "02"
#> [2593] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "03" "01"
#> [2611] "02" "02" "02" "02" "02" "03" "02" "02" "02" "02" "02" "02" "01" "02" "02" "02" "01" "01"
#> [2629] "01" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "03" "02" "02" "02" "02"
#> [2647] "02" "02" "03" "02" "01" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "03"
#> [2665] "03" "02" "03" "02" "02" "03" "02" "01" "03" "02" "03" "02" "02" "03" "01" "01" "01" "02"
#> [2683] "01" "01" "01" "02" "02" "02" "02" "03" "02" "02" "02" "02" "01" "02" "03" "02" "02" "03"
#> [2701] "02" "02" "01" "02" "02" "03" "01" "02" "01" "01" "02" "01" "01" "02" "02" "02" "01" "01"
#> [2719] "02" "01" "02" "02" "01" "02" "03" "01" "03" "01" "01" "03" "02" "02" "03" "02" "03" "01"
#> [2737] "01" "03" "01" "03" "01" "01" "02" "02" "02" "01" "01" "01" "01" "03" "01" "01" "02" "02"
#> [2755] "03" "03" "01" "02" "02" "02" "02" "02" "02" "02" "02" "01" "01" "02" "02" "02" "02" "02"
#> [2773] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "01"
#> [2791] "01" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2809] "02" "02" "02" "02" "02" "02" "02" "02" "02" "01" "01" "01" "02" "02" "02" "02" "02" "02"
#> [2827] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02"
#> [2845] "02" "02" "02" "02" "02" "02" "02" "02" "03" "02" "01" "02" "02" "01" "02" "02" "02" "02"
#> [2863] "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "02" "01" "02"
#> [2881] "02"
Heatmaps of the top rows:
top_rows_heatmap(res_rh)
Top rows on each node:
top_rows_overlap(res_rh, method = "upset")
UMAP plot which shows how samples are separated.
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 347),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 347),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 438),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 438),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 508),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 508),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 577),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 577),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 628),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 628),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 634),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 634),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 677),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 677),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 755),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 755),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 890),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 890),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 960),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 960),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 979),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 979),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1000),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1000),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1348),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1348),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1387),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1387),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1390),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1390),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1908),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1908),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2292),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2292),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2797),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2797),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2878),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2878),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3262),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3262),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3273),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3273),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3301),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3301),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3324),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3324),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 4249),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 4249),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 5466),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 5466),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 6708),
method = "UMAP", top_value_method = "SD", top_n = 1200, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 6708),
method = "UMAP", top_value_method = "ATC", top_n = 1200, scale_rows = TRUE)
Signatures on the heatmap are the union of all signatures found on every node on the hierarchy. The number of k-means on rows are automatically selected by the function.
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 347))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 438))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 508))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 577))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 628))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 634))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 677))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 755))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 890))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 960))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 979))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1000))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1348))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1387))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1390))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1908))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2292))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2797))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2878))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 3262))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 3273))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 3301))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 3324))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 4249))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 5466))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 6708))
Compare signatures from different nodes:
compare_signatures(res_rh, verbose = FALSE)
If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs. Note it only works on every node and the final signatures
are the union of all signatures of all nodes.
# code only for demonstration
# e.g. to show the top 500 most significant rows on each node.
tb = get_signature(res_rh, top_signatures = 500)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 347))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 438))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 508))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 577))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 628))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 634))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 677))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 755))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 890))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 960))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 979))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1000))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1348))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1387))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1390))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1908))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 2292))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 2797))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 2878))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 3262))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 3273))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 3301))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 3324))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 4249))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 5466))
#> level1.class
#> class 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 6708))
#> level1.class
#> class 0
Child nodes: Node01 , Node02 , Node03 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0"]
A summary of res and all the functions that can be applied to it:
res
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 10389 rows and 500 columns, randomly sampled from 2881 columns.
#> Top rows (980) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.983 0.993 0.4868 0.512 0.512
#> 3 3 1.000 0.973 0.990 0.2476 0.858 0.729
#> 4 4 0.998 0.950 0.977 0.0822 0.938 0.846
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
get_classes(res, k = 2)
#> class p
#> 1 1 0.000
#> 2 1 0.249
#> 3 2 0.000
#> 4 1 0.000
#> 5 1 0.000
#> 6 1 0.000
#> 7 1 0.000
#> 8 1 0.000
#> 9 1 0.000
#> 10 1 0.000
#> 11 1 0.000
#> 12 1 0.000
#> 13 1 0.000
#> 14 1 0.000
#> 15 1 0.000
#> 16 1 0.000
#> 17 1 0.000
#> 18 1 0.000
#> 19 1 0.000
#> 20 1 0.000
#> 21 1 0.000
#> 22 1 0.000
#> 23 1 0.000
#> 24 1 0.000
#> 25 1 0.000
#> 26 1 0.249
#> 27 1 0.000
#> 28 1 0.000
#> 29 1 0.000
#> 30 1 0.000
#> 31 1 0.000
#> 32 1 0.000
#> 33 1 0.000
#> 34 1 0.000
#> 35 1 0.249
#> 36 1 0.000
#> 37 1 0.000
#> 38 1 0.000
#> 39 1 0.000
#> 40 1 0.000
#> 41 1 0.000
#> 42 2 0.000
#> 43 1 0.000
#> 44 1 0.000
#> 45 1 0.000
#> 46 1 0.000
#> 47 1 0.000
#> 48 1 0.000
#> 49 1 0.000
#> 50 1 0.000
#> 51 1 0.000
#> 52 1 0.249
#> 53 1 0.000
#> 54 1 0.000
#> 55 1 0.249
#> 56 2 0.000
#> 57 2 0.000
#> 58 1 0.000
#> 59 1 0.000
#> 60 1 0.000
#> 61 1 0.000
#> 62 1 0.000
#> 63 1 0.249
#> 64 2 0.000
#> 65 1 0.000
#> 66 1 0.249
#> 67 1 0.000
#> 68 1 0.502
#> 69 1 0.000
#> 70 1 0.000
#> 71 1 1.000
#> 72 1 0.000
#> 73 1 0.253
#> 74 1 0.249
#> 75 1 0.000
#> 76 1 0.751
#> 77 1 0.000
#> 78 1 0.000
#> 79 2 0.000
#> 80 1 0.000
#> 81 1 1.000
#> 82 1 0.000
#> 83 1 0.000
#> 84 1 0.249
#> 85 1 0.249
#> 86 2 0.000
#> 87 1 0.000
#> 88 1 0.000
#> 89 1 0.000
#> 90 1 0.249
#> 91 1 0.000
#> 92 1 0.000
#> 93 2 0.000
#> 94 1 0.000
#> 95 1 0.000
#> 96 1 0.000
#> 97 1 0.000
#> 98 1 0.000
#> 99 1 0.498
#> 100 1 0.000
#> 101 1 0.000
#> 102 1 0.000
#> 103 1 0.000
#> 104 1 0.000
#> 105 1 0.000
#> 106 1 0.000
#> 107 2 0.000
#> 108 1 0.502
#> 109 1 0.751
#> 110 1 0.000
#> 111 1 0.000
#> 112 1 0.249
#> 113 1 1.000
#> 114 1 0.751
#> 115 1 1.000
#> 116 2 0.000
#> 117 2 0.000
#> 118 1 0.000
#> 119 1 0.000
#> 120 1 1.000
#> 121 1 0.000
#> 122 1 0.747
#> 123 2 0.000
#> 124 2 0.000
#> 125 2 0.000
#> 126 2 0.000
#> 127 1 0.502
#> 128 1 0.000
#> 129 1 0.000
#> 130 1 0.502
#> 131 1 0.000
#> 132 1 0.000
#> 133 1 0.000
#> 134 1 1.000
#> 135 1 0.751
#> 136 2 0.000
#> 137 1 0.000
#> 138 1 0.000
#> 139 1 0.000
#> 140 1 0.502
#> 141 1 0.000
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.249
#> 145 1 0.000
#> 146 2 0.000
#> 147 1 0.000
#> 148 2 0.000
#> 149 1 0.000
#> 150 1 1.000
#> 151 1 0.249
#> 152 1 0.000
#> 153 1 0.000
#> 154 1 0.000
#> 155 1 0.000
#> 156 1 0.000
#> 157 1 0.000
#> 158 2 1.000
#> 159 1 0.000
#> 160 1 0.000
#> 161 1 0.000
#> 162 1 0.000
#> 163 1 0.000
#> 164 1 0.000
#> 165 2 1.000
#> 166 1 0.000
#> 167 1 0.000
#> 168 1 0.000
#> 169 1 0.000
#> 170 1 0.000
#> 171 2 0.502
#> 172 1 0.000
#> 173 1 0.000
#> 174 2 0.000
#> 175 2 0.249
#> 176 1 0.000
#> 177 1 0.000
#> 178 1 0.000
#> 179 1 0.000
#> 180 1 0.000
#> 181 1 0.000
#> 182 1 0.000
#> 183 1 0.000
#> 184 1 0.000
#> 185 1 0.000
#> 186 1 0.000
#> 187 1 0.000
#> 188 1 0.000
#> 189 1 0.000
#> 190 2 0.000
#> 191 1 0.498
#> 192 1 0.000
#> 193 1 0.000
#> 194 1 0.000
#> 195 1 0.000
#> 196 1 0.000
#> 197 2 1.000
#> 198 1 0.000
#> 199 2 1.000
#> 200 2 1.000
#> 201 1 0.000
#> 202 1 0.000
#> 203 1 0.000
#> 204 1 0.000
#> 205 1 0.000
#> 206 1 0.000
#> 207 1 0.000
#> 208 1 0.000
#> 209 1 0.000
#> 210 2 0.000
#> 211 1 0.000
#> 212 2 0.000
#> 213 1 0.000
#> 214 1 0.000
#> 215 1 0.000
#> 216 1 0.000
#> 217 1 0.000
#> 218 2 0.000
#> 219 1 0.000
#> 220 1 0.000
#> 221 2 1.000
#> 222 1 0.000
#> 223 2 1.000
#> 224 1 0.000
#> 225 1 0.000
#> 226 1 0.000
#> 227 1 0.000
#> 228 1 0.000
#> 229 1 0.000
#> 230 2 0.000
#> 231 1 0.000
#> 232 1 0.751
#> 233 1 0.000
#> 234 1 0.000
#> 235 2 0.000
#> 236 2 1.000
#> 237 2 0.000
#> 238 1 0.000
#> 239 1 0.000
#> 240 1 0.000
#> 241 1 0.000
#> 242 1 0.000
#> 243 1 0.000
#> 244 1 0.000
#> 245 1 0.000
#> 246 1 0.000
#> 247 1 0.000
#> 248 2 0.000
#> 249 1 0.000
#> 250 1 0.000
#> 251 1 0.249
#> 252 1 0.000
#> 253 1 0.000
#> 254 1 0.000
#> 255 1 0.249
#> 256 1 0.249
#> 257 1 0.000
#> 258 1 0.000
#> 259 1 0.498
#> 260 1 0.000
#> 261 1 0.000
#> 262 1 0.249
#> 263 1 0.000
#> 264 1 0.000
#> 265 1 0.253
#> 266 1 0.000
#> 267 1 0.000
#> 268 1 0.000
#> 269 1 0.253
#> 270 1 0.000
#> 271 1 0.000
#> 272 1 0.000
#> 273 1 0.000
#> 274 1 0.000
#> 275 1 0.000
#> 276 1 0.000
#> 277 1 0.000
#> 278 1 0.000
#> 279 1 0.000
#> 280 1 0.249
#> 281 2 0.000
#> 282 1 0.000
#> 283 1 0.253
#> 284 1 0.000
#> 285 1 0.000
#> 286 2 0.000
#> 287 1 0.000
#> 288 1 0.000
#> 289 1 0.000
#> 290 2 1.000
#> 291 1 0.000
#> 292 1 0.000
#> 293 1 0.000
#> 294 1 0.000
#> 295 2 0.000
#> 296 1 0.000
#> 297 1 0.000
#> 298 2 1.000
#> 299 2 0.249
#> 300 1 0.000
#> 301 2 0.249
#> 302 1 0.502
#> 303 1 0.249
#> 304 2 0.000
#> 305 1 0.000
#> 306 2 0.000
#> 307 2 0.000
#> 308 2 1.000
#> 309 2 0.000
#> 310 1 0.000
#> 311 1 0.000
#> 312 1 0.000
#> 313 2 0.000
#> 314 1 0.000
#> 315 1 0.000
#> 316 2 1.000
#> 317 1 0.000
#> 318 1 0.498
#> 319 1 0.000
#> 320 1 0.249
#> 321 1 0.000
#> 322 1 0.000
#> 323 2 0.000
#> 324 1 0.000
#> 325 1 0.000
#> 326 1 0.249
#> 327 1 0.000
#> 328 1 0.000
#> 329 1 0.000
#> 330 1 0.000
#> 331 1 0.000
#> 332 1 0.000
#> 333 1 0.253
#> 334 1 0.000
#> 335 1 0.000
#> 336 2 0.000
#> 337 2 0.000
#> 338 1 0.000
#> 339 1 0.249
#> 340 1 0.000
#> 341 1 0.000
#> 342 1 1.000
#> 343 2 0.000
#> 344 1 0.000
#> 345 1 0.000
#> 346 1 0.000
#> 347 2 0.000
#> 348 1 0.747
#> 349 1 0.000
#> 350 2 0.000
#> 351 1 0.249
#> 352 1 0.000
#> 353 1 0.000
#> 354 1 0.000
#> 355 1 0.000
#> 356 1 0.000
#> 357 1 0.000
#> 358 1 0.000
#> 359 2 1.000
#> 360 1 0.000
#> 361 2 0.249
#> 362 1 0.000
#> 363 1 0.000
#> 364 1 0.249
#> 365 1 0.000
#> 366 1 0.249
#> 367 1 0.000
#> 368 1 0.000
#> 369 1 0.000
#> 370 1 0.000
#> 371 1 0.000
#> 372 2 1.000
#> 373 1 0.000
#> 374 1 0.000
#> 375 1 0.000
#> 376 1 0.000
#> 377 1 0.000
#> 378 1 0.000
#> 379 2 1.000
#> 380 1 0.000
#> 381 1 0.000
#> 382 1 0.000
#> 383 2 0.000
#> 384 1 0.498
#> 385 1 0.000
#> 386 1 0.000
#> 387 1 0.000
#> 388 1 0.253
#> 389 1 0.751
#> 390 2 0.502
#> 391 1 0.000
#> 392 1 0.000
#> 393 1 0.000
#> 394 1 1.000
#> 395 2 0.000
#> 396 1 0.000
#> 397 1 0.498
#> 398 1 0.000
#> 399 1 0.000
#> 400 1 0.000
#> 401 1 0.000
#> 402 1 0.000
#> 403 1 1.000
#> 404 1 0.000
#> 405 1 0.000
#> 406 2 0.000
#> 407 1 0.000
#> 408 1 0.249
#> 409 1 0.249
#> 410 1 0.249
#> 411 1 0.249
#> 412 1 0.000
#> 413 2 0.000
#> 414 1 0.747
#> 415 1 0.000
#> 416 1 0.249
#> 417 1 0.000
#> 418 1 0.000
#> 419 1 0.502
#> 420 1 0.000
#> 421 1 1.000
#> 422 1 0.000
#> 423 1 0.000
#> 424 1 0.000
#> 425 2 0.249
#> 426 1 0.000
#> 427 1 0.000
#> 428 1 0.000
#> 429 1 0.000
#> 430 1 0.000
#> 431 1 0.502
#> 432 2 1.000
#> 433 1 0.249
#> 434 2 0.249
#> 435 1 0.000
#> 436 2 0.000
#> 437 1 0.000
#> 438 2 0.000
#> 439 1 0.000
#> 440 2 0.000
#> 441 2 0.000
#> 442 1 0.000
#> 443 1 0.000
#> 444 1 0.000
#> 445 1 0.000
#> 446 1 0.249
#> 447 1 0.000
#> 448 1 0.000
#> 449 1 0.000
#> 450 1 0.000
#> 451 1 0.000
#> 452 1 0.000
#> 453 1 0.000
#> 454 1 0.502
#> 455 1 0.000
#> 456 1 0.000
#> 457 1 0.000
#> 458 1 0.000
#> 459 1 0.000
#> 460 1 0.000
#> 461 1 0.000
#> 462 1 0.000
#> 463 1 0.000
#> 464 1 0.000
#> 465 1 0.000
#> 466 1 0.000
#> 467 1 0.000
#> 468 1 0.000
#> 469 2 1.000
#> 470 2 1.000
#> 471 1 0.000
#> 472 1 0.000
#> 473 1 0.000
#> 474 2 1.000
#> 475 2 1.000
#> 476 1 0.000
#> 477 1 0.000
#> 478 1 0.000
#> 479 1 0.000
#> 480 1 0.000
#> 481 1 0.000
#> 482 1 0.000
#> 483 1 0.000
#> 484 1 0.000
#> 485 2 0.000
#> 486 1 0.000
#> 487 1 0.000
#> 488 1 0.000
#> 489 2 0.502
#> 490 2 0.000
#> 491 2 0.000
#> 492 1 0.000
#> 493 1 0.000
#> 494 2 0.000
#> 495 1 0.000
#> 496 1 0.000
#> 497 2 0.000
#> 498 2 0.249
#> 499 1 0.000
#> 500 1 0.000
#> 501 2 0.000
#> 502 1 0.000
#> 503 1 0.000
#> 504 2 1.000
#> 505 1 0.000
#> 506 2 0.000
#> 507 1 0.000
#> 508 1 0.000
#> 509 1 0.000
#> 510 1 0.000
#> 511 1 0.000
#> 512 2 1.000
#> 513 1 0.000
#> 514 1 0.000
#> 515 1 0.000
#> 516 1 0.000
#> 517 2 0.000
#> 518 1 0.000
#> 519 1 0.000
#> 520 1 0.000
#> 521 1 0.000
#> 522 1 0.000
#> 523 1 0.253
#> 524 2 1.000
#> 525 1 0.000
#> 526 1 0.000
#> 527 1 0.000
#> 528 1 0.000
#> 529 1 1.000
#> 530 2 0.000
#> 531 2 0.000
#> 532 2 0.000
#> 533 1 0.000
#> 534 2 0.000
#> 535 1 0.000
#> 536 1 0.000
#> 537 1 0.000
#> 538 2 0.000
#> 539 1 0.000
#> 540 1 0.000
#> 541 1 0.000
#> 542 2 0.000
#> 543 1 0.000
#> 544 1 0.000
#> 545 2 0.000
#> 546 1 0.000
#> 547 1 0.000
#> 548 1 0.000
#> 549 1 0.000
#> 550 1 0.000
#> 551 1 0.000
#> 552 1 0.000
#> 553 1 0.000
#> 554 1 0.000
#> 555 1 0.000
#> 556 1 0.498
#> 557 1 0.000
#> 558 1 0.502
#> 559 1 0.000
#> 560 1 0.502
#> 561 1 1.000
#> 562 1 0.249
#> 563 2 0.000
#> 564 1 0.751
#> 565 1 0.000
#> 566 1 0.000
#> 567 1 0.498
#> 568 2 0.000
#> 569 1 0.000
#> 570 1 0.000
#> 571 1 0.502
#> 572 2 0.000
#> 573 2 0.000
#> 574 1 0.000
#> 575 1 0.000
#> 576 1 0.000
#> 577 1 0.000
#> 578 1 0.000
#> 579 1 0.000
#> 580 2 0.000
#> 581 1 0.249
#> 582 1 0.000
#> 583 1 0.000
#> 584 1 0.249
#> 585 1 0.502
#> 586 1 0.000
#> 587 2 0.498
#> 588 1 0.249
#> 589 1 0.751
#> 590 1 0.502
#> 591 1 0.502
#> 592 1 0.000
#> 593 1 0.000
#> 594 1 0.000
#> 595 1 0.000
#> 596 1 0.498
#> 597 1 0.249
#> 598 1 0.000
#> 599 1 0.000
#> 600 1 0.000
#> 601 1 0.000
#> 602 1 0.498
#> 603 1 0.000
#> 604 1 0.000
#> 605 1 0.000
#> 606 1 0.000
#> 607 2 0.000
#> 608 1 0.000
#> 609 1 0.000
#> 610 1 0.000
#> 611 1 0.000
#> 612 1 0.000
#> 613 1 0.253
#> 614 1 0.000
#> 615 1 0.249
#> 616 2 0.249
#> 617 1 0.000
#> 618 1 0.000
#> 619 1 0.000
#> 620 1 0.000
#> 621 1 0.000
#> 622 1 0.000
#> 623 1 0.000
#> 624 1 0.747
#> 625 1 0.000
#> 626 1 0.000
#> 627 1 0.000
#> 628 1 0.000
#> 629 1 0.000
#> 630 1 0.000
#> 631 1 0.000
#> 632 1 0.000
#> 633 1 0.000
#> 634 1 0.000
#> 635 1 0.498
#> 636 2 0.253
#> 637 1 0.000
#> 638 1 0.000
#> 639 1 0.249
#> 640 1 0.253
#> 641 1 0.000
#> 642 1 0.502
#> 643 1 0.000
#> 644 1 0.249
#> 645 1 0.000
#> 646 1 0.000
#> 647 1 0.000
#> 648 1 0.000
#> 649 2 0.000
#> 650 1 0.000
#> 651 1 0.000
#> 652 1 0.000
#> 653 1 0.000
#> 654 1 0.000
#> 655 1 0.000
#> 656 1 0.000
#> 657 1 0.000
#> 658 1 0.000
#> 659 1 0.000
#> 660 1 0.000
#> 661 1 0.000
#> 662 1 0.000
#> 663 2 0.000
#> 664 1 0.000
#> 665 1 0.000
#> 666 1 0.000
#> 667 1 0.000
#> 668 1 0.000
#> 669 1 0.000
#> 670 1 0.000
#> 671 2 0.000
#> 672 1 0.000
#> 673 2 0.000
#> 674 1 0.000
#> 675 1 0.000
#> 676 2 0.253
#> 677 1 0.000
#> 678 1 0.000
#> 679 2 0.000
#> 680 1 0.249
#> 681 1 0.000
#> 682 1 0.000
#> 683 1 0.000
#> 684 2 0.751
#> 685 2 0.502
#> 686 2 1.000
#> 687 2 1.000
#> 688 1 0.000
#> 689 1 0.000
#> 690 1 0.000
#> 691 1 0.000
#> 692 1 0.000
#> 693 2 1.000
#> 694 1 0.000
#> 695 2 0.000
#> 696 1 0.000
#> 697 2 1.000
#> 698 1 0.000
#> 699 1 0.000
#> 700 1 0.000
#> 701 2 1.000
#> 702 1 0.000
#> 703 1 0.000
#> 704 1 0.000
#> 705 1 0.000
#> 706 2 0.000
#> 707 2 0.249
#> 708 1 0.000
#> 709 1 0.000
#> 710 1 0.751
#> 711 1 0.000
#> 712 2 0.000
#> 713 1 0.000
#> 714 1 0.000
#> 715 1 0.000
#> 716 1 0.000
#> 717 1 0.000
#> 718 1 0.000
#> 719 2 0.000
#> 720 1 0.000
#> 721 1 0.000
#> 722 2 0.000
#> 723 2 0.000
#> 724 1 0.000
#> 725 2 0.000
#> 726 1 0.249
#> 727 1 0.000
#> 728 1 0.000
#> 729 1 0.000
#> 730 1 0.000
#> 731 1 0.000
#> 732 1 0.000
#> 733 1 0.000
#> 734 1 0.000
#> 735 1 0.000
#> 736 1 0.000
#> 737 1 0.000
#> 738 1 0.000
#> 739 1 0.000
#> 740 1 0.000
#> 741 1 0.000
#> 742 1 0.000
#> 743 1 0.000
#> 744 1 0.000
#> 745 1 0.000
#> 746 1 0.000
#> 747 1 0.253
#> 748 1 0.000
#> 749 1 0.000
#> 750 2 1.000
#> 751 1 0.000
#> 752 1 0.000
#> 753 1 0.000
#> 754 1 0.000
#> 755 1 0.000
#> 756 1 0.000
#> 757 1 0.000
#> 758 1 0.000
#> 759 1 0.000
#> 760 2 0.253
#> 761 2 0.253
#> 762 2 0.751
#> 763 1 0.000
#> 764 1 0.000
#> 765 1 0.253
#> 766 1 0.000
#> 767 1 0.000
#> 768 1 0.000
#> 769 1 0.000
#> 770 1 0.000
#> 771 1 0.000
#> 772 1 0.000
#> 773 1 0.000
#> 774 1 0.000
#> 775 1 0.000
#> 776 2 0.000
#> 777 1 0.000
#> 778 1 0.000
#> 779 2 0.000
#> 780 1 0.000
#> 781 1 0.000
#> 782 1 0.000
#> 783 1 0.000
#> 784 1 0.000
#> 785 1 0.000
#> 786 1 0.000
#> 787 1 0.000
#> 788 1 0.000
#> 789 1 0.000
#> 790 1 0.000
#> 791 1 0.000
#> 792 1 0.000
#> 793 2 1.000
#> 794 2 0.000
#> 795 1 0.000
#> 796 1 0.000
#> 797 1 0.000
#> 798 1 0.000
#> 799 2 0.249
#> 800 1 0.000
#> 801 1 0.000
#> 802 2 0.000
#> 803 1 0.000
#> 804 1 0.000
#> 805 1 0.000
#> 806 2 0.498
#> 807 1 0.000
#> 808 1 0.000
#> 809 1 0.000
#> 810 1 0.000
#> 811 1 0.000
#> 812 1 0.000
#> 813 1 0.747
#> 814 2 0.000
#> 815 1 0.000
#> 816 1 1.000
#> 817 1 0.000
#> 818 1 0.249
#> 819 1 0.000
#> 820 2 0.000
#> 821 1 0.000
#> 822 1 0.000
#> 823 1 0.000
#> 824 1 0.000
#> 825 1 0.249
#> 826 1 0.000
#> 827 1 0.000
#> 828 1 0.000
#> 829 1 0.000
#> 830 1 0.000
#> 831 1 0.000
#> 832 1 0.000
#> 833 1 0.000
#> 834 1 0.000
#> 835 1 0.000
#> 836 1 0.000
#> 837 1 0.000
#> 838 1 0.000
#> 839 1 0.000
#> 840 1 0.000
#> 841 1 0.000
#> 842 1 0.000
#> 843 1 0.000
#> 844 1 0.000
#> 845 1 0.000
#> 846 1 0.000
#> 847 1 0.000
#> 848 1 0.000
#> 849 2 0.000
#> 850 1 0.502
#> 851 1 0.249
#> 852 2 0.000
#> 853 1 1.000
#> 854 2 0.000
#> 855 1 0.249
#> 856 1 1.000
#> 857 1 0.253
#> 858 1 1.000
#> 859 1 1.000
#> 860 1 0.000
#> 861 1 0.000
#> 862 1 1.000
#> 863 1 1.000
#> 864 1 0.000
#> 865 1 0.000
#> 866 1 0.000
#> 867 1 0.000
#> 868 1 0.502
#> 869 1 0.000
#> 870 1 0.000
#> 871 1 0.000
#> 872 1 0.000
#> 873 1 0.000
#> 874 1 0.000
#> 875 1 0.000
#> 876 1 0.000
#> 877 1 0.751
#> 878 1 0.000
#> 879 1 0.253
#> 880 1 0.000
#> 881 1 0.000
#> 882 1 0.000
#> 883 1 0.000
#> 884 1 1.000
#> 885 1 0.000
#> 886 2 0.000
#> 887 1 0.000
#> 888 2 0.000
#> 889 1 0.000
#> 890 1 1.000
#> 891 1 0.000
#> 892 1 0.000
#> 893 1 0.000
#> 894 1 0.000
#> 895 1 0.000
#> 896 2 0.000
#> 897 2 0.000
#> 898 1 0.000
#> 899 1 0.000
#> 900 1 0.000
#> 901 1 0.000
#> 902 1 0.000
#> 903 1 0.000
#> 904 1 0.249
#> 905 1 0.000
#> 906 1 0.253
#> 907 1 0.502
#> 908 1 0.000
#> 909 1 0.000
#> 910 1 0.249
#> 911 1 0.000
#> 912 1 0.000
#> 913 1 0.000
#> 914 1 0.253
#> 915 1 0.000
#> 916 1 0.000
#> 917 1 0.000
#> 918 1 0.000
#> 919 1 0.000
#> 920 1 0.000
#> 921 1 0.000
#> 922 1 0.000
#> 923 1 0.000
#> 924 1 0.000
#> 925 1 0.000
#> 926 1 0.000
#> 927 1 0.253
#> 928 1 0.000
#> 929 2 0.000
#> 930 1 1.000
#> 931 1 1.000
#> 932 1 0.000
#> 933 1 0.000
#> 934 1 0.000
#> 935 2 0.000
#> 936 1 1.000
#> 937 1 0.000
#> 938 2 0.000
#> 939 1 0.000
#> 940 1 0.000
#> 941 1 0.000
#> 942 1 0.249
#> 943 1 0.000
#> 944 1 0.000
#> 945 1 0.000
#> 946 1 0.000
#> 947 1 0.000
#> 948 1 0.000
#> 949 1 0.000
#> 950 1 0.000
#> 951 1 0.253
#> 952 1 0.502
#> 953 1 0.000
#> 954 1 0.000
#> 955 1 0.502
#> 956 2 0.000
#> 957 1 0.502
#> 958 1 0.502
#> 959 1 0.000
#> 960 2 0.000
#> 961 2 0.000
#> 962 1 0.000
#> 963 1 0.000
#> 964 2 0.000
#> 965 1 0.498
#> 966 1 0.249
#> 967 1 0.000
#> 968 1 1.000
#> 969 1 0.000
#> 970 1 0.000
#> 971 1 0.000
#> 972 1 0.253
#> 973 2 0.000
#> 974 1 0.502
#> 975 1 0.000
#> 976 1 0.000
#> 977 1 0.000
#> 978 1 0.000
#> 979 1 0.000
#> 980 1 0.253
#> 981 1 0.751
#> 982 1 1.000
#> 983 2 0.000
#> 984 2 0.000
#> 985 1 0.000
#> 986 1 0.000
#> 987 1 0.000
#> 988 1 0.000
#> 989 1 0.502
#> 990 1 0.000
#> 991 1 0.000
#> 992 1 0.253
#> 993 1 0.000
#> 994 1 0.000
#> 995 1 0.000
#> 996 1 0.000
#> 997 1 0.000
#> 998 1 0.000
#> 999 1 1.000
#> 1000 1 0.502
#> 1001 2 0.000
#> 1002 1 0.502
#> 1003 1 0.253
#> 1004 1 0.000
#> 1005 1 0.000
#> 1006 1 0.000
#> 1007 1 0.000
#> 1008 1 0.249
#> 1009 1 0.751
#> 1010 1 0.000
#> 1011 1 0.253
#> 1012 1 0.000
#> 1013 1 0.000
#> 1014 2 0.000
#> 1015 1 1.000
#> 1016 2 0.000
#> 1017 1 0.000
#> 1018 1 0.000
#> 1019 1 0.249
#> 1020 1 0.253
#> 1021 1 0.000
#> 1022 1 0.000
#> 1023 1 0.000
#> 1024 1 0.000
#> 1025 1 0.000
#> 1026 1 0.498
#> 1027 1 0.253
#> 1028 1 0.000
#> 1029 1 0.000
#> 1030 1 0.000
#> 1031 1 0.249
#> 1032 1 0.000
#> 1033 1 0.000
#> 1034 1 0.000
#> 1035 1 0.249
#> 1036 1 0.000
#> 1037 1 0.000
#> 1038 1 0.000
#> 1039 1 0.000
#> 1040 2 0.000
#> 1041 1 0.000
#> 1042 1 0.000
#> 1043 1 0.000
#> 1044 1 0.000
#> 1045 1 0.000
#> 1046 1 0.502
#> 1047 1 0.000
#> 1048 1 0.000
#> 1049 1 0.000
#> 1050 1 0.249
#> 1051 1 0.000
#> 1052 1 0.498
#> 1053 1 0.000
#> 1054 1 0.000
#> 1055 1 0.253
#> 1056 1 0.000
#> 1057 1 0.000
#> 1058 1 0.000
#> 1059 1 0.751
#> 1060 1 0.000
#> 1061 1 0.000
#> 1062 1 0.000
#> 1063 1 0.000
#> 1064 1 0.000
#> 1065 1 0.249
#> 1066 1 0.000
#> 1067 1 0.751
#> 1068 1 0.249
#> 1069 1 0.000
#> 1070 1 0.000
#> 1071 1 0.249
#> 1072 1 0.000
#> 1073 1 0.000
#> 1074 1 0.249
#> 1075 1 0.000
#> 1076 1 0.000
#> 1077 1 0.000
#> 1078 1 0.249
#> 1079 1 0.000
#> 1080 1 0.000
#> 1081 1 0.000
#> 1082 1 0.249
#> 1083 1 0.000
#> 1084 1 0.000
#> 1085 1 0.000
#> 1086 1 0.000
#> 1087 1 0.249
#> 1088 1 0.000
#> 1089 1 0.000
#> 1090 1 0.747
#> 1091 1 0.502
#> 1092 1 0.000
#> 1093 1 0.249
#> 1094 1 0.000
#> 1095 1 0.000
#> 1096 1 0.000
#> 1097 1 0.249
#> 1098 1 0.751
#> 1099 1 0.000
#> 1100 1 0.747
#> 1101 1 0.249
#> 1102 1 0.000
#> 1103 1 0.000
#> 1104 1 0.498
#> 1105 1 0.000
#> 1106 1 0.000
#> 1107 1 0.249
#> 1108 1 0.000
#> 1109 1 0.000
#> 1110 1 0.000
#> 1111 1 0.000
#> 1112 1 0.000
#> 1113 1 0.000
#> 1114 1 0.000
#> 1115 1 0.249
#> 1116 1 0.000
#> 1117 1 0.000
#> 1118 1 0.000
#> 1119 1 1.000
#> 1120 1 1.000
#> 1121 1 1.000
#> 1122 1 0.000
#> 1123 1 0.253
#> 1124 1 0.000
#> 1125 1 0.000
#> 1126 1 0.249
#> 1127 1 0.000
#> 1128 2 1.000
#> 1129 1 0.249
#> 1130 1 0.000
#> 1131 1 0.502
#> 1132 1 0.502
#> 1133 1 0.000
#> 1134 1 0.000
#> 1135 1 0.000
#> 1136 1 0.000
#> 1137 1 0.000
#> 1138 1 0.000
#> 1139 1 0.000
#> 1140 1 0.751
#> 1141 2 0.000
#> 1142 1 0.249
#> 1143 1 0.000
#> 1144 1 0.000
#> 1145 1 0.000
#> 1146 1 0.249
#> 1147 1 0.000
#> 1148 1 0.000
#> 1149 1 0.000
#> 1150 1 0.751
#> 1151 1 0.253
#> 1152 1 0.000
#> 1153 1 0.253
#> 1154 1 0.249
#> 1155 1 0.000
#> 1156 1 0.000
#> 1157 1 0.000
#> 1158 1 0.000
#> 1159 1 1.000
#> 1160 1 0.502
#> 1161 1 0.502
#> 1162 1 0.000
#> 1163 1 0.000
#> 1164 1 0.000
#> 1165 1 0.000
#> 1166 1 0.000
#> 1167 1 0.000
#> 1168 1 0.000
#> 1169 1 0.000
#> 1170 1 0.253
#> 1171 1 1.000
#> 1172 1 0.000
#> 1173 1 1.000
#> 1174 1 0.498
#> 1175 1 0.000
#> 1176 1 0.253
#> 1177 1 0.000
#> 1178 1 0.253
#> 1179 1 1.000
#> 1180 1 0.000
#> 1181 2 0.000
#> 1182 1 0.502
#> 1183 1 1.000
#> 1184 1 0.751
#> 1185 2 0.000
#> 1186 1 0.000
#> 1187 1 0.000
#> 1188 1 0.000
#> 1189 1 0.000
#> 1190 1 0.751
#> 1191 1 0.502
#> 1192 1 0.000
#> 1193 1 0.502
#> 1194 1 0.751
#> 1195 1 0.000
#> 1196 1 0.751
#> 1197 1 0.502
#> 1198 1 1.000
#> 1199 1 0.502
#> 1200 1 0.253
#> 1201 1 0.000
#> 1202 1 1.000
#> 1203 1 1.000
#> 1204 1 0.249
#> 1205 1 0.253
#> 1206 1 0.000
#> 1207 1 0.249
#> 1208 1 0.000
#> 1209 1 0.000
#> 1210 1 0.000
#> 1211 1 0.000
#> 1212 1 0.000
#> 1213 1 0.747
#> 1214 1 0.502
#> 1215 2 0.000
#> 1216 1 0.000
#> 1217 1 1.000
#> 1218 1 0.249
#> 1219 1 0.502
#> 1220 1 1.000
#> 1221 1 0.000
#> 1222 1 0.751
#> 1223 1 0.751
#> 1224 2 0.000
#> 1225 1 0.253
#> 1226 2 0.000
#> 1227 1 0.000
#> 1228 1 0.000
#> 1229 1 0.000
#> 1230 1 0.249
#> 1231 1 0.000
#> 1232 1 0.000
#> 1233 1 1.000
#> 1234 1 0.000
#> 1235 1 0.249
#> 1236 1 0.000
#> 1237 1 0.000
#> 1238 1 0.000
#> 1239 1 0.502
#> 1240 1 0.249
#> 1241 1 0.000
#> 1242 1 1.000
#> 1243 1 0.000
#> 1244 1 0.000
#> 1245 1 1.000
#> 1246 1 0.502
#> 1247 1 0.000
#> 1248 1 0.000
#> 1249 1 0.502
#> 1250 1 0.000
#> 1251 1 0.000
#> 1252 1 0.000
#> 1253 1 0.000
#> 1254 1 0.253
#> 1255 1 0.000
#> 1256 2 0.000
#> 1257 2 0.000
#> 1258 2 0.000
#> 1259 2 0.000
#> 1260 1 0.000
#> 1261 1 0.000
#> 1262 1 0.000
#> 1263 1 0.502
#> 1264 1 0.249
#> 1265 1 0.000
#> 1266 1 0.000
#> 1267 1 0.000
#> 1268 1 0.000
#> 1269 1 0.000
#> 1270 1 0.249
#> 1271 1 0.000
#> 1272 1 0.000
#> 1273 2 0.000
#> 1274 1 0.000
#> 1275 1 0.498
#> 1276 1 0.502
#> 1277 1 0.000
#> 1278 2 0.000
#> 1279 2 1.000
#> 1280 1 0.502
#> 1281 1 0.000
#> 1282 1 0.000
#> 1283 2 1.000
#> 1284 1 0.000
#> 1285 1 0.000
#> 1286 1 0.000
#> 1287 1 0.000
#> 1288 1 0.000
#> 1289 1 0.000
#> 1290 2 1.000
#> 1291 1 0.000
#> 1292 1 0.000
#> 1293 1 0.000
#> 1294 1 0.249
#> 1295 2 0.000
#> 1296 1 0.000
#> 1297 1 0.000
#> 1298 1 0.502
#> 1299 1 0.253
#> 1300 1 0.751
#> 1301 1 0.000
#> 1302 1 0.000
#> 1303 1 0.000
#> 1304 1 0.000
#> 1305 1 0.000
#> 1306 1 0.000
#> 1307 1 0.000
#> 1308 1 0.000
#> 1309 1 0.000
#> 1310 1 0.249
#> 1311 1 1.000
#> 1312 1 0.000
#> 1313 2 0.000
#> 1314 1 0.000
#> 1315 2 0.000
#> 1316 1 0.000
#> 1317 1 0.000
#> 1318 2 0.000
#> 1319 1 0.000
#> 1320 1 0.000
#> 1321 1 0.000
#> 1322 1 0.000
#> 1323 1 0.000
#> 1324 1 0.000
#> 1325 1 0.253
#> 1326 1 0.249
#> 1327 1 0.000
#> 1328 1 0.249
#> 1329 1 0.000
#> 1330 1 0.000
#> 1331 1 0.502
#> 1332 2 0.000
#> 1333 1 0.249
#> 1334 1 0.000
#> 1335 1 0.249
#> 1336 1 0.000
#> 1337 1 0.000
#> 1338 1 0.000
#> 1339 1 0.000
#> 1340 1 0.000
#> 1341 1 0.000
#> 1342 1 0.249
#> 1343 2 0.000
#> 1344 1 0.000
#> 1345 1 0.249
#> 1346 2 0.000
#> 1347 2 0.000
#> 1348 2 0.000
#> 1349 1 0.000
#> 1350 1 0.000
#> 1351 1 0.502
#> 1352 1 0.000
#> 1353 2 0.000
#> 1354 2 0.000
#> 1355 1 0.000
#> 1356 1 0.000
#> 1357 1 0.000
#> 1358 1 0.000
#> 1359 1 0.000
#> 1360 1 0.751
#> 1361 2 0.000
#> 1362 2 0.000
#> 1363 1 0.502
#> 1364 1 0.000
#> 1365 1 0.000
#> 1366 1 0.000
#> 1367 1 0.000
#> 1368 2 0.751
#> 1369 1 0.000
#> 1370 1 0.000
#> 1371 1 0.000
#> 1372 1 0.249
#> 1373 1 0.000
#> 1374 1 0.498
#> 1375 1 0.000
#> 1376 1 0.000
#> 1377 1 0.751
#> 1378 1 0.000
#> 1379 1 0.249
#> 1380 1 0.000
#> 1381 1 0.000
#> 1382 1 0.249
#> 1383 1 0.000
#> 1384 1 0.000
#> 1385 1 0.000
#> 1386 1 0.502
#> 1387 1 0.000
#> 1388 1 1.000
#> 1389 1 0.249
#> 1390 1 0.000
#> 1391 1 0.498
#> 1392 1 0.502
#> 1393 1 0.000
#> 1394 1 0.000
#> 1395 2 0.000
#> 1396 1 0.502
#> 1397 2 0.000
#> 1398 2 0.000
#> 1399 2 0.249
#> 1400 1 0.253
#> 1401 2 0.249
#> 1402 1 1.000
#> 1403 1 1.000
#> 1404 1 0.249
#> 1405 1 0.751
#> 1406 1 0.000
#> 1407 2 0.000
#> 1408 1 0.000
#> 1409 1 0.249
#> 1410 1 0.000
#> 1411 1 0.000
#> 1412 1 0.000
#> 1413 1 0.249
#> 1414 1 0.000
#> 1415 1 0.000
#> 1416 1 0.000
#> 1417 1 0.000
#> 1418 1 0.000
#> 1419 1 0.000
#> 1420 1 0.000
#> 1421 1 0.000
#> 1422 1 0.000
#> 1423 1 0.000
#> 1424 1 0.000
#> 1425 1 0.000
#> 1426 1 0.000
#> 1427 1 0.000
#> 1428 1 0.000
#> 1429 1 0.000
#> 1430 1 0.000
#> 1431 1 0.000
#> 1432 1 0.000
#> 1433 1 0.000
#> 1434 1 0.000
#> 1435 1 0.000
#> 1436 1 0.000
#> 1437 1 0.000
#> 1438 1 0.000
#> 1439 1 0.000
#> 1440 1 0.000
#> 1441 1 0.000
#> 1442 1 0.502
#> 1443 1 0.000
#> 1444 1 0.747
#> 1445 1 0.000
#> 1446 1 0.000
#> 1447 1 0.000
#> 1448 1 0.000
#> 1449 2 0.249
#> 1450 1 1.000
#> 1451 1 0.000
#> 1452 2 0.000
#> 1453 1 0.249
#> 1454 1 0.253
#> 1455 2 0.000
#> 1456 1 0.249
#> 1457 1 0.498
#> 1458 1 0.000
#> 1459 2 0.000
#> 1460 1 0.498
#> 1461 1 0.000
#> 1462 1 0.253
#> 1463 1 1.000
#> 1464 1 1.000
#> 1465 1 0.253
#> 1466 1 0.000
#> 1467 1 0.000
#> 1468 1 0.000
#> 1469 1 0.000
#> 1470 1 0.000
#> 1471 1 0.249
#> 1472 1 0.000
#> 1473 1 0.502
#> 1474 1 0.000
#> 1475 1 0.000
#> 1476 1 0.000
#> 1477 1 0.000
#> 1478 2 0.000
#> 1479 1 0.000
#> 1480 2 0.000
#> 1481 1 0.751
#> 1482 1 0.000
#> 1483 1 0.000
#> 1484 1 0.000
#> 1485 1 0.498
#> 1486 1 0.000
#> 1487 1 0.000
#> 1488 1 1.000
#> 1489 1 1.000
#> 1490 1 0.000
#> 1491 1 1.000
#> 1492 1 0.502
#> 1493 1 0.751
#> 1494 1 0.751
#> 1495 2 0.249
#> 1496 1 0.751
#> 1497 1 0.000
#> 1498 1 0.000
#> 1499 1 0.253
#> 1500 2 0.000
#> 1501 1 0.000
#> 1502 1 0.751
#> 1503 1 0.000
#> 1504 1 0.000
#> 1505 1 0.249
#> 1506 2 0.000
#> 1507 1 0.000
#> 1508 2 0.000
#> 1509 1 0.249
#> 1510 2 0.000
#> 1511 1 1.000
#> 1512 1 0.000
#> 1513 1 0.253
#> 1514 1 0.751
#> 1515 1 1.000
#> 1516 1 0.253
#> 1517 1 0.000
#> 1518 2 0.000
#> 1519 2 0.000
#> 1520 2 0.000
#> 1521 2 0.000
#> 1522 2 0.000
#> 1523 1 1.000
#> 1524 2 0.000
#> 1525 1 1.000
#> 1526 1 0.000
#> 1527 1 0.000
#> 1528 1 1.000
#> 1529 1 1.000
#> 1530 1 0.249
#> 1531 1 0.249
#> 1532 1 0.000
#> 1533 1 0.000
#> 1534 2 0.249
#> 1535 1 0.000
#> 1536 2 0.000
#> 1537 1 0.000
#> 1538 1 0.000
#> 1539 1 1.000
#> 1540 1 0.249
#> 1541 1 0.000
#> 1542 1 0.000
#> 1543 1 0.000
#> 1544 1 0.000
#> 1545 1 0.000
#> 1546 1 0.000
#> 1547 1 0.000
#> 1548 1 0.000
#> 1549 1 0.000
#> 1550 1 0.000
#> 1551 1 0.000
#> 1552 1 0.000
#> 1553 1 0.000
#> 1554 1 0.502
#> 1555 1 0.249
#> 1556 1 0.000
#> 1557 1 0.751
#> 1558 2 0.000
#> 1559 2 0.000
#> 1560 1 0.000
#> 1561 1 0.747
#> 1562 1 0.498
#> 1563 1 0.000
#> 1564 1 0.502
#> 1565 2 0.000
#> 1566 1 0.000
#> 1567 1 0.498
#> 1568 1 0.249
#> 1569 1 0.000
#> 1570 1 0.253
#> 1571 1 0.000
#> 1572 2 0.000
#> 1573 2 0.000
#> 1574 1 1.000
#> 1575 2 0.000
#> 1576 2 1.000
#> 1577 2 1.000
#> 1578 2 0.498
#> 1579 2 0.751
#> 1580 2 1.000
#> 1581 2 1.000
#> 1582 2 0.747
#> 1583 2 1.000
#> 1584 2 0.249
#> 1585 1 0.000
#> 1586 2 1.000
#> 1587 1 0.249
#> 1588 2 1.000
#> 1589 1 0.000
#> 1590 2 1.000
#> 1591 1 0.502
#> 1592 1 0.000
#> 1593 1 0.000
#> 1594 2 0.249
#> 1595 1 0.000
#> 1596 2 0.000
#> 1597 2 0.000
#> 1598 2 0.000
#> 1599 1 0.000
#> 1600 2 1.000
#> 1601 1 0.000
#> 1602 2 1.000
#> 1603 1 0.000
#> 1604 2 0.249
#> 1605 2 0.000
#> 1606 1 0.000
#> 1607 1 0.000
#> 1608 2 0.249
#> 1609 2 1.000
#> 1610 2 1.000
#> 1611 1 0.000
#> 1612 1 0.000
#> 1613 1 0.000
#> 1614 1 0.000
#> 1615 1 0.000
#> 1616 1 0.000
#> 1617 1 0.000
#> 1618 2 0.249
#> 1619 1 0.000
#> 1620 2 0.000
#> 1621 2 0.000
#> 1622 2 0.498
#> 1623 1 0.249
#> 1624 1 0.498
#> 1625 1 0.498
#> 1626 2 0.000
#> 1627 1 0.000
#> 1628 1 0.000
#> 1629 1 0.000
#> 1630 1 0.000
#> 1631 1 0.000
#> 1632 1 0.000
#> 1633 1 0.249
#> 1634 1 0.000
#> 1635 2 0.000
#> 1636 1 0.000
#> 1637 1 0.000
#> 1638 1 0.000
#> 1639 2 0.000
#> 1640 1 0.502
#> 1641 1 0.249
#> 1642 1 0.000
#> 1643 1 0.000
#> 1644 2 0.751
#> 1645 1 1.000
#> 1646 1 0.000
#> 1647 1 0.000
#> 1648 1 0.000
#> 1649 1 0.000
#> 1650 1 0.000
#> 1651 1 1.000
#> 1652 1 0.000
#> 1653 1 0.253
#> 1654 1 1.000
#> 1655 1 0.000
#> 1656 1 0.000
#> 1657 1 0.000
#> 1658 1 0.000
#> 1659 1 0.000
#> 1660 1 0.751
#> 1661 1 0.249
#> 1662 1 0.000
#> 1663 1 0.000
#> 1664 1 0.751
#> 1665 1 0.000
#> 1666 1 0.000
#> 1667 1 0.000
#> 1668 1 0.000
#> 1669 1 0.000
#> 1670 1 0.000
#> 1671 1 0.000
#> 1672 1 0.751
#> 1673 1 0.000
#> 1674 1 0.249
#> 1675 1 0.000
#> 1676 2 0.000
#> 1677 1 0.000
#> 1678 1 0.249
#> 1679 1 0.000
#> 1680 2 0.000
#> 1681 1 0.000
#> 1682 1 0.000
#> 1683 1 0.000
#> 1684 1 1.000
#> 1685 1 0.249
#> 1686 1 0.000
#> 1687 1 0.000
#> 1688 1 0.253
#> 1689 1 0.000
#> 1690 1 0.000
#> 1691 1 0.000
#> 1692 1 0.000
#> 1693 1 0.000
#> 1694 1 0.249
#> 1695 1 0.000
#> 1696 1 0.000
#> 1697 1 0.249
#> 1698 1 0.000
#> 1699 1 0.000
#> 1700 1 0.000
#> 1701 1 0.000
#> 1702 1 0.000
#> 1703 1 0.000
#> 1704 1 0.000
#> 1705 1 0.000
#> 1706 1 0.000
#> 1707 1 1.000
#> 1708 1 0.000
#> 1709 1 0.000
#> 1710 1 0.502
#> 1711 1 0.000
#> 1712 1 0.000
#> 1713 1 0.000
#> 1714 1 0.000
#> 1715 1 0.000
#> 1716 1 0.000
#> 1717 1 0.000
#> 1718 1 0.000
#> 1719 1 1.000
#> 1720 1 0.000
#> 1721 1 0.000
#> 1722 1 0.249
#> 1723 1 1.000
#> 1724 1 0.751
#> 1725 1 0.253
#> 1726 1 0.249
#> 1727 1 0.000
#> 1728 1 0.000
#> 1729 1 1.000
#> 1730 1 0.000
#> 1731 1 0.000
#> 1732 1 0.498
#> 1733 1 0.249
#> 1734 2 0.000
#> 1735 1 0.000
#> 1736 1 0.000
#> 1737 1 0.000
#> 1738 1 0.249
#> 1739 1 0.000
#> 1740 1 0.000
#> 1741 1 0.000
#> 1742 1 0.000
#> 1743 1 0.000
#> 1744 1 0.000
#> 1745 1 0.498
#> 1746 1 0.000
#> 1747 1 0.000
#> 1748 1 0.249
#> 1749 1 0.000
#> 1750 1 0.000
#> 1751 1 0.000
#> 1752 1 0.000
#> 1753 2 0.000
#> 1754 1 0.000
#> 1755 2 0.000
#> 1756 1 0.000
#> 1757 1 0.000
#> 1758 1 0.000
#> 1759 1 1.000
#> 1760 1 0.000
#> 1761 2 0.000
#> 1762 1 0.000
#> 1763 1 0.000
#> 1764 1 0.249
#> 1765 1 0.249
#> 1766 2 0.000
#> 1767 1 0.000
#> 1768 1 0.253
#> 1769 1 0.000
#> 1770 2 0.000
#> 1771 1 0.000
#> 1772 1 0.000
#> 1773 1 0.000
#> 1774 1 0.000
#> 1775 1 0.000
#> 1776 1 0.249
#> 1777 1 0.000
#> 1778 1 0.000
#> 1779 1 0.000
#> 1780 1 0.249
#> 1781 1 0.000
#> 1782 1 0.000
#> 1783 1 0.000
#> 1784 1 0.000
#> 1785 1 0.000
#> 1786 1 0.000
#> 1787 1 0.000
#> 1788 1 0.000
#> 1789 1 0.000
#> 1790 1 1.000
#> 1791 1 0.249
#> 1792 1 0.000
#> 1793 1 0.000
#> 1794 2 0.000
#> 1795 2 0.000
#> 1796 1 0.000
#> 1797 1 0.000
#> 1798 1 0.253
#> 1799 2 0.000
#> 1800 1 0.249
#> 1801 1 0.249
#> 1802 1 0.000
#> 1803 1 0.000
#> 1804 1 0.000
#> 1805 1 0.249
#> 1806 1 0.253
#> 1807 2 0.000
#> 1808 2 0.000
#> 1809 2 0.249
#> 1810 1 0.000
#> 1811 2 0.249
#> 1812 1 0.000
#> 1813 1 0.000
#> 1814 1 0.249
#> 1815 1 0.000
#> 1816 1 0.000
#> 1817 1 0.000
#> 1818 1 0.249
#> 1819 1 1.000
#> 1820 1 0.000
#> 1821 1 0.000
#> 1822 1 0.000
#> 1823 1 0.000
#> 1824 1 0.000
#> 1825 1 0.502
#> 1826 1 0.000
#> 1827 1 0.000
#> 1828 1 0.000
#> 1829 1 0.000
#> 1830 2 0.000
#> 1831 1 0.249
#> 1832 1 0.751
#> 1833 2 0.000
#> 1834 1 0.000
#> 1835 1 0.000
#> 1836 2 0.000
#> 1837 2 0.000
#> 1838 1 0.000
#> 1839 1 0.000
#> 1840 1 0.000
#> 1841 1 0.000
#> 1842 1 0.000
#> 1843 2 0.000
#> 1844 1 0.253
#> 1845 1 0.000
#> 1846 1 0.747
#> 1847 1 0.000
#> 1848 1 0.000
#> 1849 1 0.000
#> 1850 1 0.000
#> 1851 1 0.000
#> 1852 1 0.249
#> 1853 1 0.000
#> 1854 1 0.000
#> 1855 1 0.000
#> 1856 1 0.000
#> 1857 1 0.000
#> 1858 1 0.249
#> 1859 1 0.000
#> 1860 1 0.000
#> 1861 1 0.000
#> 1862 1 0.000
#> 1863 1 0.000
#> 1864 2 1.000
#> 1865 2 0.000
#> 1866 2 1.000
#> 1867 1 1.000
#> 1868 1 0.249
#> 1869 2 0.000
#> 1870 1 0.000
#> 1871 1 0.249
#> 1872 1 1.000
#> 1873 1 0.000
#> 1874 1 0.000
#> 1875 1 0.000
#> 1876 1 0.000
#> 1877 2 0.000
#> 1878 1 0.502
#> 1879 1 0.000
#> 1880 1 0.000
#> 1881 2 0.000
#> 1882 1 0.000
#> 1883 1 0.000
#> 1884 1 0.000
#> 1885 1 0.000
#> 1886 1 0.000
#> 1887 1 0.000
#> 1888 1 0.000
#> 1889 1 0.000
#> 1890 1 0.000
#> 1891 1 0.000
#> 1892 2 0.000
#> 1893 1 0.000
#> 1894 1 0.000
#> 1895 1 0.000
#> 1896 1 0.000
#> 1897 1 0.000
#> 1898 1 0.000
#> 1899 1 0.000
#> 1900 1 0.000
#> 1901 1 0.249
#> 1902 1 0.000
#> 1903 1 0.000
#> 1904 1 0.000
#> 1905 1 0.000
#> 1906 1 0.000
#> 1907 2 1.000
#> 1908 1 0.000
#> 1909 1 0.000
#> 1910 1 0.249
#> 1911 1 0.000
#> 1912 1 0.000
#> 1913 1 0.249
#> 1914 1 0.000
#> 1915 1 0.498
#> 1916 1 0.000
#> 1917 1 0.000
#> 1918 1 0.000
#> 1919 1 0.000
#> 1920 2 1.000
#> 1921 2 0.000
#> 1922 1 0.000
#> 1923 1 0.249
#> 1924 1 0.249
#> 1925 1 0.000
#> 1926 1 0.000
#> 1927 1 0.000
#> 1928 1 0.000
#> 1929 1 0.000
#> 1930 2 0.000
#> 1931 1 0.000
#> 1932 1 0.000
#> 1933 2 0.000
#> 1934 1 0.000
#> 1935 1 0.000
#> 1936 2 0.751
#> 1937 1 0.000
#> 1938 1 0.000
#> 1939 1 0.000
#> 1940 1 0.249
#> 1941 1 0.000
#> 1942 1 0.000
#> 1943 1 0.000
#> 1944 1 0.000
#> 1945 2 1.000
#> 1946 2 0.502
#> 1947 2 1.000
#> 1948 1 0.000
#> 1949 1 0.000
#> 1950 1 0.000
#> 1951 1 0.000
#> 1952 1 0.000
#> 1953 1 0.751
#> 1954 2 0.000
#> 1955 1 0.000
#> 1956 1 0.000
#> 1957 2 0.000
#> 1958 1 0.000
#> 1959 2 0.249
#> 1960 1 0.249
#> 1961 2 0.000
#> 1962 1 0.000
#> 1963 1 0.000
#> 1964 2 0.000
#> 1965 1 0.000
#> 1966 1 0.000
#> 1967 2 0.000
#> 1968 1 0.249
#> 1969 1 0.502
#> 1970 1 0.000
#> 1971 1 0.000
#> 1972 2 0.000
#> 1973 1 0.000
#> 1974 1 0.000
#> 1975 2 0.000
#> 1976 2 0.498
#> 1977 1 0.249
#> 1978 2 0.000
#> 1979 1 0.249
#> 1980 2 0.000
#> 1981 1 0.000
#> 1982 1 0.000
#> 1983 2 1.000
#> 1984 2 0.000
#> 1985 2 0.000
#> 1986 2 0.249
#> 1987 2 0.000
#> 1988 2 0.000
#> 1989 2 0.000
#> 1990 2 0.000
#> 1991 2 0.000
#> 1992 2 0.000
#> 1993 2 0.000
#> 1994 2 0.000
#> 1995 2 0.249
#> 1996 2 0.000
#> 1997 2 0.000
#> 1998 2 0.000
#> 1999 2 0.000
#> 2000 2 0.000
#> 2001 2 0.000
#> 2002 2 0.000
#> 2003 2 0.751
#> 2004 2 0.000
#> 2005 2 0.000
#> 2006 2 0.000
#> 2007 2 0.000
#> 2008 2 0.000
#> 2009 2 0.000
#> 2010 2 0.249
#> 2011 2 0.000
#> 2012 2 0.000
#> 2013 2 0.000
#> 2014 2 0.000
#> 2015 1 0.751
#> 2016 2 0.000
#> 2017 2 0.000
#> 2018 2 0.000
#> 2019 2 0.000
#> 2020 2 0.000
#> 2021 2 0.000
#> 2022 2 0.000
#> 2023 2 0.000
#> 2024 2 0.000
#> 2025 2 0.000
#> 2026 1 1.000
#> 2027 2 0.000
#> 2028 2 0.000
#> 2029 2 0.000
#> 2030 2 0.000
#> 2031 2 0.000
#> 2032 2 0.000
#> 2033 2 0.253
#> 2034 2 0.000
#> 2035 2 0.000
#> 2036 2 0.000
#> 2037 2 0.249
#> 2038 2 0.000
#> 2039 2 0.000
#> 2040 2 0.000
#> 2041 2 0.000
#> 2042 1 1.000
#> 2043 2 0.000
#> 2044 2 0.000
#> 2045 2 0.000
#> 2046 2 0.000
#> 2047 2 0.000
#> 2048 2 0.000
#> 2049 2 0.000
#> 2050 1 1.000
#> 2051 2 0.000
#> 2052 2 1.000
#> 2053 2 0.000
#> 2054 2 0.249
#> 2055 2 0.000
#> 2056 2 0.000
#> 2057 2 0.000
#> 2058 2 0.000
#> 2059 1 1.000
#> 2060 2 0.000
#> 2061 2 0.249
#> 2062 2 0.000
#> 2063 2 0.000
#> 2064 2 0.000
#> 2065 2 0.000
#> 2066 2 0.000
#> 2067 2 0.249
#> 2068 2 0.000
#> 2069 2 0.000
#> 2070 2 0.000
#> 2071 2 0.000
#> 2072 2 0.000
#> 2073 2 0.000
#> 2074 2 0.000
#> 2075 2 0.000
#> 2076 2 0.000
#> 2077 2 0.000
#> 2078 2 0.000
#> 2079 2 0.000
#> 2080 2 1.000
#> 2081 2 0.000
#> 2082 2 0.000
#> 2083 2 0.000
#> 2084 2 0.000
#> 2085 2 0.000
#> 2086 2 0.000
#> 2087 2 0.000
#> 2088 2 0.000
#> 2089 2 0.000
#> 2090 2 0.000
#> 2091 2 0.000
#> 2092 2 0.000
#> 2093 2 0.000
#> 2094 2 0.000
#> 2095 2 0.000
#> 2096 2 0.000
#> 2097 2 0.000
#> 2098 2 0.000
#> 2099 2 0.000
#> 2100 2 0.249
#> 2101 2 0.000
#> 2102 2 0.000
#> 2103 2 0.000
#> 2104 2 0.000
#> 2105 2 0.000
#> 2106 2 0.000
#> 2107 2 0.000
#> 2108 2 0.000
#> 2109 2 0.000
#> 2110 2 0.000
#> 2111 2 0.000
#> 2112 2 0.000
#> 2113 2 0.000
#> 2114 2 0.000
#> 2115 2 0.000
#> 2116 2 0.000
#> 2117 2 0.000
#> 2118 2 0.000
#> 2119 2 0.000
#> 2120 2 0.000
#> 2121 2 0.000
#> 2122 2 0.000
#> 2123 2 0.000
#> 2124 2 0.000
#> 2125 1 1.000
#> 2126 2 0.000
#> 2127 2 0.000
#> 2128 2 0.249
#> 2129 2 0.000
#> 2130 2 0.000
#> 2131 2 0.000
#> 2132 2 0.000
#> 2133 2 0.000
#> 2134 2 0.000
#> 2135 2 0.498
#> 2136 2 0.000
#> 2137 2 0.000
#> 2138 2 0.249
#> 2139 2 0.000
#> 2140 2 0.000
#> 2141 2 0.000
#> 2142 2 0.000
#> 2143 2 0.000
#> 2144 2 0.000
#> 2145 2 0.000
#> 2146 2 0.000
#> 2147 2 0.000
#> 2148 2 0.000
#> 2149 2 0.000
#> 2150 2 0.000
#> 2151 2 0.000
#> 2152 2 0.000
#> 2153 2 0.000
#> 2154 2 0.000
#> 2155 2 0.000
#> 2156 2 0.249
#> 2157 2 0.000
#> 2158 2 0.000
#> 2159 2 0.000
#> 2160 2 0.000
#> 2161 2 0.000
#> 2162 2 0.000
#> 2163 2 0.000
#> 2164 2 0.000
#> 2165 2 0.000
#> 2166 2 0.000
#> 2167 2 0.000
#> 2168 2 0.000
#> 2169 2 0.000
#> 2170 2 0.000
#> 2171 2 0.000
#> 2172 2 0.000
#> 2173 2 0.000
#> 2174 1 1.000
#> 2175 2 0.000
#> 2176 2 0.000
#> 2177 2 0.000
#> 2178 2 0.000
#> 2179 2 0.000
#> 2180 2 0.000
#> 2181 2 0.000
#> 2182 2 0.000
#> 2183 2 0.249
#> 2184 2 0.000
#> 2185 2 0.000
#> 2186 2 0.000
#> 2187 2 0.000
#> 2188 2 0.000
#> 2189 2 0.000
#> 2190 1 1.000
#> 2191 2 0.000
#> 2192 1 1.000
#> 2193 2 0.000
#> 2194 1 0.000
#> 2195 2 0.000
#> 2196 2 0.000
#> 2197 2 0.000
#> 2198 2 0.249
#> 2199 2 0.000
#> 2200 2 0.000
#> 2201 2 0.000
#> 2202 2 0.000
#> 2203 2 0.000
#> 2204 2 0.000
#> 2205 2 0.000
#> 2206 2 0.000
#> 2207 2 0.000
#> 2208 2 0.000
#> 2209 2 0.000
#> 2210 2 0.000
#> 2211 2 0.000
#> 2212 2 0.000
#> 2213 2 0.000
#> 2214 2 0.000
#> 2215 2 0.000
#> 2216 2 0.000
#> 2217 2 0.000
#> 2218 2 0.000
#> 2219 2 0.000
#> 2220 2 0.000
#> 2221 2 0.000
#> 2222 2 0.000
#> 2223 2 0.000
#> 2224 2 0.000
#> 2225 2 0.000
#> 2226 2 0.000
#> 2227 2 0.000
#> 2228 2 0.000
#> 2229 2 0.000
#> 2230 2 0.000
#> 2231 2 0.000
#> 2232 2 0.000
#> 2233 2 0.000
#> 2234 2 0.000
#> 2235 2 0.000
#> 2236 2 0.000
#> 2237 1 1.000
#> 2238 2 0.000
#> 2239 2 0.000
#> 2240 2 0.000
#> 2241 2 0.000
#> 2242 1 1.000
#> 2243 2 0.000
#> 2244 2 0.000
#> 2245 2 0.000
#> 2246 2 0.000
#> 2247 2 0.000
#> 2248 2 0.000
#> 2249 2 0.000
#> 2250 2 0.000
#> 2251 2 0.000
#> 2252 2 0.000
#> 2253 2 0.000
#> 2254 2 0.000
#> 2255 2 0.000
#> 2256 2 0.000
#> 2257 2 0.000
#> 2258 2 0.000
#> 2259 1 1.000
#> 2260 1 1.000
#> 2261 2 0.000
#> 2262 2 0.000
#> 2263 2 0.000
#> 2264 2 0.000
#> 2265 2 0.000
#> 2266 2 0.000
#> 2267 2 0.000
#> 2268 2 0.000
#> 2269 2 0.000
#> 2270 2 0.000
#> 2271 2 0.000
#> 2272 2 0.000
#> 2273 1 1.000
#> 2274 2 0.000
#> 2275 1 0.751
#> 2276 2 0.000
#> 2277 2 0.000
#> 2278 1 1.000
#> 2279 2 0.000
#> 2280 2 0.000
#> 2281 2 0.000
#> 2282 2 0.000
#> 2283 1 1.000
#> 2284 2 0.000
#> 2285 2 0.000
#> 2286 2 0.502
#> 2287 1 1.000
#> 2288 2 0.000
#> 2289 1 1.000
#> 2290 2 0.000
#> 2291 2 0.000
#> 2292 1 1.000
#> 2293 2 0.000
#> 2294 2 0.000
#> 2295 1 1.000
#> 2296 2 0.000
#> 2297 2 0.000
#> 2298 2 0.000
#> 2299 1 1.000
#> 2300 2 0.000
#> 2301 1 0.502
#> 2302 1 1.000
#> 2303 1 1.000
#> 2304 2 0.000
#> 2305 1 0.000
#> 2306 2 0.000
#> 2307 1 1.000
#> 2308 1 0.751
#> 2309 1 1.000
#> 2310 2 0.000
#> 2311 1 0.751
#> 2312 2 0.000
#> 2313 2 0.249
#> 2314 1 1.000
#> 2315 1 1.000
#> 2316 1 0.253
#> 2317 1 1.000
#> 2318 2 0.249
#> 2319 2 0.000
#> 2320 2 0.000
#> 2321 1 1.000
#> 2322 2 0.000
#> 2323 2 0.000
#> 2324 2 0.000
#> 2325 2 0.000
#> 2326 2 0.000
#> 2327 2 0.000
#> 2328 2 0.000
#> 2329 2 0.000
#> 2330 2 0.000
#> 2331 2 0.000
#> 2332 2 0.000
#> 2333 2 0.000
#> 2334 2 0.000
#> 2335 2 0.000
#> 2336 1 0.751
#> 2337 1 1.000
#> 2338 1 1.000
#> 2339 2 0.000
#> 2340 1 1.000
#> 2341 1 1.000
#> 2342 1 1.000
#> 2343 2 0.000
#> 2344 2 0.000
#> 2345 2 0.000
#> 2346 2 0.000
#> 2347 2 0.000
#> 2348 2 0.000
#> 2349 2 0.000
#> 2350 2 0.000
#> 2351 2 1.000
#> 2352 2 0.000
#> 2353 2 0.000
#> 2354 2 0.000
#> 2355 2 0.000
#> 2356 2 0.000
#> 2357 1 1.000
#> 2358 2 0.000
#> 2359 2 0.000
#> 2360 2 0.000
#> 2361 2 0.000
#> 2362 2 0.000
#> 2363 2 0.000
#> 2364 2 0.000
#> 2365 2 0.000
#> 2366 2 0.000
#> 2367 2 0.000
#> 2368 2 0.000
#> 2369 2 0.000
#> 2370 2 0.000
#> 2371 2 0.000
#> 2372 2 0.000
#> 2373 2 0.000
#> 2374 2 0.000
#> 2375 2 0.000
#> 2376 2 0.000
#> 2377 2 0.000
#> 2378 2 0.000
#> 2379 2 0.000
#> 2380 2 0.000
#> 2381 2 0.000
#> 2382 2 0.000
#> 2383 2 0.000
#> 2384 2 0.000
#> 2385 2 0.000
#> 2386 2 0.000
#> 2387 2 0.000
#> 2388 2 0.000
#> 2389 2 0.000
#> 2390 2 0.000
#> 2391 2 0.000
#> 2392 2 0.000
#> 2393 2 0.000
#> 2394 2 0.000
#> 2395 2 0.000
#> 2396 2 0.000
#> 2397 2 0.000
#> 2398 2 0.498
#> 2399 1 0.747
#> 2400 2 0.000
#> 2401 1 1.000
#> 2402 1 1.000
#> 2403 1 1.000
#> 2404 2 0.000
#> 2405 2 0.000
#> 2406 2 0.000
#> 2407 2 0.000
#> 2408 2 0.000
#> 2409 2 0.000
#> 2410 2 0.000
#> 2411 2 0.000
#> 2412 2 0.000
#> 2413 2 0.000
#> 2414 1 1.000
#> 2415 2 0.000
#> 2416 2 0.000
#> 2417 2 0.000
#> 2418 2 0.000
#> 2419 2 0.000
#> 2420 1 0.253
#> 2421 2 0.000
#> 2422 2 0.000
#> 2423 1 1.000
#> 2424 2 0.000
#> 2425 2 0.000
#> 2426 2 0.000
#> 2427 2 0.000
#> 2428 2 0.000
#> 2429 2 0.000
#> 2430 2 0.000
#> 2431 2 0.000
#> 2432 2 0.000
#> 2433 1 1.000
#> 2434 2 0.000
#> 2435 1 1.000
#> 2436 2 0.000
#> 2437 1 1.000
#> 2438 1 1.000
#> 2439 1 1.000
#> 2440 1 1.000
#> 2441 1 1.000
#> 2442 1 1.000
#> 2443 1 1.000
#> 2444 1 1.000
#> 2445 1 1.000
#> 2446 2 0.000
#> 2447 2 0.000
#> 2448 2 0.000
#> 2449 2 0.000
#> 2450 2 0.000
#> 2451 2 0.000
#> 2452 2 0.000
#> 2453 2 0.000
#> 2454 2 0.747
#> 2455 2 0.000
#> 2456 2 0.000
#> 2457 2 0.000
#> 2458 2 0.000
#> 2459 2 0.000
#> 2460 2 0.000
#> 2461 2 0.000
#> 2462 2 0.000
#> 2463 2 0.000
#> 2464 2 0.000
#> 2465 2 0.000
#> 2466 2 0.000
#> 2467 2 0.000
#> 2468 2 0.000
#> 2469 2 0.000
#> 2470 2 0.000
#> 2471 1 1.000
#> 2472 2 0.000
#> 2473 2 0.000
#> 2474 2 0.000
#> 2475 2 0.000
#> 2476 2 0.502
#> 2477 2 0.000
#> 2478 2 0.498
#> 2479 2 0.000
#> 2480 2 0.000
#> 2481 2 0.751
#> 2482 2 0.000
#> 2483 2 0.000
#> 2484 2 0.000
#> 2485 2 0.253
#> 2486 2 0.000
#> 2487 2 0.000
#> 2488 2 0.000
#> 2489 2 0.000
#> 2490 2 0.000
#> 2491 2 0.249
#> 2492 2 0.000
#> 2493 2 0.000
#> 2494 2 0.000
#> 2495 2 0.000
#> 2496 2 0.000
#> 2497 2 0.000
#> 2498 2 0.000
#> 2499 2 0.000
#> 2500 2 0.000
#> 2501 2 0.000
#> 2502 2 0.000
#> 2503 2 0.000
#> 2504 2 0.000
#> 2505 2 0.000
#> 2506 2 0.000
#> 2507 2 0.000
#> 2508 2 0.000
#> 2509 2 0.000
#> 2510 2 0.000
#> 2511 2 0.000
#> 2512 2 0.000
#> 2513 2 0.000
#> 2514 2 0.000
#> 2515 2 0.000
#> 2516 2 0.000
#> 2517 2 0.000
#> 2518 2 0.000
#> 2519 2 0.000
#> 2520 2 0.000
#> 2521 2 0.000
#> 2522 2 0.000
#> 2523 2 0.000
#> 2524 2 0.249
#> 2525 2 0.000
#> 2526 2 0.000
#> 2527 2 0.000
#> 2528 2 0.000
#> 2529 2 0.000
#> 2530 2 0.000
#> 2531 2 0.000
#> 2532 2 0.000
#> 2533 2 0.502
#> 2534 1 1.000
#> 2535 2 0.000
#> 2536 2 0.000
#> 2537 2 0.000
#> 2538 2 0.000
#> 2539 2 0.000
#> 2540 2 0.000
#> 2541 2 0.000
#> 2542 2 0.000
#> 2543 2 0.000
#> 2544 2 0.000
#> 2545 2 0.000
#> 2546 2 0.000
#> 2547 2 0.000
#> 2548 2 0.000
#> 2549 1 1.000
#> 2550 2 0.000
#> 2551 1 1.000
#> 2552 1 1.000
#> 2553 1 0.502
#> 2554 1 1.000
#> 2555 2 0.000
#> 2556 1 1.000
#> 2557 2 0.000
#> 2558 1 1.000
#> 2559 2 0.000
#> 2560 2 0.000
#> 2561 2 0.000
#> 2562 2 0.000
#> 2563 1 1.000
#> 2564 2 0.000
#> 2565 2 0.000
#> 2566 2 0.000
#> 2567 2 0.000
#> 2568 2 0.000
#> 2569 2 0.000
#> 2570 2 0.000
#> 2571 2 0.000
#> 2572 1 0.751
#> 2573 1 1.000
#> 2574 1 1.000
#> 2575 1 1.000
#> 2576 1 1.000
#> 2577 2 0.000
#> 2578 2 0.000
#> 2579 2 0.000
#> 2580 2 0.000
#> 2581 2 0.000
#> 2582 2 0.000
#> 2583 2 0.000
#> 2584 2 0.000
#> 2585 2 0.000
#> 2586 2 0.000
#> 2587 2 0.000
#> 2588 1 1.000
#> 2589 2 0.000
#> 2590 2 0.000
#> 2591 2 0.000
#> 2592 2 0.000
#> 2593 2 1.000
#> 2594 2 0.000
#> 2595 2 0.000
#> 2596 2 0.000
#> 2597 2 0.000
#> 2598 2 0.000
#> 2599 2 0.000
#> 2600 2 0.000
#> 2601 2 0.000
#> 2602 2 0.000
#> 2603 2 0.000
#> 2604 2 0.249
#> 2605 2 0.000
#> 2606 2 0.000
#> 2607 2 0.000
#> 2608 2 0.000
#> 2609 2 0.747
#> 2610 1 1.000
#> 2611 2 0.000
#> 2612 2 0.000
#> 2613 2 0.000
#> 2614 2 0.000
#> 2615 2 0.000
#> 2616 1 1.000
#> 2617 2 0.000
#> 2618 2 0.000
#> 2619 2 0.000
#> 2620 2 0.000
#> 2621 2 0.249
#> 2622 2 0.000
#> 2623 1 0.502
#> 2624 2 0.000
#> 2625 2 0.000
#> 2626 1 1.000
#> 2627 1 1.000
#> 2628 1 0.751
#> 2629 1 1.000
#> 2630 2 0.249
#> 2631 2 0.000
#> 2632 2 0.000
#> 2633 2 0.000
#> 2634 2 0.000
#> 2635 2 1.000
#> 2636 2 0.000
#> 2637 2 0.000
#> 2638 2 0.000
#> 2639 2 0.000
#> 2640 2 0.000
#> 2641 2 0.000
#> 2642 1 1.000
#> 2643 2 0.000
#> 2644 2 0.000
#> 2645 2 0.000
#> 2646 2 0.000
#> 2647 2 0.000
#> 2648 2 0.000
#> 2649 2 0.000
#> 2650 2 0.000
#> 2651 1 0.000
#> 2652 2 0.000
#> 2653 2 0.000
#> 2654 2 0.000
#> 2655 2 0.000
#> 2656 2 0.000
#> 2657 2 0.000
#> 2658 2 0.000
#> 2659 2 0.000
#> 2660 2 0.000
#> 2661 2 0.000
#> 2662 2 0.000
#> 2663 2 0.000
#> 2664 1 1.000
#> 2665 1 1.000
#> 2666 2 0.000
#> 2667 2 0.000
#> 2668 2 0.000
#> 2669 2 0.000
#> 2670 1 1.000
#> 2671 2 0.000
#> 2672 1 1.000
#> 2673 1 1.000
#> 2674 2 0.000
#> 2675 2 0.000
#> 2676 2 0.000
#> 2677 2 0.000
#> 2678 1 1.000
#> 2679 1 1.000
#> 2680 1 1.000
#> 2681 1 1.000
#> 2682 2 0.000
#> 2683 1 1.000
#> 2684 1 1.000
#> 2685 1 0.000
#> 2686 2 0.000
#> 2687 2 0.000
#> 2688 2 0.000
#> 2689 2 0.000
#> 2690 2 0.000
#> 2691 2 0.000
#> 2692 2 0.000
#> 2693 2 0.000
#> 2694 2 0.000
#> 2695 1 0.751
#> 2696 2 0.000
#> 2697 2 0.000
#> 2698 2 0.000
#> 2699 2 0.000
#> 2700 1 1.000
#> 2701 2 0.000
#> 2702 2 0.000
#> 2703 1 1.000
#> 2704 2 0.000
#> 2705 2 0.000
#> 2706 2 0.000
#> 2707 1 1.000
#> 2708 2 0.000
#> 2709 1 1.000
#> 2710 1 0.751
#> 2711 2 0.000
#> 2712 1 1.000
#> 2713 1 1.000
#> 2714 2 0.000
#> 2715 2 0.000
#> 2716 2 0.000
#> 2717 1 1.000
#> 2718 1 0.751
#> 2719 2 0.000
#> 2720 1 1.000
#> 2721 2 0.000
#> 2722 2 0.000
#> 2723 1 1.000
#> 2724 2 0.000
#> 2725 1 1.000
#> 2726 1 1.000
#> 2727 1 1.000
#> 2728 1 1.000
#> 2729 1 1.000
#> 2730 2 0.000
#> 2731 2 0.000
#> 2732 2 0.000
#> 2733 1 1.000
#> 2734 2 0.249
#> 2735 1 1.000
#> 2736 1 0.751
#> 2737 1 1.000
#> 2738 1 1.000
#> 2739 1 0.751
#> 2740 1 1.000
#> 2741 1 1.000
#> 2742 1 1.000
#> 2743 2 0.000
#> 2744 2 0.000
#> 2745 2 0.000
#> 2746 1 1.000
#> 2747 1 1.000
#> 2748 1 1.000
#> 2749 1 1.000
#> 2750 1 1.000
#> 2751 1 1.000
#> 2752 1 1.000
#> 2753 2 0.000
#> 2754 2 0.000
#> 2755 1 1.000
#> 2756 2 1.000
#> 2757 1 0.253
#> 2758 2 1.000
#> 2759 2 0.249
#> 2760 2 0.249
#> 2761 2 1.000
#> 2762 2 0.249
#> 2763 2 0.751
#> 2764 2 1.000
#> 2765 2 0.000
#> 2766 1 1.000
#> 2767 1 1.000
#> 2768 2 0.000
#> 2769 2 0.000
#> 2770 2 0.000
#> 2771 2 0.000
#> 2772 2 0.000
#> 2773 2 0.249
#> 2774 2 0.000
#> 2775 2 0.000
#> 2776 2 0.249
#> 2777 2 0.000
#> 2778 2 0.000
#> 2779 2 0.000
#> 2780 2 0.000
#> 2781 2 0.000
#> 2782 2 0.000
#> 2783 2 0.000
#> 2784 2 0.000
#> 2785 2 0.249
#> 2786 2 0.000
#> 2787 2 0.000
#> 2788 2 0.000
#> 2789 2 0.000
#> 2790 1 0.751
#> 2791 1 0.751
#> 2792 2 0.000
#> 2793 2 0.000
#> 2794 2 0.000
#> 2795 2 0.000
#> 2796 2 0.000
#> 2797 2 0.000
#> 2798 2 0.000
#> 2799 2 0.249
#> 2800 2 0.000
#> 2801 2 0.000
#> 2802 2 0.000
#> 2803 2 0.000
#> 2804 2 0.000
#> 2805 2 0.249
#> 2806 2 0.000
#> 2807 2 0.000
#> 2808 2 0.000
#> 2809 2 0.502
#> 2810 2 0.000
#> 2811 2 0.000
#> 2812 2 0.249
#> 2813 2 0.000
#> 2814 2 0.000
#> 2815 2 0.000
#> 2816 2 0.000
#> 2817 2 0.000
#> 2818 1 0.751
#> 2819 1 1.000
#> 2820 1 1.000
#> 2821 2 0.000
#> 2822 2 0.000
#> 2823 2 0.000
#> 2824 2 0.000
#> 2825 2 0.249
#> 2826 2 0.249
#> 2827 2 0.000
#> 2828 2 0.000
#> 2829 2 0.000
#> 2830 2 0.000
#> 2831 2 0.000
#> 2832 2 0.000
#> 2833 2 0.000
#> 2834 2 0.000
#> 2835 2 0.000
#> 2836 2 0.000
#> 2837 2 0.000
#> 2838 2 0.000
#> 2839 2 0.000
#> 2840 2 0.000
#> 2841 2 0.000
#> 2842 2 0.000
#> 2843 2 0.249
#> 2844 2 0.000
#> 2845 2 0.000
#> 2846 2 0.249
#> 2847 2 0.000
#> 2848 2 0.000
#> 2849 2 0.000
#> 2850 2 0.000
#> 2851 2 0.498
#> 2852 2 0.000
#> 2853 1 1.000
#> 2854 2 0.000
#> 2855 1 0.751
#> 2856 2 0.000
#> 2857 2 0.000
#> 2858 1 1.000
#> 2859 2 0.000
#> 2860 2 0.000
#> 2861 2 0.000
#> 2862 2 0.000
#> 2863 2 0.000
#> 2864 2 0.000
#> 2865 2 0.000
#> 2866 2 0.000
#> 2867 2 0.000
#> 2868 2 0.000
#> 2869 2 0.000
#> 2870 2 0.000
#> 2871 2 0.000
#> 2872 2 0.000
#> 2873 2 0.000
#> 2874 2 0.000
#> 2875 2 0.000
#> 2876 2 0.000
#> 2877 2 0.000
#> 2878 2 0.000
#> 2879 1 1.000
#> 2880 2 0.000
#> 2881 2 0.000
get_classes(res, k = 3)
#> class p
#> 1 1 0.249
#> 2 1 0.249
#> 3 2 0.000
#> 4 3 1.000
#> 5 1 0.747
#> 6 1 0.000
#> 7 3 0.000
#> 8 1 1.000
#> 9 1 1.000
#> 10 1 0.253
#> 11 1 1.000
#> 12 1 1.000
#> 13 3 1.000
#> 14 1 0.747
#> 15 1 1.000
#> 16 1 1.000
#> 17 1 1.000
#> 18 1 1.000
#> 19 3 0.000
#> 20 1 1.000
#> 21 3 1.000
#> 22 1 0.000
#> 23 1 1.000
#> 24 3 0.000
#> 25 1 1.000
#> 26 3 1.000
#> 27 3 0.000
#> 28 3 0.000
#> 29 1 0.000
#> 30 3 0.000
#> 31 3 0.000
#> 32 1 1.000
#> 33 3 0.000
#> 34 3 1.000
#> 35 3 0.498
#> 36 1 1.000
#> 37 1 1.000
#> 38 1 1.000
#> 39 3 0.000
#> 40 3 0.000
#> 41 1 0.498
#> 42 2 0.000
#> 43 1 1.000
#> 44 3 0.000
#> 45 1 1.000
#> 46 1 1.000
#> 47 3 0.000
#> 48 3 0.000
#> 49 3 0.000
#> 50 1 0.249
#> 51 3 0.000
#> 52 1 1.000
#> 53 1 1.000
#> 54 1 1.000
#> 55 1 0.000
#> 56 2 1.000
#> 57 2 1.000
#> 58 1 0.000
#> 59 1 1.000
#> 60 3 0.000
#> 61 3 0.000
#> 62 1 0.502
#> 63 1 1.000
#> 64 2 1.000
#> 65 3 0.000
#> 66 1 1.000
#> 67 1 0.000
#> 68 1 0.000
#> 69 3 0.000
#> 70 1 0.498
#> 71 3 1.000
#> 72 3 1.000
#> 73 3 0.000
#> 74 1 1.000
#> 75 1 1.000
#> 76 1 0.000
#> 77 1 0.000
#> 78 1 1.000
#> 79 2 1.000
#> 80 1 1.000
#> 81 1 0.000
#> 82 3 1.000
#> 83 1 1.000
#> 84 1 0.249
#> 85 1 0.000
#> 86 2 1.000
#> 87 1 1.000
#> 88 1 1.000
#> 89 1 1.000
#> 90 1 0.000
#> 91 3 0.000
#> 92 1 0.000
#> 93 2 0.000
#> 94 1 0.000
#> 95 1 1.000
#> 96 1 1.000
#> 97 1 1.000
#> 98 1 0.000
#> 99 1 0.747
#> 100 3 1.000
#> 101 1 1.000
#> 102 1 1.000
#> 103 1 0.000
#> 104 1 1.000
#> 105 1 0.000
#> 106 3 1.000
#> 107 2 0.000
#> 108 1 0.000
#> 109 1 0.000
#> 110 1 0.000
#> 111 1 1.000
#> 112 1 1.000
#> 113 1 0.751
#> 114 1 0.000
#> 115 1 0.000
#> 116 2 0.000
#> 117 2 1.000
#> 118 3 1.000
#> 119 1 0.000
#> 120 3 0.498
#> 121 3 1.000
#> 122 3 0.000
#> 123 2 0.253
#> 124 2 0.751
#> 125 2 1.000
#> 126 2 0.000
#> 127 1 0.000
#> 128 1 0.000
#> 129 1 0.000
#> 130 1 0.000
#> 131 1 0.000
#> 132 1 0.000
#> 133 1 0.502
#> 134 3 1.000
#> 135 1 0.000
#> 136 2 0.498
#> 137 1 1.000
#> 138 1 1.000
#> 139 3 1.000
#> 140 1 0.000
#> 141 1 0.000
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 2 0.000
#> 147 1 1.000
#> 148 2 1.000
#> 149 1 1.000
#> 150 3 1.000
#> 151 3 0.249
#> 152 3 0.000
#> 153 1 1.000
#> 154 3 0.249
#> 155 3 0.000
#> 156 1 1.000
#> 157 3 0.000
#> 158 2 1.000
#> 159 3 0.000
#> 160 3 0.751
#> 161 3 1.000
#> 162 1 1.000
#> 163 3 0.000
#> 164 3 1.000
#> 165 2 1.000
#> 166 1 1.000
#> 167 3 0.000
#> 168 3 0.000
#> 169 1 1.000
#> 170 1 1.000
#> 171 3 0.000
#> 172 3 0.000
#> 173 3 0.000
#> 174 2 1.000
#> 175 2 0.502
#> 176 3 0.000
#> 177 3 0.000
#> 178 1 1.000
#> 179 3 0.000
#> 180 3 0.000
#> 181 3 0.000
#> 182 3 0.000
#> 183 3 1.000
#> 184 3 0.000
#> 185 3 1.000
#> 186 3 0.000
#> 187 3 1.000
#> 188 3 0.000
#> 189 3 0.000
#> 190 2 1.000
#> 191 3 1.000
#> 192 1 1.000
#> 193 3 0.000
#> 194 3 0.000
#> 195 3 0.000
#> 196 3 1.000
#> 197 3 0.000
#> 198 3 0.000
#> 199 3 0.000
#> 200 3 0.000
#> 201 3 0.000
#> 202 3 1.000
#> 203 3 0.000
#> 204 3 0.000
#> 205 3 0.000
#> 206 3 0.000
#> 207 3 1.000
#> 208 3 0.000
#> 209 3 0.253
#> 210 2 1.000
#> 211 3 0.000
#> 212 2 0.249
#> 213 3 0.000
#> 214 3 0.000
#> 215 3 0.000
#> 216 3 0.000
#> 217 3 1.000
#> 218 2 1.000
#> 219 3 0.000
#> 220 1 1.000
#> 221 3 0.000
#> 222 3 0.000
#> 223 3 0.000
#> 224 3 0.000
#> 225 3 0.000
#> 226 3 0.000
#> 227 3 0.000
#> 228 3 0.000
#> 229 1 1.000
#> 230 2 0.751
#> 231 3 0.000
#> 232 3 0.000
#> 233 3 0.000
#> 234 3 0.000
#> 235 2 1.000
#> 236 3 0.000
#> 237 3 0.000
#> 238 3 0.000
#> 239 3 0.000
#> 240 3 0.000
#> 241 3 0.000
#> 242 3 1.000
#> 243 3 0.000
#> 244 1 1.000
#> 245 3 0.000
#> 246 3 0.000
#> 247 3 0.000
#> 248 2 1.000
#> 249 3 0.000
#> 250 3 0.000
#> 251 1 0.747
#> 252 3 0.000
#> 253 3 0.000
#> 254 3 0.000
#> 255 3 0.000
#> 256 3 0.000
#> 257 1 1.000
#> 258 3 0.000
#> 259 3 0.000
#> 260 3 0.000
#> 261 1 0.000
#> 262 3 0.000
#> 263 1 1.000
#> 264 1 1.000
#> 265 1 1.000
#> 266 3 0.000
#> 267 1 1.000
#> 268 1 1.000
#> 269 1 0.502
#> 270 3 0.000
#> 271 1 1.000
#> 272 1 1.000
#> 273 3 1.000
#> 274 3 0.000
#> 275 1 1.000
#> 276 3 1.000
#> 277 3 0.000
#> 278 3 0.000
#> 279 3 1.000
#> 280 3 0.000
#> 281 2 1.000
#> 282 1 1.000
#> 283 3 1.000
#> 284 3 1.000
#> 285 3 0.000
#> 286 2 1.000
#> 287 3 0.000
#> 288 3 0.000
#> 289 3 0.000
#> 290 3 0.000
#> 291 3 0.000
#> 292 3 0.000
#> 293 3 0.000
#> 294 3 0.000
#> 295 3 0.000
#> 296 3 0.000
#> 297 3 0.000
#> 298 3 0.000
#> 299 2 1.000
#> 300 3 0.000
#> 301 2 0.000
#> 302 3 0.000
#> 303 3 0.000
#> 304 2 0.249
#> 305 3 1.000
#> 306 2 1.000
#> 307 2 1.000
#> 308 3 0.000
#> 309 2 1.000
#> 310 3 0.000
#> 311 3 0.000
#> 312 3 0.000
#> 313 2 1.000
#> 314 3 0.000
#> 315 3 0.000
#> 316 3 0.000
#> 317 1 0.000
#> 318 3 0.253
#> 319 3 0.253
#> 320 1 1.000
#> 321 3 0.000
#> 322 1 1.000
#> 323 3 0.000
#> 324 3 1.000
#> 325 3 0.000
#> 326 1 0.747
#> 327 3 0.751
#> 328 3 0.000
#> 329 3 0.000
#> 330 3 0.000
#> 331 3 0.000
#> 332 1 1.000
#> 333 1 0.498
#> 334 1 1.000
#> 335 1 1.000
#> 336 2 0.000
#> 337 2 0.000
#> 338 1 0.000
#> 339 3 1.000
#> 340 1 1.000
#> 341 3 0.000
#> 342 3 0.000
#> 343 2 0.000
#> 344 3 0.000
#> 345 3 0.000
#> 346 3 0.000
#> 347 2 1.000
#> 348 3 0.502
#> 349 3 0.000
#> 350 2 0.249
#> 351 3 1.000
#> 352 1 1.000
#> 353 3 0.000
#> 354 3 1.000
#> 355 3 0.000
#> 356 3 1.000
#> 357 3 0.000
#> 358 3 0.000
#> 359 3 0.000
#> 360 3 0.000
#> 361 3 0.249
#> 362 3 0.000
#> 363 3 0.000
#> 364 3 0.000
#> 365 3 0.498
#> 366 3 0.000
#> 367 3 0.253
#> 368 3 0.000
#> 369 3 0.000
#> 370 3 0.000
#> 371 3 0.249
#> 372 3 0.000
#> 373 3 0.000
#> 374 3 0.000
#> 375 3 0.000
#> 376 3 0.000
#> 377 3 0.000
#> 378 3 0.000
#> 379 3 0.000
#> 380 3 0.000
#> 381 1 1.000
#> 382 1 1.000
#> 383 2 1.000
#> 384 3 1.000
#> 385 1 1.000
#> 386 1 1.000
#> 387 1 1.000
#> 388 3 0.000
#> 389 3 1.000
#> 390 3 0.751
#> 391 1 1.000
#> 392 3 0.751
#> 393 1 1.000
#> 394 3 1.000
#> 395 2 1.000
#> 396 3 0.000
#> 397 3 0.000
#> 398 3 1.000
#> 399 1 1.000
#> 400 1 0.751
#> 401 3 0.000
#> 402 1 1.000
#> 403 1 0.000
#> 404 3 1.000
#> 405 1 1.000
#> 406 2 1.000
#> 407 1 1.000
#> 408 3 0.000
#> 409 1 1.000
#> 410 1 1.000
#> 411 3 0.000
#> 412 3 0.000
#> 413 2 1.000
#> 414 1 1.000
#> 415 1 1.000
#> 416 1 1.000
#> 417 1 1.000
#> 418 1 1.000
#> 419 3 1.000
#> 420 1 0.751
#> 421 1 0.000
#> 422 1 1.000
#> 423 1 1.000
#> 424 3 0.000
#> 425 2 0.000
#> 426 1 1.000
#> 427 1 1.000
#> 428 1 1.000
#> 429 1 1.000
#> 430 1 1.000
#> 431 1 0.000
#> 432 2 0.000
#> 433 1 1.000
#> 434 2 0.000
#> 435 1 0.000
#> 436 2 1.000
#> 437 1 1.000
#> 438 2 1.000
#> 439 1 0.751
#> 440 2 1.000
#> 441 2 0.000
#> 442 3 1.000
#> 443 3 1.000
#> 444 3 0.502
#> 445 3 1.000
#> 446 1 0.253
#> 447 1 1.000
#> 448 3 0.000
#> 449 3 0.000
#> 450 3 0.000
#> 451 3 0.751
#> 452 1 1.000
#> 453 3 1.000
#> 454 1 1.000
#> 455 1 1.000
#> 456 3 1.000
#> 457 1 1.000
#> 458 1 1.000
#> 459 3 0.000
#> 460 3 0.000
#> 461 3 0.000
#> 462 3 0.000
#> 463 3 1.000
#> 464 3 0.000
#> 465 3 0.000
#> 466 3 0.000
#> 467 3 0.000
#> 468 3 0.000
#> 469 3 1.000
#> 470 3 0.000
#> 471 3 0.000
#> 472 3 0.000
#> 473 3 0.000
#> 474 3 0.000
#> 475 3 0.000
#> 476 3 0.000
#> 477 3 1.000
#> 478 3 0.000
#> 479 3 0.000
#> 480 3 0.000
#> 481 3 0.000
#> 482 3 1.000
#> 483 3 0.000
#> 484 1 1.000
#> 485 2 1.000
#> 486 3 0.000
#> 487 3 0.502
#> 488 3 0.000
#> 489 2 1.000
#> 490 2 1.000
#> 491 2 1.000
#> 492 3 0.000
#> 493 3 0.000
#> 494 2 0.751
#> 495 3 0.000
#> 496 1 1.000
#> 497 2 1.000
#> 498 2 0.249
#> 499 1 1.000
#> 500 3 0.000
#> 501 2 1.000
#> 502 1 1.000
#> 503 3 0.000
#> 504 3 0.000
#> 505 3 0.000
#> 506 2 0.747
#> 507 1 1.000
#> 508 3 1.000
#> 509 3 0.498
#> 510 3 0.000
#> 511 3 0.000
#> 512 2 1.000
#> 513 3 1.000
#> 514 3 1.000
#> 515 1 1.000
#> 516 1 0.747
#> 517 2 1.000
#> 518 3 0.000
#> 519 3 0.000
#> 520 3 0.000
#> 521 1 0.249
#> 522 1 0.000
#> 523 1 1.000
#> 524 3 0.000
#> 525 3 0.000
#> 526 3 0.000
#> 527 3 0.000
#> 528 1 1.000
#> 529 1 0.000
#> 530 2 0.000
#> 531 2 1.000
#> 532 2 1.000
#> 533 1 1.000
#> 534 2 1.000
#> 535 1 0.751
#> 536 1 1.000
#> 537 1 0.502
#> 538 2 0.249
#> 539 1 0.000
#> 540 3 1.000
#> 541 1 1.000
#> 542 2 1.000
#> 543 1 0.249
#> 544 1 1.000
#> 545 2 0.000
#> 546 1 0.000
#> 547 1 0.249
#> 548 1 1.000
#> 549 3 1.000
#> 550 1 1.000
#> 551 1 1.000
#> 552 1 1.000
#> 553 1 1.000
#> 554 1 1.000
#> 555 1 1.000
#> 556 1 0.000
#> 557 1 1.000
#> 558 3 1.000
#> 559 3 0.000
#> 560 1 0.000
#> 561 3 0.000
#> 562 1 1.000
#> 563 3 0.747
#> 564 3 0.000
#> 565 1 1.000
#> 566 1 1.000
#> 567 1 1.000
#> 568 2 1.000
#> 569 3 0.000
#> 570 1 1.000
#> 571 1 0.000
#> 572 2 1.000
#> 573 3 0.000
#> 574 3 0.000
#> 575 1 1.000
#> 576 3 0.000
#> 577 1 1.000
#> 578 1 1.000
#> 579 1 1.000
#> 580 2 0.000
#> 581 1 1.000
#> 582 1 1.000
#> 583 1 1.000
#> 584 1 0.249
#> 585 3 0.000
#> 586 1 1.000
#> 587 3 1.000
#> 588 1 1.000
#> 589 1 0.000
#> 590 1 0.000
#> 591 1 0.000
#> 592 3 0.751
#> 593 1 1.000
#> 594 3 0.000
#> 595 3 0.000
#> 596 3 1.000
#> 597 1 1.000
#> 598 3 1.000
#> 599 3 1.000
#> 600 1 1.000
#> 601 1 0.249
#> 602 1 0.000
#> 603 1 1.000
#> 604 3 0.000
#> 605 1 1.000
#> 606 1 1.000
#> 607 2 0.000
#> 608 1 0.751
#> 609 1 1.000
#> 610 1 1.000
#> 611 1 0.249
#> 612 1 0.502
#> 613 1 1.000
#> 614 1 1.000
#> 615 1 0.000
#> 616 2 0.000
#> 617 3 0.000
#> 618 3 1.000
#> 619 1 1.000
#> 620 3 1.000
#> 621 3 1.000
#> 622 3 0.000
#> 623 1 1.000
#> 624 3 0.000
#> 625 1 1.000
#> 626 3 0.000
#> 627 3 0.000
#> 628 3 0.000
#> 629 3 0.000
#> 630 3 0.000
#> 631 1 1.000
#> 632 1 1.000
#> 633 3 0.000
#> 634 1 1.000
#> 635 3 0.000
#> 636 2 0.751
#> 637 3 0.000
#> 638 1 1.000
#> 639 1 0.751
#> 640 1 1.000
#> 641 1 1.000
#> 642 3 0.000
#> 643 3 0.502
#> 644 1 1.000
#> 645 3 1.000
#> 646 3 0.000
#> 647 1 1.000
#> 648 1 1.000
#> 649 2 0.502
#> 650 1 1.000
#> 651 1 1.000
#> 652 1 1.000
#> 653 1 1.000
#> 654 1 1.000
#> 655 3 0.000
#> 656 1 1.000
#> 657 1 1.000
#> 658 1 1.000
#> 659 3 1.000
#> 660 3 0.751
#> 661 3 0.000
#> 662 1 1.000
#> 663 2 0.000
#> 664 1 1.000
#> 665 1 1.000
#> 666 3 0.000
#> 667 3 0.000
#> 668 3 0.747
#> 669 3 1.000
#> 670 3 1.000
#> 671 2 0.502
#> 672 1 1.000
#> 673 2 0.502
#> 674 3 0.751
#> 675 3 0.000
#> 676 2 0.000
#> 677 3 0.000
#> 678 3 1.000
#> 679 2 0.000
#> 680 1 1.000
#> 681 3 0.000
#> 682 3 0.502
#> 683 3 0.000
#> 684 3 0.253
#> 685 3 0.000
#> 686 3 0.000
#> 687 3 0.000
#> 688 3 0.000
#> 689 3 0.000
#> 690 3 0.000
#> 691 3 0.000
#> 692 1 1.000
#> 693 3 0.000
#> 694 3 0.000
#> 695 2 0.000
#> 696 3 0.000
#> 697 3 1.000
#> 698 1 1.000
#> 699 3 0.000
#> 700 1 0.502
#> 701 3 0.000
#> 702 1 1.000
#> 703 1 1.000
#> 704 3 0.000
#> 705 3 0.000
#> 706 2 0.751
#> 707 2 0.000
#> 708 1 1.000
#> 709 3 1.000
#> 710 1 0.249
#> 711 3 1.000
#> 712 2 0.249
#> 713 1 0.249
#> 714 1 1.000
#> 715 1 1.000
#> 716 1 1.000
#> 717 1 0.498
#> 718 1 1.000
#> 719 2 0.747
#> 720 3 1.000
#> 721 3 1.000
#> 722 2 1.000
#> 723 2 0.000
#> 724 1 1.000
#> 725 2 0.751
#> 726 3 1.000
#> 727 3 1.000
#> 728 3 0.000
#> 729 1 1.000
#> 730 1 1.000
#> 731 3 0.000
#> 732 1 1.000
#> 733 1 1.000
#> 734 3 0.000
#> 735 1 1.000
#> 736 1 1.000
#> 737 1 0.249
#> 738 1 1.000
#> 739 1 1.000
#> 740 3 0.502
#> 741 1 1.000
#> 742 1 1.000
#> 743 1 1.000
#> 744 3 0.000
#> 745 3 1.000
#> 746 1 1.000
#> 747 3 0.000
#> 748 3 0.000
#> 749 3 0.000
#> 750 3 0.000
#> 751 3 0.000
#> 752 1 1.000
#> 753 3 1.000
#> 754 1 1.000
#> 755 1 1.000
#> 756 1 1.000
#> 757 3 1.000
#> 758 1 1.000
#> 759 3 1.000
#> 760 3 1.000
#> 761 2 1.000
#> 762 2 1.000
#> 763 1 1.000
#> 764 1 1.000
#> 765 3 1.000
#> 766 1 1.000
#> 767 1 1.000
#> 768 1 1.000
#> 769 1 1.000
#> 770 1 0.249
#> 771 3 0.000
#> 772 1 1.000
#> 773 1 1.000
#> 774 3 1.000
#> 775 1 1.000
#> 776 2 0.249
#> 777 1 0.498
#> 778 1 1.000
#> 779 2 0.751
#> 780 1 1.000
#> 781 3 1.000
#> 782 1 1.000
#> 783 1 1.000
#> 784 1 1.000
#> 785 1 1.000
#> 786 1 1.000
#> 787 3 0.000
#> 788 1 0.000
#> 789 3 0.249
#> 790 3 0.000
#> 791 1 1.000
#> 792 1 1.000
#> 793 3 0.000
#> 794 2 0.249
#> 795 1 1.000
#> 796 3 0.000
#> 797 1 1.000
#> 798 1 1.000
#> 799 2 0.000
#> 800 3 1.000
#> 801 3 0.000
#> 802 2 0.000
#> 803 3 0.000
#> 804 3 1.000
#> 805 3 0.000
#> 806 2 0.000
#> 807 3 1.000
#> 808 1 1.000
#> 809 3 0.502
#> 810 1 0.751
#> 811 1 1.000
#> 812 1 0.000
#> 813 3 0.249
#> 814 3 0.751
#> 815 1 0.000
#> 816 1 0.000
#> 817 1 1.000
#> 818 1 1.000
#> 819 1 0.000
#> 820 2 0.000
#> 821 1 0.000
#> 822 1 0.751
#> 823 1 0.000
#> 824 1 0.000
#> 825 1 1.000
#> 826 1 1.000
#> 827 1 1.000
#> 828 1 0.747
#> 829 1 1.000
#> 830 1 1.000
#> 831 1 0.498
#> 832 1 0.249
#> 833 1 0.000
#> 834 1 0.000
#> 835 1 0.000
#> 836 1 0.000
#> 837 1 0.000
#> 838 1 0.000
#> 839 1 0.249
#> 840 1 0.000
#> 841 1 1.000
#> 842 1 0.498
#> 843 1 0.000
#> 844 1 0.000
#> 845 1 0.000
#> 846 1 0.000
#> 847 1 1.000
#> 848 1 0.000
#> 849 3 1.000
#> 850 1 0.000
#> 851 1 0.000
#> 852 2 0.000
#> 853 3 0.747
#> 854 2 0.249
#> 855 1 0.000
#> 856 1 0.000
#> 857 1 0.000
#> 858 1 0.502
#> 859 3 1.000
#> 860 1 0.498
#> 861 1 1.000
#> 862 3 1.000
#> 863 1 0.000
#> 864 1 0.000
#> 865 1 0.253
#> 866 1 0.000
#> 867 1 0.000
#> 868 1 0.000
#> 869 1 1.000
#> 870 3 0.000
#> 871 1 1.000
#> 872 1 1.000
#> 873 1 0.249
#> 874 1 0.000
#> 875 1 1.000
#> 876 1 0.502
#> 877 1 0.000
#> 878 1 1.000
#> 879 1 0.249
#> 880 1 0.000
#> 881 1 0.253
#> 882 1 1.000
#> 883 1 0.253
#> 884 1 0.751
#> 885 1 0.000
#> 886 2 0.502
#> 887 1 0.000
#> 888 3 1.000
#> 889 1 1.000
#> 890 3 1.000
#> 891 1 1.000
#> 892 3 1.000
#> 893 1 1.000
#> 894 1 0.000
#> 895 1 1.000
#> 896 2 0.000
#> 897 2 0.502
#> 898 1 0.000
#> 899 1 0.000
#> 900 1 0.000
#> 901 1 0.000
#> 902 1 0.249
#> 903 3 0.000
#> 904 1 0.000
#> 905 1 1.000
#> 906 1 1.000
#> 907 1 0.000
#> 908 1 1.000
#> 909 1 1.000
#> 910 1 0.751
#> 911 1 0.751
#> 912 1 0.747
#> 913 1 0.000
#> 914 1 0.249
#> 915 1 1.000
#> 916 1 1.000
#> 917 1 0.000
#> 918 1 0.000
#> 919 1 0.000
#> 920 1 0.000
#> 921 1 0.000
#> 922 1 0.000
#> 923 1 0.000
#> 924 1 0.751
#> 925 1 1.000
#> 926 1 0.000
#> 927 1 0.000
#> 928 1 0.000
#> 929 2 0.751
#> 930 1 0.000
#> 931 1 0.000
#> 932 1 0.000
#> 933 1 0.498
#> 934 1 0.000
#> 935 2 0.253
#> 936 3 1.000
#> 937 1 0.000
#> 938 2 1.000
#> 939 1 1.000
#> 940 1 0.000
#> 941 1 1.000
#> 942 1 0.000
#> 943 1 0.751
#> 944 1 0.000
#> 945 1 0.249
#> 946 1 0.498
#> 947 1 0.751
#> 948 1 1.000
#> 949 1 0.000
#> 950 1 0.249
#> 951 1 0.000
#> 952 1 0.000
#> 953 1 0.249
#> 954 3 1.000
#> 955 1 0.000
#> 956 2 1.000
#> 957 1 0.000
#> 958 1 0.000
#> 959 1 0.000
#> 960 2 1.000
#> 961 2 0.751
#> 962 1 1.000
#> 963 1 0.000
#> 964 2 1.000
#> 965 3 0.000
#> 966 1 1.000
#> 967 1 0.747
#> 968 1 0.000
#> 969 1 1.000
#> 970 3 0.000
#> 971 1 0.000
#> 972 1 0.000
#> 973 2 1.000
#> 974 1 1.000
#> 975 1 0.000
#> 976 1 0.249
#> 977 1 0.000
#> 978 1 1.000
#> 979 1 0.000
#> 980 1 0.249
#> 981 3 1.000
#> 982 3 1.000
#> 983 2 1.000
#> 984 2 1.000
#> 985 3 1.000
#> 986 1 1.000
#> 987 1 1.000
#> 988 1 1.000
#> 989 1 1.000
#> 990 3 0.000
#> 991 3 1.000
#> 992 1 0.249
#> 993 1 1.000
#> 994 1 1.000
#> 995 3 1.000
#> 996 1 1.000
#> 997 1 0.498
#> 998 3 1.000
#> 999 3 0.751
#> 1000 1 0.000
#> 1001 2 0.000
#> 1002 1 0.000
#> 1003 1 0.000
#> 1004 1 0.249
#> 1005 1 0.000
#> 1006 1 0.751
#> 1007 1 1.000
#> 1008 1 0.000
#> 1009 1 0.000
#> 1010 1 0.000
#> 1011 1 0.000
#> 1012 1 0.249
#> 1013 1 0.253
#> 1014 2 1.000
#> 1015 1 0.000
#> 1016 2 1.000
#> 1017 1 0.000
#> 1018 1 0.751
#> 1019 1 0.000
#> 1020 1 0.502
#> 1021 1 1.000
#> 1022 1 1.000
#> 1023 1 1.000
#> 1024 1 1.000
#> 1025 1 1.000
#> 1026 3 0.000
#> 1027 1 0.000
#> 1028 1 1.000
#> 1029 1 0.751
#> 1030 1 1.000
#> 1031 1 0.000
#> 1032 1 0.249
#> 1033 1 1.000
#> 1034 1 0.000
#> 1035 1 1.000
#> 1036 3 1.000
#> 1037 1 0.249
#> 1038 3 0.000
#> 1039 1 1.000
#> 1040 2 0.000
#> 1041 1 0.000
#> 1042 1 1.000
#> 1043 3 0.000
#> 1044 1 0.502
#> 1045 1 1.000
#> 1046 1 0.000
#> 1047 3 0.000
#> 1048 1 0.000
#> 1049 1 0.000
#> 1050 1 0.000
#> 1051 1 1.000
#> 1052 1 0.502
#> 1053 1 0.751
#> 1054 1 0.000
#> 1055 1 0.000
#> 1056 1 0.000
#> 1057 1 0.000
#> 1058 1 0.000
#> 1059 1 0.000
#> 1060 1 0.000
#> 1061 1 0.000
#> 1062 1 0.000
#> 1063 1 0.000
#> 1064 1 0.000
#> 1065 1 0.000
#> 1066 1 0.000
#> 1067 1 0.000
#> 1068 1 0.000
#> 1069 1 0.000
#> 1070 1 0.000
#> 1071 1 0.000
#> 1072 1 0.000
#> 1073 1 1.000
#> 1074 1 0.000
#> 1075 3 0.000
#> 1076 1 0.000
#> 1077 1 0.000
#> 1078 1 0.000
#> 1079 1 0.502
#> 1080 1 0.000
#> 1081 1 0.000
#> 1082 1 0.000
#> 1083 3 0.000
#> 1084 1 0.000
#> 1085 1 0.000
#> 1086 1 0.000
#> 1087 1 0.000
#> 1088 3 0.000
#> 1089 1 0.000
#> 1090 1 0.000
#> 1091 1 0.000
#> 1092 1 0.000
#> 1093 1 0.000
#> 1094 1 1.000
#> 1095 1 0.000
#> 1096 1 0.000
#> 1097 1 0.000
#> 1098 1 0.000
#> 1099 1 0.000
#> 1100 1 0.000
#> 1101 1 1.000
#> 1102 1 0.000
#> 1103 1 0.000
#> 1104 1 1.000
#> 1105 1 0.000
#> 1106 1 0.000
#> 1107 1 0.000
#> 1108 1 0.000
#> 1109 1 0.000
#> 1110 3 0.000
#> 1111 1 0.000
#> 1112 1 0.000
#> 1113 1 0.000
#> 1114 1 0.000
#> 1115 1 0.000
#> 1116 1 1.000
#> 1117 1 0.000
#> 1118 3 0.000
#> 1119 1 0.000
#> 1120 1 0.000
#> 1121 1 0.253
#> 1122 3 0.000
#> 1123 1 0.502
#> 1124 1 0.000
#> 1125 1 0.000
#> 1126 1 0.000
#> 1127 1 0.000
#> 1128 3 0.000
#> 1129 1 0.000
#> 1130 1 0.000
#> 1131 1 0.000
#> 1132 1 0.000
#> 1133 1 1.000
#> 1134 1 0.000
#> 1135 3 0.000
#> 1136 1 0.000
#> 1137 1 0.000
#> 1138 1 0.000
#> 1139 1 0.000
#> 1140 1 0.000
#> 1141 2 0.000
#> 1142 1 0.000
#> 1143 1 0.000
#> 1144 1 1.000
#> 1145 3 0.000
#> 1146 1 0.000
#> 1147 1 0.000
#> 1148 1 0.000
#> 1149 1 0.000
#> 1150 1 0.000
#> 1151 1 0.000
#> 1152 1 0.000
#> 1153 1 0.000
#> 1154 1 0.000
#> 1155 1 0.000
#> 1156 1 0.000
#> 1157 1 0.000
#> 1158 1 0.000
#> 1159 1 0.000
#> 1160 1 0.000
#> 1161 1 0.000
#> 1162 1 0.747
#> 1163 1 1.000
#> 1164 1 0.000
#> 1165 1 0.000
#> 1166 1 0.000
#> 1167 1 0.000
#> 1168 1 0.249
#> 1169 1 0.000
#> 1170 1 0.000
#> 1171 1 0.000
#> 1172 3 0.000
#> 1173 1 0.000
#> 1174 1 0.000
#> 1175 1 1.000
#> 1176 1 0.498
#> 1177 1 0.000
#> 1178 1 0.000
#> 1179 1 0.000
#> 1180 1 0.000
#> 1181 2 1.000
#> 1182 1 0.000
#> 1183 1 0.000
#> 1184 1 0.000
#> 1185 2 0.000
#> 1186 1 0.000
#> 1187 1 0.000
#> 1188 1 0.000
#> 1189 1 1.000
#> 1190 1 0.000
#> 1191 1 0.000
#> 1192 1 0.000
#> 1193 1 0.000
#> 1194 1 0.000
#> 1195 1 0.000
#> 1196 1 0.000
#> 1197 1 0.000
#> 1198 1 0.000
#> 1199 1 0.000
#> 1200 1 0.000
#> 1201 1 0.000
#> 1202 1 0.000
#> 1203 1 0.000
#> 1204 1 0.000
#> 1205 1 0.000
#> 1206 1 0.000
#> 1207 1 0.000
#> 1208 1 1.000
#> 1209 1 0.000
#> 1210 1 0.000
#> 1211 1 0.000
#> 1212 1 0.000
#> 1213 1 0.000
#> 1214 1 0.000
#> 1215 2 1.000
#> 1216 1 0.000
#> 1217 3 1.000
#> 1218 3 0.000
#> 1219 1 0.000
#> 1220 1 0.000
#> 1221 1 1.000
#> 1222 1 0.000
#> 1223 1 0.000
#> 1224 2 0.249
#> 1225 1 0.000
#> 1226 2 0.000
#> 1227 1 0.000
#> 1228 1 0.000
#> 1229 1 0.000
#> 1230 1 0.000
#> 1231 1 0.000
#> 1232 1 0.000
#> 1233 1 0.000
#> 1234 1 1.000
#> 1235 1 0.000
#> 1236 1 1.000
#> 1237 1 1.000
#> 1238 3 0.000
#> 1239 1 0.000
#> 1240 1 0.000
#> 1241 3 0.000
#> 1242 3 1.000
#> 1243 3 0.000
#> 1244 1 0.000
#> 1245 1 0.000
#> 1246 1 0.000
#> 1247 1 1.000
#> 1248 1 1.000
#> 1249 1 0.000
#> 1250 1 0.000
#> 1251 1 1.000
#> 1252 1 0.000
#> 1253 1 1.000
#> 1254 1 0.000
#> 1255 1 1.000
#> 1256 2 1.000
#> 1257 2 1.000
#> 1258 2 0.000
#> 1259 2 0.000
#> 1260 1 0.751
#> 1261 1 0.000
#> 1262 1 1.000
#> 1263 1 0.000
#> 1264 1 0.000
#> 1265 3 1.000
#> 1266 1 0.000
#> 1267 1 0.000
#> 1268 1 1.000
#> 1269 1 0.249
#> 1270 1 1.000
#> 1271 1 1.000
#> 1272 1 0.751
#> 1273 3 1.000
#> 1274 1 0.000
#> 1275 1 0.000
#> 1276 1 0.000
#> 1277 3 0.000
#> 1278 2 0.249
#> 1279 3 0.000
#> 1280 3 0.000
#> 1281 3 0.000
#> 1282 1 0.751
#> 1283 3 0.000
#> 1284 1 1.000
#> 1285 1 1.000
#> 1286 1 0.000
#> 1287 1 0.000
#> 1288 1 1.000
#> 1289 3 0.000
#> 1290 3 0.000
#> 1291 1 1.000
#> 1292 3 0.000
#> 1293 1 1.000
#> 1294 1 1.000
#> 1295 2 0.000
#> 1296 3 0.000
#> 1297 1 0.502
#> 1298 1 1.000
#> 1299 1 0.000
#> 1300 1 0.000
#> 1301 1 0.249
#> 1302 1 0.000
#> 1303 1 0.502
#> 1304 1 0.000
#> 1305 1 1.000
#> 1306 1 0.000
#> 1307 1 0.000
#> 1308 1 0.000
#> 1309 1 0.000
#> 1310 1 0.000
#> 1311 1 0.000
#> 1312 1 0.747
#> 1313 2 0.000
#> 1314 3 1.000
#> 1315 2 0.000
#> 1316 1 0.000
#> 1317 3 0.000
#> 1318 2 0.751
#> 1319 1 0.249
#> 1320 1 0.000
#> 1321 1 0.751
#> 1322 1 0.000
#> 1323 1 0.000
#> 1324 1 0.249
#> 1325 1 0.000
#> 1326 1 0.253
#> 1327 3 0.000
#> 1328 1 0.000
#> 1329 1 0.000
#> 1330 1 0.000
#> 1331 1 0.000
#> 1332 2 0.000
#> 1333 1 1.000
#> 1334 3 1.000
#> 1335 1 1.000
#> 1336 3 0.000
#> 1337 1 0.000
#> 1338 1 0.000
#> 1339 1 0.000
#> 1340 1 0.751
#> 1341 1 0.751
#> 1342 1 0.000
#> 1343 2 0.498
#> 1344 1 1.000
#> 1345 1 0.000
#> 1346 2 1.000
#> 1347 2 0.000
#> 1348 2 0.000
#> 1349 1 0.502
#> 1350 1 0.751
#> 1351 1 0.000
#> 1352 1 0.000
#> 1353 2 0.498
#> 1354 2 1.000
#> 1355 1 0.751
#> 1356 1 0.000
#> 1357 1 0.751
#> 1358 1 1.000
#> 1359 1 0.000
#> 1360 1 0.000
#> 1361 2 0.249
#> 1362 2 1.000
#> 1363 1 0.000
#> 1364 1 0.498
#> 1365 3 1.000
#> 1366 3 0.000
#> 1367 1 0.000
#> 1368 3 0.000
#> 1369 1 1.000
#> 1370 3 0.000
#> 1371 3 0.000
#> 1372 3 0.000
#> 1373 3 0.000
#> 1374 1 1.000
#> 1375 3 0.000
#> 1376 1 0.751
#> 1377 1 0.502
#> 1378 1 0.000
#> 1379 1 0.249
#> 1380 1 0.000
#> 1381 1 0.000
#> 1382 1 0.498
#> 1383 1 0.249
#> 1384 1 0.000
#> 1385 1 0.000
#> 1386 1 1.000
#> 1387 1 0.000
#> 1388 1 0.000
#> 1389 1 0.253
#> 1390 1 0.000
#> 1391 1 0.000
#> 1392 3 1.000
#> 1393 1 0.000
#> 1394 1 0.000
#> 1395 2 0.000
#> 1396 1 0.000
#> 1397 2 0.000
#> 1398 2 0.000
#> 1399 2 0.000
#> 1400 1 0.000
#> 1401 2 0.000
#> 1402 1 0.000
#> 1403 1 0.000
#> 1404 1 0.000
#> 1405 1 0.000
#> 1406 3 0.000
#> 1407 2 0.000
#> 1408 1 0.000
#> 1409 1 0.000
#> 1410 3 0.000
#> 1411 1 0.000
#> 1412 1 0.000
#> 1413 1 0.000
#> 1414 1 0.000
#> 1415 1 0.000
#> 1416 1 0.000
#> 1417 1 0.000
#> 1418 1 0.000
#> 1419 3 0.000
#> 1420 1 0.000
#> 1421 1 0.000
#> 1422 3 0.253
#> 1423 1 0.000
#> 1424 1 0.000
#> 1425 1 0.000
#> 1426 1 0.747
#> 1427 1 0.000
#> 1428 1 0.000
#> 1429 3 0.253
#> 1430 3 1.000
#> 1431 1 0.000
#> 1432 1 1.000
#> 1433 1 0.000
#> 1434 3 1.000
#> 1435 1 0.000
#> 1436 1 0.000
#> 1437 1 0.000
#> 1438 1 0.000
#> 1439 1 0.000
#> 1440 1 0.000
#> 1441 1 0.000
#> 1442 1 0.000
#> 1443 1 0.253
#> 1444 1 0.000
#> 1445 1 0.000
#> 1446 1 0.000
#> 1447 1 0.000
#> 1448 1 0.000
#> 1449 2 0.000
#> 1450 1 0.000
#> 1451 3 0.000
#> 1452 2 0.000
#> 1453 1 0.000
#> 1454 1 0.000
#> 1455 2 0.000
#> 1456 1 0.000
#> 1457 1 0.000
#> 1458 1 0.000
#> 1459 2 1.000
#> 1460 1 0.000
#> 1461 1 0.000
#> 1462 1 0.000
#> 1463 1 0.000
#> 1464 1 0.000
#> 1465 1 0.000
#> 1466 1 0.000
#> 1467 1 0.000
#> 1468 1 1.000
#> 1469 1 1.000
#> 1470 1 1.000
#> 1471 1 0.000
#> 1472 3 0.000
#> 1473 1 0.000
#> 1474 3 0.000
#> 1475 3 1.000
#> 1476 3 0.000
#> 1477 1 1.000
#> 1478 2 0.000
#> 1479 1 0.000
#> 1480 2 0.000
#> 1481 1 0.000
#> 1482 1 0.000
#> 1483 1 1.000
#> 1484 1 0.498
#> 1485 1 0.000
#> 1486 1 0.000
#> 1487 1 0.000
#> 1488 3 1.000
#> 1489 1 1.000
#> 1490 1 0.000
#> 1491 1 0.000
#> 1492 1 0.000
#> 1493 1 0.000
#> 1494 1 0.000
#> 1495 2 0.000
#> 1496 1 0.000
#> 1497 1 0.000
#> 1498 1 0.000
#> 1499 1 0.000
#> 1500 2 0.000
#> 1501 1 0.000
#> 1502 1 0.000
#> 1503 1 0.000
#> 1504 1 0.751
#> 1505 1 0.000
#> 1506 3 1.000
#> 1507 1 0.000
#> 1508 2 1.000
#> 1509 1 0.000
#> 1510 2 0.000
#> 1511 1 0.000
#> 1512 1 0.000
#> 1513 1 0.000
#> 1514 1 0.000
#> 1515 1 0.000
#> 1516 1 0.000
#> 1517 1 0.000
#> 1518 2 0.000
#> 1519 3 1.000
#> 1520 2 0.000
#> 1521 2 0.000
#> 1522 2 0.000
#> 1523 1 0.000
#> 1524 2 0.000
#> 1525 1 0.000
#> 1526 1 0.000
#> 1527 3 1.000
#> 1528 1 1.000
#> 1529 1 0.000
#> 1530 1 0.000
#> 1531 1 0.000
#> 1532 1 0.000
#> 1533 1 0.751
#> 1534 2 0.000
#> 1535 1 1.000
#> 1536 2 0.000
#> 1537 1 1.000
#> 1538 1 1.000
#> 1539 1 0.000
#> 1540 1 0.000
#> 1541 1 0.000
#> 1542 1 0.000
#> 1543 1 0.000
#> 1544 1 0.000
#> 1545 1 0.000
#> 1546 1 0.000
#> 1547 1 0.000
#> 1548 1 0.000
#> 1549 1 0.000
#> 1550 1 0.000
#> 1551 3 0.253
#> 1552 1 0.000
#> 1553 1 0.751
#> 1554 1 0.000
#> 1555 1 0.000
#> 1556 1 0.000
#> 1557 1 0.000
#> 1558 2 0.000
#> 1559 2 0.000
#> 1560 1 0.000
#> 1561 1 0.000
#> 1562 1 0.000
#> 1563 1 0.000
#> 1564 3 0.249
#> 1565 2 1.000
#> 1566 1 1.000
#> 1567 1 0.751
#> 1568 3 0.000
#> 1569 1 0.000
#> 1570 1 0.000
#> 1571 1 0.000
#> 1572 2 1.000
#> 1573 2 0.000
#> 1574 1 0.000
#> 1575 2 0.000
#> 1576 3 1.000
#> 1577 3 0.502
#> 1578 2 0.000
#> 1579 2 0.502
#> 1580 2 1.000
#> 1581 3 0.000
#> 1582 2 0.000
#> 1583 3 0.000
#> 1584 2 0.000
#> 1585 3 0.000
#> 1586 3 0.000
#> 1587 3 0.000
#> 1588 3 0.000
#> 1589 3 0.000
#> 1590 3 0.751
#> 1591 1 0.000
#> 1592 3 0.000
#> 1593 3 0.000
#> 1594 2 0.498
#> 1595 3 0.000
#> 1596 2 0.000
#> 1597 2 0.000
#> 1598 2 0.000
#> 1599 1 0.502
#> 1600 2 0.000
#> 1601 1 0.000
#> 1602 3 1.000
#> 1603 1 0.502
#> 1604 2 0.000
#> 1605 2 0.000
#> 1606 1 0.751
#> 1607 1 1.000
#> 1608 2 0.000
#> 1609 2 1.000
#> 1610 2 1.000
#> 1611 3 0.000
#> 1612 3 0.249
#> 1613 3 0.000
#> 1614 3 0.000
#> 1615 3 0.000
#> 1616 3 0.000
#> 1617 3 0.000
#> 1618 2 0.000
#> 1619 1 0.249
#> 1620 2 0.000
#> 1621 2 0.000
#> 1622 2 0.000
#> 1623 1 0.000
#> 1624 3 0.000
#> 1625 1 0.000
#> 1626 2 0.249
#> 1627 1 0.000
#> 1628 1 0.751
#> 1629 1 0.000
#> 1630 1 1.000
#> 1631 1 0.000
#> 1632 1 0.000
#> 1633 3 0.502
#> 1634 1 0.000
#> 1635 2 0.253
#> 1636 1 0.000
#> 1637 1 0.000
#> 1638 1 0.000
#> 1639 2 0.000
#> 1640 1 0.000
#> 1641 1 0.000
#> 1642 1 1.000
#> 1643 1 0.000
#> 1644 2 0.000
#> 1645 1 0.000
#> 1646 1 0.000
#> 1647 1 0.000
#> 1648 1 0.000
#> 1649 1 0.249
#> 1650 1 0.000
#> 1651 1 0.000
#> 1652 1 0.000
#> 1653 1 0.000
#> 1654 3 0.751
#> 1655 1 0.000
#> 1656 1 0.000
#> 1657 1 0.000
#> 1658 1 0.000
#> 1659 1 1.000
#> 1660 1 0.000
#> 1661 1 0.000
#> 1662 1 0.000
#> 1663 1 0.000
#> 1664 1 0.000
#> 1665 1 0.000
#> 1666 1 0.000
#> 1667 1 0.000
#> 1668 1 0.000
#> 1669 1 0.249
#> 1670 1 0.000
#> 1671 1 0.000
#> 1672 1 0.000
#> 1673 1 0.000
#> 1674 1 0.000
#> 1675 1 0.000
#> 1676 2 1.000
#> 1677 1 0.000
#> 1678 1 0.751
#> 1679 1 0.000
#> 1680 2 0.000
#> 1681 1 0.000
#> 1682 1 0.000
#> 1683 1 0.000
#> 1684 1 0.502
#> 1685 1 0.000
#> 1686 1 0.498
#> 1687 1 0.000
#> 1688 1 0.000
#> 1689 1 1.000
#> 1690 1 0.747
#> 1691 1 0.000
#> 1692 1 0.000
#> 1693 3 1.000
#> 1694 1 0.000
#> 1695 1 0.000
#> 1696 1 0.000
#> 1697 1 0.000
#> 1698 1 1.000
#> 1699 1 0.000
#> 1700 1 0.000
#> 1701 1 1.000
#> 1702 1 0.000
#> 1703 3 1.000
#> 1704 1 0.000
#> 1705 1 0.000
#> 1706 1 0.000
#> 1707 3 1.000
#> 1708 1 0.000
#> 1709 1 0.000
#> 1710 1 0.000
#> 1711 1 0.000
#> 1712 3 1.000
#> 1713 1 0.000
#> 1714 1 0.000
#> 1715 1 0.249
#> 1716 1 0.000
#> 1717 1 0.000
#> 1718 1 0.000
#> 1719 3 1.000
#> 1720 3 1.000
#> 1721 1 0.000
#> 1722 1 0.000
#> 1723 1 0.000
#> 1724 1 0.000
#> 1725 1 0.000
#> 1726 1 0.000
#> 1727 1 0.000
#> 1728 3 0.000
#> 1729 1 0.000
#> 1730 3 0.000
#> 1731 3 0.000
#> 1732 1 0.000
#> 1733 1 0.000
#> 1734 2 0.000
#> 1735 1 0.000
#> 1736 1 0.000
#> 1737 1 1.000
#> 1738 1 1.000
#> 1739 1 1.000
#> 1740 1 0.498
#> 1741 1 0.000
#> 1742 1 0.000
#> 1743 1 0.502
#> 1744 1 0.000
#> 1745 1 0.000
#> 1746 1 0.000
#> 1747 1 0.751
#> 1748 1 1.000
#> 1749 3 0.249
#> 1750 1 1.000
#> 1751 3 0.000
#> 1752 1 0.000
#> 1753 2 0.000
#> 1754 1 0.000
#> 1755 2 0.000
#> 1756 1 0.000
#> 1757 1 1.000
#> 1758 1 0.000
#> 1759 1 0.249
#> 1760 3 0.000
#> 1761 2 0.000
#> 1762 1 0.000
#> 1763 1 1.000
#> 1764 1 1.000
#> 1765 1 0.000
#> 1766 2 0.000
#> 1767 3 0.000
#> 1768 1 0.000
#> 1769 1 0.000
#> 1770 2 1.000
#> 1771 1 0.000
#> 1772 1 0.000
#> 1773 1 0.000
#> 1774 1 0.000
#> 1775 1 0.000
#> 1776 1 0.000
#> 1777 1 1.000
#> 1778 1 0.000
#> 1779 1 0.000
#> 1780 1 0.000
#> 1781 3 0.000
#> 1782 1 0.000
#> 1783 1 0.000
#> 1784 1 0.000
#> 1785 1 0.249
#> 1786 1 0.747
#> 1787 3 0.253
#> 1788 1 1.000
#> 1789 1 0.000
#> 1790 3 1.000
#> 1791 1 0.000
#> 1792 3 0.000
#> 1793 3 0.000
#> 1794 3 0.747
#> 1795 2 1.000
#> 1796 1 1.000
#> 1797 1 0.000
#> 1798 3 0.000
#> 1799 2 0.000
#> 1800 1 0.000
#> 1801 1 0.000
#> 1802 1 0.000
#> 1803 1 1.000
#> 1804 3 0.751
#> 1805 1 0.000
#> 1806 1 0.000
#> 1807 2 0.000
#> 1808 2 0.000
#> 1809 2 0.000
#> 1810 3 1.000
#> 1811 2 0.747
#> 1812 1 0.000
#> 1813 1 0.000
#> 1814 1 1.000
#> 1815 1 0.000
#> 1816 1 0.000
#> 1817 1 0.000
#> 1818 1 0.000
#> 1819 1 0.000
#> 1820 1 0.000
#> 1821 3 0.000
#> 1822 1 0.000
#> 1823 1 1.000
#> 1824 1 0.000
#> 1825 1 0.000
#> 1826 1 0.000
#> 1827 1 0.000
#> 1828 1 0.000
#> 1829 1 0.000
#> 1830 2 0.000
#> 1831 1 0.000
#> 1832 1 0.000
#> 1833 3 0.249
#> 1834 1 0.249
#> 1835 1 0.747
#> 1836 2 0.000
#> 1837 2 0.000
#> 1838 1 0.000
#> 1839 1 0.000
#> 1840 1 0.000
#> 1841 1 0.000
#> 1842 1 0.000
#> 1843 2 0.249
#> 1844 1 0.000
#> 1845 1 0.000
#> 1846 1 0.000
#> 1847 1 0.000
#> 1848 1 0.000
#> 1849 1 0.249
#> 1850 1 0.000
#> 1851 1 0.000
#> 1852 3 0.000
#> 1853 1 0.498
#> 1854 1 0.000
#> 1855 1 1.000
#> 1856 1 0.000
#> 1857 1 1.000
#> 1858 1 0.747
#> 1859 3 0.000
#> 1860 1 0.000
#> 1861 3 0.000
#> 1862 1 0.000
#> 1863 1 0.000
#> 1864 3 0.000
#> 1865 2 1.000
#> 1866 3 0.000
#> 1867 3 1.000
#> 1868 3 0.498
#> 1869 2 0.000
#> 1870 1 0.000
#> 1871 1 0.000
#> 1872 3 1.000
#> 1873 3 0.751
#> 1874 1 0.249
#> 1875 1 1.000
#> 1876 1 0.249
#> 1877 2 0.000
#> 1878 1 0.000
#> 1879 1 0.498
#> 1880 1 0.000
#> 1881 2 0.000
#> 1882 1 1.000
#> 1883 1 0.000
#> 1884 1 0.000
#> 1885 1 1.000
#> 1886 3 0.751
#> 1887 1 0.000
#> 1888 3 0.000
#> 1889 1 1.000
#> 1890 1 1.000
#> 1891 1 1.000
#> 1892 2 0.000
#> 1893 1 0.000
#> 1894 1 0.000
#> 1895 1 0.000
#> 1896 3 1.000
#> 1897 1 0.000
#> 1898 1 1.000
#> 1899 1 0.000
#> 1900 3 0.000
#> 1901 1 1.000
#> 1902 3 0.000
#> 1903 1 1.000
#> 1904 3 1.000
#> 1905 1 0.249
#> 1906 1 0.000
#> 1907 3 0.000
#> 1908 1 1.000
#> 1909 1 1.000
#> 1910 1 0.000
#> 1911 1 1.000
#> 1912 1 1.000
#> 1913 1 1.000
#> 1914 1 0.000
#> 1915 3 0.000
#> 1916 1 1.000
#> 1917 1 0.000
#> 1918 1 0.751
#> 1919 1 0.000
#> 1920 3 0.000
#> 1921 2 1.000
#> 1922 1 1.000
#> 1923 3 0.000
#> 1924 3 0.000
#> 1925 1 0.498
#> 1926 3 0.000
#> 1927 1 1.000
#> 1928 1 0.249
#> 1929 1 1.000
#> 1930 2 0.000
#> 1931 3 1.000
#> 1932 1 1.000
#> 1933 2 0.000
#> 1934 1 1.000
#> 1935 3 0.249
#> 1936 2 0.000
#> 1937 3 0.000
#> 1938 1 0.751
#> 1939 1 1.000
#> 1940 3 0.000
#> 1941 3 0.000
#> 1942 1 0.000
#> 1943 3 1.000
#> 1944 1 1.000
#> 1945 3 0.000
#> 1946 2 0.000
#> 1947 3 0.000
#> 1948 3 1.000
#> 1949 1 1.000
#> 1950 3 0.000
#> 1951 3 1.000
#> 1952 1 1.000
#> 1953 1 0.000
#> 1954 2 0.502
#> 1955 1 1.000
#> 1956 1 1.000
#> 1957 2 0.000
#> 1958 3 0.000
#> 1959 2 0.747
#> 1960 3 1.000
#> 1961 2 0.498
#> 1962 3 0.000
#> 1963 3 1.000
#> 1964 2 0.249
#> 1965 1 1.000
#> 1966 3 0.000
#> 1967 2 0.000
#> 1968 3 0.000
#> 1969 3 1.000
#> 1970 3 0.000
#> 1971 1 1.000
#> 1972 2 0.000
#> 1973 3 0.000
#> 1974 3 0.000
#> 1975 2 0.000
#> 1976 3 0.000
#> 1977 1 1.000
#> 1978 2 0.498
#> 1979 3 1.000
#> 1980 2 0.502
#> 1981 3 0.000
#> 1982 3 0.000
#> 1983 3 1.000
#> 1984 2 0.000
#> 1985 2 0.000
#> 1986 2 0.000
#> 1987 2 0.000
#> 1988 2 0.000
#> 1989 2 0.000
#> 1990 2 0.000
#> 1991 2 0.000
#> 1992 2 0.000
#> 1993 2 0.000
#> 1994 2 0.000
#> 1995 2 0.000
#> 1996 2 0.000
#> 1997 2 0.000
#> 1998 2 0.000
#> 1999 2 0.000
#> 2000 2 0.000
#> 2001 2 0.000
#> 2002 2 0.000
#> 2003 2 0.000
#> 2004 2 0.000
#> 2005 2 0.000
#> 2006 2 0.000
#> 2007 2 0.000
#> 2008 2 0.498
#> 2009 2 0.000
#> 2010 2 0.000
#> 2011 2 0.000
#> 2012 2 0.000
#> 2013 2 0.502
#> 2014 2 0.000
#> 2015 1 0.000
#> 2016 2 0.000
#> 2017 2 0.000
#> 2018 3 1.000
#> 2019 2 0.498
#> 2020 2 0.000
#> 2021 2 0.502
#> 2022 2 0.502
#> 2023 2 1.000
#> 2024 2 0.751
#> 2025 2 1.000
#> 2026 3 0.249
#> 2027 2 0.000
#> 2028 2 1.000
#> 2029 2 1.000
#> 2030 2 0.000
#> 2031 2 1.000
#> 2032 2 1.000
#> 2033 2 0.502
#> 2034 2 0.000
#> 2035 2 0.000
#> 2036 2 0.000
#> 2037 2 0.000
#> 2038 2 0.498
#> 2039 2 0.000
#> 2040 2 0.000
#> 2041 2 1.000
#> 2042 3 1.000
#> 2043 2 0.000
#> 2044 2 0.000
#> 2045 2 0.000
#> 2046 2 0.000
#> 2047 2 0.000
#> 2048 2 0.000
#> 2049 2 0.000
#> 2050 1 0.000
#> 2051 2 0.000
#> 2052 2 0.000
#> 2053 2 0.000
#> 2054 2 0.000
#> 2055 2 0.000
#> 2056 2 0.000
#> 2057 2 0.000
#> 2058 2 0.000
#> 2059 3 1.000
#> 2060 2 0.000
#> 2061 2 0.000
#> 2062 2 1.000
#> 2063 2 0.000
#> 2064 2 0.000
#> 2065 2 0.000
#> 2066 2 0.000
#> 2067 2 0.000
#> 2068 2 0.249
#> 2069 2 0.000
#> 2070 2 0.000
#> 2071 2 0.000
#> 2072 2 0.000
#> 2073 2 0.000
#> 2074 2 0.751
#> 2075 2 0.000
#> 2076 2 0.000
#> 2077 2 0.000
#> 2078 2 0.751
#> 2079 2 0.000
#> 2080 2 0.000
#> 2081 2 0.000
#> 2082 2 1.000
#> 2083 2 0.000
#> 2084 2 0.000
#> 2085 2 0.000
#> 2086 2 0.000
#> 2087 2 0.000
#> 2088 2 0.000
#> 2089 2 0.000
#> 2090 2 0.000
#> 2091 2 0.000
#> 2092 2 0.000
#> 2093 2 0.000
#> 2094 2 0.498
#> 2095 2 0.000
#> 2096 2 0.000
#> 2097 2 0.000
#> 2098 2 0.502
#> 2099 2 0.249
#> 2100 2 1.000
#> 2101 2 0.000
#> 2102 2 0.000
#> 2103 2 0.000
#> 2104 2 0.000
#> 2105 2 0.000
#> 2106 2 0.000
#> 2107 2 0.000
#> 2108 2 0.000
#> 2109 2 0.000
#> 2110 2 0.000
#> 2111 2 0.000
#> 2112 2 0.000
#> 2113 2 0.000
#> 2114 2 0.000
#> 2115 2 0.000
#> 2116 2 0.000
#> 2117 2 0.000
#> 2118 2 1.000
#> 2119 2 0.000
#> 2120 2 0.000
#> 2121 2 0.000
#> 2122 2 0.000
#> 2123 2 0.000
#> 2124 2 0.000
#> 2125 1 0.751
#> 2126 2 0.000
#> 2127 2 0.000
#> 2128 1 1.000
#> 2129 2 0.000
#> 2130 2 0.000
#> 2131 2 0.000
#> 2132 2 0.249
#> 2133 2 0.000
#> 2134 2 0.000
#> 2135 2 0.000
#> 2136 2 0.000
#> 2137 2 0.000
#> 2138 2 0.000
#> 2139 2 0.000
#> 2140 2 0.000
#> 2141 2 0.000
#> 2142 2 0.751
#> 2143 2 0.498
#> 2144 2 0.000
#> 2145 2 0.000
#> 2146 2 0.000
#> 2147 2 0.498
#> 2148 2 0.000
#> 2149 2 0.249
#> 2150 2 0.000
#> 2151 2 0.000
#> 2152 2 0.498
#> 2153 2 0.000
#> 2154 2 0.000
#> 2155 2 1.000
#> 2156 2 0.000
#> 2157 2 0.000
#> 2158 2 0.751
#> 2159 2 0.249
#> 2160 2 0.000
#> 2161 2 0.000
#> 2162 2 0.000
#> 2163 2 0.000
#> 2164 2 0.249
#> 2165 2 0.000
#> 2166 2 0.000
#> 2167 2 0.000
#> 2168 2 0.000
#> 2169 2 0.000
#> 2170 2 0.000
#> 2171 2 0.000
#> 2172 2 0.000
#> 2173 2 0.000
#> 2174 3 1.000
#> 2175 2 0.498
#> 2176 2 0.000
#> 2177 2 0.000
#> 2178 2 0.000
#> 2179 2 0.000
#> 2180 2 0.000
#> 2181 2 0.000
#> 2182 2 0.000
#> 2183 2 0.000
#> 2184 2 0.498
#> 2185 2 0.000
#> 2186 2 1.000
#> 2187 2 0.000
#> 2188 2 0.000
#> 2189 2 0.249
#> 2190 1 1.000
#> 2191 2 0.000
#> 2192 1 1.000
#> 2193 2 0.000
#> 2194 3 0.000
#> 2195 2 0.000
#> 2196 3 1.000
#> 2197 2 1.000
#> 2198 2 0.000
#> 2199 2 0.000
#> 2200 2 0.000
#> 2201 2 0.000
#> 2202 2 0.000
#> 2203 2 0.000
#> 2204 2 0.000
#> 2205 2 0.000
#> 2206 2 0.000
#> 2207 2 0.000
#> 2208 2 0.000
#> 2209 2 0.000
#> 2210 2 0.000
#> 2211 2 0.000
#> 2212 2 0.000
#> 2213 2 0.000
#> 2214 2 0.000
#> 2215 2 0.253
#> 2216 2 0.747
#> 2217 2 0.000
#> 2218 2 0.000
#> 2219 2 0.000
#> 2220 2 0.000
#> 2221 2 0.000
#> 2222 2 0.000
#> 2223 2 0.000
#> 2224 2 0.000
#> 2225 2 0.000
#> 2226 2 0.000
#> 2227 2 0.000
#> 2228 2 0.000
#> 2229 2 0.000
#> 2230 2 0.000
#> 2231 2 0.000
#> 2232 2 0.000
#> 2233 2 0.249
#> 2234 2 0.000
#> 2235 2 0.000
#> 2236 2 0.000
#> 2237 3 1.000
#> 2238 2 0.000
#> 2239 2 0.000
#> 2240 3 1.000
#> 2241 2 0.498
#> 2242 3 1.000
#> 2243 2 0.502
#> 2244 2 0.000
#> 2245 2 0.000
#> 2246 2 0.000
#> 2247 2 0.000
#> 2248 2 1.000
#> 2249 2 1.000
#> 2250 2 0.000
#> 2251 2 0.000
#> 2252 3 1.000
#> 2253 2 0.000
#> 2254 2 0.000
#> 2255 2 0.000
#> 2256 2 0.000
#> 2257 2 0.000
#> 2258 2 0.000
#> 2259 1 1.000
#> 2260 3 1.000
#> 2261 2 0.000
#> 2262 2 0.000
#> 2263 2 0.000
#> 2264 2 0.000
#> 2265 2 0.000
#> 2266 3 1.000
#> 2267 2 0.000
#> 2268 2 0.000
#> 2269 3 1.000
#> 2270 2 0.000
#> 2271 2 0.000
#> 2272 2 0.000
#> 2273 1 0.498
#> 2274 2 0.253
#> 2275 1 0.253
#> 2276 2 0.000
#> 2277 2 0.000
#> 2278 3 1.000
#> 2279 2 0.498
#> 2280 2 0.000
#> 2281 2 0.000
#> 2282 2 0.000
#> 2283 1 0.000
#> 2284 2 0.000
#> 2285 2 0.000
#> 2286 2 0.000
#> 2287 1 0.000
#> 2288 3 1.000
#> 2289 3 1.000
#> 2290 2 0.000
#> 2291 2 0.000
#> 2292 3 1.000
#> 2293 2 0.000
#> 2294 2 0.000
#> 2295 1 0.000
#> 2296 2 0.000
#> 2297 2 0.000
#> 2298 2 1.000
#> 2299 1 0.000
#> 2300 3 1.000
#> 2301 1 0.000
#> 2302 1 0.000
#> 2303 1 0.000
#> 2304 2 0.253
#> 2305 1 0.000
#> 2306 2 0.747
#> 2307 1 1.000
#> 2308 1 0.000
#> 2309 1 0.000
#> 2310 2 0.000
#> 2311 1 0.000
#> 2312 3 1.000
#> 2313 2 0.000
#> 2314 1 0.000
#> 2315 1 0.000
#> 2316 1 0.000
#> 2317 1 0.000
#> 2318 2 0.000
#> 2319 2 0.000
#> 2320 2 0.000
#> 2321 1 0.253
#> 2322 2 0.000
#> 2323 2 0.000
#> 2324 3 1.000
#> 2325 2 0.000
#> 2326 2 0.000
#> 2327 2 0.000
#> 2328 2 0.000
#> 2329 2 0.000
#> 2330 2 0.000
#> 2331 2 0.000
#> 2332 2 0.249
#> 2333 2 0.000
#> 2334 2 0.000
#> 2335 3 0.249
#> 2336 1 0.000
#> 2337 1 0.000
#> 2338 3 1.000
#> 2339 2 0.000
#> 2340 1 0.000
#> 2341 3 0.498
#> 2342 3 1.000
#> 2343 2 0.000
#> 2344 2 0.747
#> 2345 2 0.000
#> 2346 2 0.000
#> 2347 2 0.000
#> 2348 2 0.000
#> 2349 2 0.000
#> 2350 2 0.000
#> 2351 3 1.000
#> 2352 2 1.000
#> 2353 2 0.253
#> 2354 2 0.000
#> 2355 2 0.000
#> 2356 2 0.000
#> 2357 3 1.000
#> 2358 2 0.000
#> 2359 2 0.000
#> 2360 2 0.000
#> 2361 2 0.000
#> 2362 2 0.000
#> 2363 2 0.000
#> 2364 2 0.000
#> 2365 2 0.000
#> 2366 2 0.000
#> 2367 2 0.000
#> 2368 2 0.000
#> 2369 2 0.000
#> 2370 2 0.000
#> 2371 2 0.000
#> 2372 2 0.000
#> 2373 2 0.000
#> 2374 2 0.000
#> 2375 2 0.000
#> 2376 2 0.000
#> 2377 2 0.000
#> 2378 2 0.000
#> 2379 2 0.249
#> 2380 2 0.000
#> 2381 2 0.000
#> 2382 2 0.000
#> 2383 2 0.000
#> 2384 2 0.000
#> 2385 2 0.000
#> 2386 2 0.000
#> 2387 2 0.000
#> 2388 2 0.000
#> 2389 2 0.249
#> 2390 2 1.000
#> 2391 2 0.000
#> 2392 2 0.000
#> 2393 2 0.000
#> 2394 2 0.000
#> 2395 2 0.000
#> 2396 2 0.000
#> 2397 2 0.502
#> 2398 2 0.751
#> 2399 3 1.000
#> 2400 2 0.000
#> 2401 1 0.249
#> 2402 1 1.000
#> 2403 1 0.000
#> 2404 2 0.000
#> 2405 2 0.000
#> 2406 2 0.000
#> 2407 2 0.000
#> 2408 2 0.000
#> 2409 2 0.249
#> 2410 2 0.751
#> 2411 2 0.000
#> 2412 2 0.000
#> 2413 2 0.249
#> 2414 1 1.000
#> 2415 2 0.000
#> 2416 2 0.000
#> 2417 2 0.000
#> 2418 2 0.000
#> 2419 2 0.000
#> 2420 1 0.000
#> 2421 2 0.000
#> 2422 2 0.000
#> 2423 3 1.000
#> 2424 2 1.000
#> 2425 2 0.000
#> 2426 2 0.000
#> 2427 2 0.000
#> 2428 2 0.000
#> 2429 2 0.249
#> 2430 2 0.249
#> 2431 2 0.000
#> 2432 2 0.000
#> 2433 1 0.000
#> 2434 3 1.000
#> 2435 1 0.000
#> 2436 2 0.000
#> 2437 3 1.000
#> 2438 3 1.000
#> 2439 3 1.000
#> 2440 1 0.000
#> 2441 1 1.000
#> 2442 1 0.000
#> 2443 3 1.000
#> 2444 3 1.000
#> 2445 3 1.000
#> 2446 2 0.249
#> 2447 2 0.000
#> 2448 2 0.000
#> 2449 2 0.000
#> 2450 2 0.000
#> 2451 2 0.498
#> 2452 2 0.751
#> 2453 2 0.747
#> 2454 2 0.000
#> 2455 2 0.249
#> 2456 2 0.747
#> 2457 2 0.000
#> 2458 2 1.000
#> 2459 2 0.000
#> 2460 2 0.000
#> 2461 2 0.249
#> 2462 2 1.000
#> 2463 2 0.000
#> 2464 2 1.000
#> 2465 2 0.249
#> 2466 2 0.000
#> 2467 2 0.751
#> 2468 2 0.747
#> 2469 2 0.000
#> 2470 2 0.000
#> 2471 3 0.747
#> 2472 2 0.000
#> 2473 2 0.000
#> 2474 2 0.000
#> 2475 2 0.249
#> 2476 2 1.000
#> 2477 2 0.000
#> 2478 2 0.249
#> 2479 2 0.249
#> 2480 2 1.000
#> 2481 2 0.000
#> 2482 2 0.249
#> 2483 2 1.000
#> 2484 2 0.249
#> 2485 2 0.747
#> 2486 2 0.751
#> 2487 2 0.747
#> 2488 2 1.000
#> 2489 2 1.000
#> 2490 2 1.000
#> 2491 2 1.000
#> 2492 2 0.000
#> 2493 2 0.000
#> 2494 2 0.000
#> 2495 2 0.249
#> 2496 2 0.000
#> 2497 2 0.000
#> 2498 2 0.000
#> 2499 2 0.249
#> 2500 2 0.000
#> 2501 2 0.000
#> 2502 2 0.000
#> 2503 2 0.000
#> 2504 2 0.000
#> 2505 2 0.000
#> 2506 2 0.000
#> 2507 2 0.000
#> 2508 3 1.000
#> 2509 3 0.751
#> 2510 2 0.000
#> 2511 3 1.000
#> 2512 2 0.000
#> 2513 2 1.000
#> 2514 2 0.000
#> 2515 2 0.000
#> 2516 2 0.000
#> 2517 2 0.498
#> 2518 2 0.000
#> 2519 2 0.249
#> 2520 2 0.000
#> 2521 2 0.000
#> 2522 2 0.000
#> 2523 2 0.498
#> 2524 2 0.498
#> 2525 2 0.000
#> 2526 2 0.000
#> 2527 2 0.000
#> 2528 2 0.249
#> 2529 2 1.000
#> 2530 2 0.498
#> 2531 2 0.000
#> 2532 2 0.000
#> 2533 2 0.000
#> 2534 1 1.000
#> 2535 2 0.000
#> 2536 3 0.253
#> 2537 2 0.000
#> 2538 2 0.000
#> 2539 2 0.000
#> 2540 2 0.000
#> 2541 2 0.000
#> 2542 2 0.000
#> 2543 2 0.000
#> 2544 2 0.000
#> 2545 2 0.000
#> 2546 2 0.000
#> 2547 2 0.000
#> 2548 2 0.751
#> 2549 1 0.000
#> 2550 2 0.000
#> 2551 3 1.000
#> 2552 3 1.000
#> 2553 1 0.000
#> 2554 1 0.000
#> 2555 2 0.249
#> 2556 3 0.751
#> 2557 2 0.498
#> 2558 1 0.498
#> 2559 2 0.000
#> 2560 2 0.000
#> 2561 3 0.502
#> 2562 2 0.498
#> 2563 3 1.000
#> 2564 2 0.000
#> 2565 2 0.000
#> 2566 3 0.000
#> 2567 2 0.000
#> 2568 2 0.000
#> 2569 2 0.000
#> 2570 2 0.253
#> 2571 2 0.000
#> 2572 1 0.000
#> 2573 1 0.000
#> 2574 1 0.000
#> 2575 3 1.000
#> 2576 3 1.000
#> 2577 2 0.000
#> 2578 2 0.000
#> 2579 2 0.000
#> 2580 2 0.000
#> 2581 2 0.000
#> 2582 2 0.000
#> 2583 2 0.000
#> 2584 2 0.000
#> 2585 2 0.000
#> 2586 2 0.000
#> 2587 2 0.000
#> 2588 1 1.000
#> 2589 2 0.000
#> 2590 2 0.000
#> 2591 2 0.000
#> 2592 2 0.000
#> 2593 2 0.000
#> 2594 2 0.000
#> 2595 2 0.000
#> 2596 2 0.000
#> 2597 2 0.000
#> 2598 2 0.000
#> 2599 2 0.000
#> 2600 2 0.000
#> 2601 2 0.000
#> 2602 2 0.000
#> 2603 2 0.000
#> 2604 2 0.000
#> 2605 2 0.000
#> 2606 2 0.000
#> 2607 2 0.000
#> 2608 2 0.000
#> 2609 3 1.000
#> 2610 1 1.000
#> 2611 2 0.000
#> 2612 2 0.000
#> 2613 2 0.000
#> 2614 2 0.000
#> 2615 2 0.000
#> 2616 3 1.000
#> 2617 2 0.000
#> 2618 2 0.000
#> 2619 2 1.000
#> 2620 2 0.000
#> 2621 2 0.000
#> 2622 2 0.000
#> 2623 1 0.000
#> 2624 2 0.000
#> 2625 2 0.000
#> 2626 2 1.000
#> 2627 1 0.000
#> 2628 1 0.000
#> 2629 1 0.000
#> 2630 2 0.751
#> 2631 2 0.000
#> 2632 2 0.000
#> 2633 2 0.000
#> 2634 2 0.000
#> 2635 2 0.000
#> 2636 2 0.000
#> 2637 2 0.751
#> 2638 2 0.000
#> 2639 2 1.000
#> 2640 2 0.751
#> 2641 2 0.000
#> 2642 3 1.000
#> 2643 2 0.751
#> 2644 2 0.000
#> 2645 2 0.000
#> 2646 2 0.000
#> 2647 2 0.000
#> 2648 2 0.000
#> 2649 3 1.000
#> 2650 2 0.000
#> 2651 1 0.000
#> 2652 2 0.000
#> 2653 2 0.000
#> 2654 2 0.498
#> 2655 2 0.249
#> 2656 2 0.000
#> 2657 2 0.000
#> 2658 2 0.000
#> 2659 2 0.249
#> 2660 2 0.000
#> 2661 2 0.000
#> 2662 2 0.000
#> 2663 2 0.000
#> 2664 3 1.000
#> 2665 3 1.000
#> 2666 2 0.000
#> 2667 3 1.000
#> 2668 2 0.000
#> 2669 2 0.000
#> 2670 3 1.000
#> 2671 2 0.000
#> 2672 1 0.000
#> 2673 3 1.000
#> 2674 2 1.000
#> 2675 3 1.000
#> 2676 2 0.751
#> 2677 2 0.000
#> 2678 3 0.751
#> 2679 1 0.000
#> 2680 1 0.000
#> 2681 1 0.000
#> 2682 2 0.000
#> 2683 1 0.000
#> 2684 1 0.000
#> 2685 1 0.000
#> 2686 2 0.000
#> 2687 2 0.000
#> 2688 2 0.000
#> 2689 2 0.000
#> 2690 3 1.000
#> 2691 2 0.498
#> 2692 2 0.000
#> 2693 2 0.000
#> 2694 2 0.000
#> 2695 1 0.000
#> 2696 2 0.000
#> 2697 3 0.000
#> 2698 2 0.498
#> 2699 2 0.000
#> 2700 3 1.000
#> 2701 2 0.498
#> 2702 2 0.000
#> 2703 1 0.000
#> 2704 2 0.502
#> 2705 2 0.747
#> 2706 3 0.498
#> 2707 1 0.000
#> 2708 2 1.000
#> 2709 1 0.000
#> 2710 1 0.000
#> 2711 2 1.000
#> 2712 1 0.000
#> 2713 1 0.000
#> 2714 2 0.000
#> 2715 2 0.249
#> 2716 2 0.000
#> 2717 1 0.000
#> 2718 1 0.000
#> 2719 2 0.249
#> 2720 1 0.000
#> 2721 2 0.000
#> 2722 2 1.000
#> 2723 1 0.000
#> 2724 2 0.000
#> 2725 3 1.000
#> 2726 1 0.000
#> 2727 3 1.000
#> 2728 1 1.000
#> 2729 1 0.000
#> 2730 3 1.000
#> 2731 2 0.502
#> 2732 2 0.498
#> 2733 3 1.000
#> 2734 2 0.000
#> 2735 3 1.000
#> 2736 1 0.249
#> 2737 1 0.000
#> 2738 3 1.000
#> 2739 1 1.000
#> 2740 3 1.000
#> 2741 1 0.000
#> 2742 1 0.751
#> 2743 2 0.000
#> 2744 2 0.747
#> 2745 2 0.000
#> 2746 1 0.249
#> 2747 1 0.000
#> 2748 1 0.000
#> 2749 1 0.751
#> 2750 3 1.000
#> 2751 1 0.000
#> 2752 1 0.000
#> 2753 2 0.000
#> 2754 2 0.000
#> 2755 3 1.000
#> 2756 3 1.000
#> 2757 1 0.000
#> 2758 2 0.249
#> 2759 2 0.498
#> 2760 2 0.747
#> 2761 2 0.498
#> 2762 2 0.751
#> 2763 2 0.747
#> 2764 2 0.747
#> 2765 2 0.000
#> 2766 1 0.000
#> 2767 1 0.502
#> 2768 2 0.000
#> 2769 2 0.000
#> 2770 2 0.000
#> 2771 2 0.498
#> 2772 2 0.000
#> 2773 2 0.249
#> 2774 2 0.249
#> 2775 2 0.498
#> 2776 2 1.000
#> 2777 2 0.249
#> 2778 2 0.498
#> 2779 2 0.000
#> 2780 2 1.000
#> 2781 2 0.498
#> 2782 2 0.000
#> 2783 2 0.000
#> 2784 2 0.249
#> 2785 2 0.000
#> 2786 2 0.000
#> 2787 2 0.000
#> 2788 2 0.000
#> 2789 2 0.000
#> 2790 1 0.000
#> 2791 1 0.000
#> 2792 2 0.000
#> 2793 2 0.000
#> 2794 2 0.000
#> 2795 2 1.000
#> 2796 2 1.000
#> 2797 2 0.249
#> 2798 2 0.000
#> 2799 2 0.498
#> 2800 2 0.249
#> 2801 2 0.000
#> 2802 2 1.000
#> 2803 2 0.000
#> 2804 2 0.498
#> 2805 2 0.000
#> 2806 2 0.000
#> 2807 2 0.000
#> 2808 2 0.000
#> 2809 2 0.000
#> 2810 2 0.751
#> 2811 2 0.498
#> 2812 2 0.000
#> 2813 2 1.000
#> 2814 2 0.000
#> 2815 2 0.498
#> 2816 2 0.747
#> 2817 2 0.000
#> 2818 1 0.000
#> 2819 1 0.249
#> 2820 1 0.000
#> 2821 2 0.000
#> 2822 2 0.000
#> 2823 2 0.000
#> 2824 2 0.747
#> 2825 2 0.751
#> 2826 2 0.249
#> 2827 2 0.249
#> 2828 2 1.000
#> 2829 2 0.249
#> 2830 2 0.498
#> 2831 2 1.000
#> 2832 2 0.000
#> 2833 2 1.000
#> 2834 2 0.000
#> 2835 2 0.000
#> 2836 2 0.000
#> 2837 2 0.000
#> 2838 2 0.000
#> 2839 2 0.000
#> 2840 2 0.249
#> 2841 2 0.000
#> 2842 2 0.000
#> 2843 2 0.000
#> 2844 2 0.000
#> 2845 2 0.000
#> 2846 2 0.000
#> 2847 2 0.249
#> 2848 2 0.000
#> 2849 2 0.249
#> 2850 2 0.000
#> 2851 2 0.498
#> 2852 2 0.000
#> 2853 3 1.000
#> 2854 2 0.000
#> 2855 1 0.000
#> 2856 2 1.000
#> 2857 2 0.747
#> 2858 1 0.000
#> 2859 2 0.000
#> 2860 2 0.000
#> 2861 2 0.000
#> 2862 2 0.000
#> 2863 2 0.000
#> 2864 2 0.000
#> 2865 2 0.747
#> 2866 2 0.498
#> 2867 2 0.000
#> 2868 2 0.000
#> 2869 2 0.000
#> 2870 2 0.000
#> 2871 2 0.000
#> 2872 2 0.000
#> 2873 2 1.000
#> 2874 2 0.000
#> 2875 2 0.249
#> 2876 2 0.000
#> 2877 2 0.000
#> 2878 2 0.249
#> 2879 1 0.000
#> 2880 2 0.000
#> 2881 2 0.000
get_classes(res, k = 4)
#> class p
#> 1 1 0.000
#> 2 1 0.253
#> 3 2 1.000
#> 4 3 1.000
#> 5 1 1.000
#> 6 1 0.000
#> 7 3 0.000
#> 8 1 1.000
#> 9 1 1.000
#> 10 1 0.000
#> 11 1 1.000
#> 12 1 1.000
#> 13 3 1.000
#> 14 1 0.502
#> 15 1 1.000
#> 16 1 1.000
#> 17 1 1.000
#> 18 1 1.000
#> 19 3 0.000
#> 20 1 1.000
#> 21 3 1.000
#> 22 1 0.000
#> 23 1 1.000
#> 24 3 0.000
#> 25 1 1.000
#> 26 3 1.000
#> 27 3 0.000
#> 28 3 0.000
#> 29 1 0.000
#> 30 3 0.000
#> 31 3 0.000
#> 32 1 1.000
#> 33 3 0.000
#> 34 3 1.000
#> 35 3 1.000
#> 36 1 1.000
#> 37 1 1.000
#> 38 1 1.000
#> 39 3 0.000
#> 40 3 0.000
#> 41 1 0.751
#> 42 2 0.498
#> 43 1 1.000
#> 44 3 0.000
#> 45 1 1.000
#> 46 1 1.000
#> 47 3 0.000
#> 48 3 0.000
#> 49 3 0.000
#> 50 1 0.000
#> 51 3 0.000
#> 52 1 1.000
#> 53 1 1.000
#> 54 1 1.000
#> 55 1 0.000
#> 56 2 1.000
#> 57 2 1.000
#> 58 1 1.000
#> 59 1 1.000
#> 60 3 0.000
#> 61 3 0.000
#> 62 1 0.747
#> 63 1 1.000
#> 64 2 0.751
#> 65 3 0.000
#> 66 1 1.000
#> 67 1 0.000
#> 68 1 0.000
#> 69 3 0.000
#> 70 1 0.249
#> 71 4 0.000
#> 72 3 1.000
#> 73 3 0.000
#> 74 1 1.000
#> 75 1 1.000
#> 76 1 1.000
#> 77 1 1.000
#> 78 1 1.000
#> 79 2 1.000
#> 80 1 1.000
#> 81 4 0.000
#> 82 1 1.000
#> 83 1 1.000
#> 84 4 0.000
#> 85 1 0.751
#> 86 2 1.000
#> 87 1 1.000
#> 88 1 1.000
#> 89 1 1.000
#> 90 1 0.751
#> 91 3 0.249
#> 92 1 1.000
#> 93 2 1.000
#> 94 1 0.000
#> 95 1 0.751
#> 96 1 1.000
#> 97 1 1.000
#> 98 1 1.000
#> 99 4 0.000
#> 100 3 1.000
#> 101 1 1.000
#> 102 1 1.000
#> 103 1 0.000
#> 104 1 1.000
#> 105 1 0.000
#> 106 3 1.000
#> 107 2 0.249
#> 108 1 1.000
#> 109 4 0.000
#> 110 1 1.000
#> 111 1 1.000
#> 112 1 1.000
#> 113 1 1.000
#> 114 4 0.000
#> 115 1 1.000
#> 116 2 0.000
#> 117 2 1.000
#> 118 3 1.000
#> 119 1 1.000
#> 120 3 0.498
#> 121 3 1.000
#> 122 3 0.000
#> 123 2 0.000
#> 124 2 0.751
#> 125 2 1.000
#> 126 2 1.000
#> 127 1 1.000
#> 128 1 0.000
#> 129 1 0.000
#> 130 1 1.000
#> 131 1 0.000
#> 132 1 1.000
#> 133 1 0.751
#> 134 4 0.751
#> 135 1 1.000
#> 136 2 0.000
#> 137 1 1.000
#> 138 1 1.000
#> 139 3 1.000
#> 140 1 0.000
#> 141 1 0.249
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 2 0.249
#> 147 1 1.000
#> 148 2 1.000
#> 149 1 1.000
#> 150 3 0.751
#> 151 4 1.000
#> 152 3 0.000
#> 153 1 1.000
#> 154 4 1.000
#> 155 3 0.000
#> 156 1 1.000
#> 157 3 0.000
#> 158 2 1.000
#> 159 3 0.253
#> 160 3 1.000
#> 161 3 1.000
#> 162 1 1.000
#> 163 3 0.000
#> 164 3 1.000
#> 165 2 1.000
#> 166 1 1.000
#> 167 3 0.000
#> 168 3 0.000
#> 169 3 1.000
#> 170 1 1.000
#> 171 3 0.000
#> 172 3 0.000
#> 173 3 0.000
#> 174 2 1.000
#> 175 2 1.000
#> 176 3 0.000
#> 177 3 0.000
#> 178 1 1.000
#> 179 3 0.000
#> 180 3 0.000
#> 181 3 0.000
#> 182 3 0.000
#> 183 3 1.000
#> 184 3 0.000
#> 185 3 1.000
#> 186 3 0.000
#> 187 1 1.000
#> 188 3 0.000
#> 189 3 0.000
#> 190 2 1.000
#> 191 3 1.000
#> 192 1 1.000
#> 193 3 0.000
#> 194 3 0.000
#> 195 3 0.000
#> 196 1 1.000
#> 197 3 0.000
#> 198 3 0.000
#> 199 3 0.000
#> 200 3 0.000
#> 201 3 0.000
#> 202 3 1.000
#> 203 3 0.000
#> 204 3 0.000
#> 205 3 0.000
#> 206 3 0.000
#> 207 1 1.000
#> 208 3 0.000
#> 209 3 1.000
#> 210 2 1.000
#> 211 3 0.000
#> 212 4 0.000
#> 213 3 0.000
#> 214 3 0.000
#> 215 3 0.751
#> 216 3 0.000
#> 217 3 1.000
#> 218 2 1.000
#> 219 3 0.000
#> 220 1 1.000
#> 221 4 0.249
#> 222 3 0.000
#> 223 3 0.000
#> 224 3 0.000
#> 225 3 0.000
#> 226 3 0.000
#> 227 3 0.000
#> 228 3 0.000
#> 229 1 1.000
#> 230 2 1.000
#> 231 3 0.000
#> 232 3 1.000
#> 233 3 0.000
#> 234 3 0.000
#> 235 2 1.000
#> 236 3 0.000
#> 237 3 0.000
#> 238 3 0.000
#> 239 3 0.000
#> 240 3 0.000
#> 241 3 0.000
#> 242 3 1.000
#> 243 3 0.000
#> 244 1 1.000
#> 245 3 0.000
#> 246 3 0.000
#> 247 3 0.000
#> 248 2 1.000
#> 249 3 0.000
#> 250 3 0.000
#> 251 1 1.000
#> 252 3 0.000
#> 253 3 0.751
#> 254 3 0.000
#> 255 3 0.498
#> 256 3 0.000
#> 257 1 1.000
#> 258 3 0.000
#> 259 3 0.000
#> 260 3 0.000
#> 261 1 0.747
#> 262 3 1.000
#> 263 1 1.000
#> 264 1 1.000
#> 265 1 1.000
#> 266 3 0.000
#> 267 1 1.000
#> 268 1 1.000
#> 269 4 0.498
#> 270 3 0.000
#> 271 1 1.000
#> 272 1 1.000
#> 273 1 1.000
#> 274 3 0.000
#> 275 1 1.000
#> 276 1 1.000
#> 277 3 0.000
#> 278 3 0.000
#> 279 3 1.000
#> 280 3 0.000
#> 281 2 1.000
#> 282 1 1.000
#> 283 4 0.000
#> 284 3 1.000
#> 285 3 0.000
#> 286 2 1.000
#> 287 3 0.000
#> 288 3 0.000
#> 289 3 0.000
#> 290 3 0.253
#> 291 3 0.000
#> 292 3 0.000
#> 293 3 0.000
#> 294 3 0.000
#> 295 3 0.000
#> 296 3 0.000
#> 297 3 0.000
#> 298 3 0.000
#> 299 2 1.000
#> 300 3 0.000
#> 301 2 0.000
#> 302 3 1.000
#> 303 3 0.000
#> 304 2 0.249
#> 305 1 1.000
#> 306 2 0.751
#> 307 2 1.000
#> 308 3 0.000
#> 309 2 1.000
#> 310 3 0.000
#> 311 3 0.000
#> 312 3 0.000
#> 313 2 1.000
#> 314 3 0.000
#> 315 3 0.000
#> 316 3 0.000
#> 317 1 0.000
#> 318 3 0.502
#> 319 3 1.000
#> 320 1 1.000
#> 321 3 0.000
#> 322 1 1.000
#> 323 3 0.000
#> 324 3 1.000
#> 325 3 0.000
#> 326 1 0.747
#> 327 3 1.000
#> 328 3 0.000
#> 329 3 0.000
#> 330 3 0.000
#> 331 3 0.000
#> 332 1 1.000
#> 333 1 0.249
#> 334 1 1.000
#> 335 1 1.000
#> 336 2 0.000
#> 337 2 0.000
#> 338 1 1.000
#> 339 3 1.000
#> 340 1 1.000
#> 341 3 0.000
#> 342 3 0.000
#> 343 2 0.000
#> 344 3 0.000
#> 345 3 0.000
#> 346 3 0.000
#> 347 2 0.751
#> 348 4 1.000
#> 349 4 1.000
#> 350 2 0.502
#> 351 3 1.000
#> 352 1 1.000
#> 353 3 0.000
#> 354 1 1.000
#> 355 3 0.000
#> 356 1 1.000
#> 357 3 0.000
#> 358 3 0.000
#> 359 3 0.000
#> 360 3 0.000
#> 361 4 1.000
#> 362 3 0.000
#> 363 3 0.000
#> 364 3 0.000
#> 365 4 1.000
#> 366 3 0.000
#> 367 3 1.000
#> 368 3 0.000
#> 369 3 0.000
#> 370 3 0.000
#> 371 3 1.000
#> 372 3 0.000
#> 373 3 0.000
#> 374 3 0.000
#> 375 3 0.000
#> 376 3 0.000
#> 377 3 0.000
#> 378 3 0.000
#> 379 3 0.000
#> 380 3 0.000
#> 381 1 1.000
#> 382 1 1.000
#> 383 2 1.000
#> 384 4 0.000
#> 385 1 1.000
#> 386 1 1.000
#> 387 1 1.000
#> 388 3 0.000
#> 389 4 0.000
#> 390 4 0.249
#> 391 1 1.000
#> 392 3 1.000
#> 393 1 1.000
#> 394 4 0.253
#> 395 2 0.498
#> 396 3 0.000
#> 397 3 0.000
#> 398 1 1.000
#> 399 1 1.000
#> 400 1 0.747
#> 401 3 0.000
#> 402 1 0.751
#> 403 4 0.000
#> 404 1 1.000
#> 405 1 1.000
#> 406 2 1.000
#> 407 1 1.000
#> 408 3 0.751
#> 409 1 1.000
#> 410 1 1.000
#> 411 3 0.000
#> 412 3 0.000
#> 413 2 1.000
#> 414 1 1.000
#> 415 1 1.000
#> 416 1 1.000
#> 417 1 1.000
#> 418 1 1.000
#> 419 4 1.000
#> 420 1 1.000
#> 421 4 1.000
#> 422 1 0.747
#> 423 1 1.000
#> 424 3 0.000
#> 425 2 1.000
#> 426 1 1.000
#> 427 1 1.000
#> 428 1 1.000
#> 429 1 1.000
#> 430 1 1.000
#> 431 1 1.000
#> 432 2 1.000
#> 433 1 1.000
#> 434 2 1.000
#> 435 1 0.000
#> 436 2 1.000
#> 437 1 1.000
#> 438 2 1.000
#> 439 3 1.000
#> 440 2 1.000
#> 441 2 1.000
#> 442 3 1.000
#> 443 3 1.000
#> 444 3 1.000
#> 445 3 1.000
#> 446 1 0.000
#> 447 1 1.000
#> 448 3 0.000
#> 449 3 0.000
#> 450 3 0.000
#> 451 3 1.000
#> 452 1 1.000
#> 453 3 1.000
#> 454 1 1.000
#> 455 1 1.000
#> 456 3 1.000
#> 457 1 1.000
#> 458 1 1.000
#> 459 3 0.000
#> 460 3 0.000
#> 461 3 0.000
#> 462 3 0.000
#> 463 1 1.000
#> 464 3 1.000
#> 465 3 0.000
#> 466 3 0.000
#> 467 3 0.000
#> 468 3 0.000
#> 469 3 1.000
#> 470 3 0.000
#> 471 3 0.000
#> 472 3 0.000
#> 473 3 0.000
#> 474 3 0.000
#> 475 3 0.000
#> 476 3 0.000
#> 477 3 1.000
#> 478 3 0.000
#> 479 3 0.000
#> 480 3 0.000
#> 481 3 0.000
#> 482 3 1.000
#> 483 3 0.000
#> 484 1 1.000
#> 485 2 0.747
#> 486 3 0.000
#> 487 3 1.000
#> 488 3 0.000
#> 489 2 1.000
#> 490 2 1.000
#> 491 2 1.000
#> 492 3 0.000
#> 493 3 0.000
#> 494 2 1.000
#> 495 3 0.000
#> 496 1 1.000
#> 497 2 1.000
#> 498 2 0.502
#> 499 1 1.000
#> 500 3 0.000
#> 501 2 1.000
#> 502 1 1.000
#> 503 3 0.000
#> 504 3 0.000
#> 505 3 0.000
#> 506 2 0.000
#> 507 1 1.000
#> 508 1 1.000
#> 509 3 1.000
#> 510 3 0.000
#> 511 3 0.000
#> 512 2 1.000
#> 513 3 1.000
#> 514 3 1.000
#> 515 1 1.000
#> 516 1 1.000
#> 517 2 1.000
#> 518 3 0.000
#> 519 3 0.000
#> 520 3 0.000
#> 521 1 0.000
#> 522 1 1.000
#> 523 1 1.000
#> 524 3 0.000
#> 525 3 0.000
#> 526 3 0.000
#> 527 3 0.000
#> 528 1 1.000
#> 529 1 0.249
#> 530 4 0.000
#> 531 2 1.000
#> 532 2 1.000
#> 533 1 1.000
#> 534 2 0.751
#> 535 1 0.751
#> 536 1 1.000
#> 537 1 0.249
#> 538 2 0.747
#> 539 1 0.000
#> 540 3 1.000
#> 541 1 1.000
#> 542 2 1.000
#> 543 1 0.000
#> 544 1 1.000
#> 545 2 1.000
#> 546 1 0.000
#> 547 1 0.751
#> 548 1 1.000
#> 549 3 1.000
#> 550 1 1.000
#> 551 1 1.000
#> 552 1 1.000
#> 553 1 0.498
#> 554 1 1.000
#> 555 1 1.000
#> 556 4 0.000
#> 557 1 1.000
#> 558 1 1.000
#> 559 3 0.000
#> 560 4 0.249
#> 561 3 0.249
#> 562 1 1.000
#> 563 3 0.747
#> 564 4 0.249
#> 565 1 1.000
#> 566 1 1.000
#> 567 1 1.000
#> 568 2 0.751
#> 569 3 0.000
#> 570 1 1.000
#> 571 1 0.000
#> 572 2 0.751
#> 573 3 0.000
#> 574 3 0.000
#> 575 1 1.000
#> 576 3 0.000
#> 577 1 1.000
#> 578 1 1.000
#> 579 1 1.000
#> 580 2 0.000
#> 581 1 1.000
#> 582 1 1.000
#> 583 1 1.000
#> 584 4 0.000
#> 585 3 0.000
#> 586 1 1.000
#> 587 4 0.000
#> 588 1 1.000
#> 589 1 0.000
#> 590 4 1.000
#> 591 1 1.000
#> 592 3 1.000
#> 593 1 1.000
#> 594 3 0.000
#> 595 3 0.000
#> 596 4 0.000
#> 597 1 1.000
#> 598 3 1.000
#> 599 1 1.000
#> 600 1 0.751
#> 601 1 0.000
#> 602 4 0.000
#> 603 1 1.000
#> 604 3 0.000
#> 605 1 1.000
#> 606 1 1.000
#> 607 2 0.000
#> 608 1 0.253
#> 609 1 1.000
#> 610 1 1.000
#> 611 1 0.000
#> 612 1 0.502
#> 613 1 1.000
#> 614 1 1.000
#> 615 1 0.000
#> 616 2 1.000
#> 617 3 0.000
#> 618 3 1.000
#> 619 1 1.000
#> 620 3 1.000
#> 621 1 1.000
#> 622 3 0.000
#> 623 1 1.000
#> 624 3 0.000
#> 625 1 1.000
#> 626 3 0.000
#> 627 3 0.000
#> 628 3 0.000
#> 629 3 0.000
#> 630 3 1.000
#> 631 1 1.000
#> 632 1 1.000
#> 633 3 0.000
#> 634 1 1.000
#> 635 3 0.000
#> 636 2 0.249
#> 637 3 0.751
#> 638 1 1.000
#> 639 1 1.000
#> 640 1 0.751
#> 641 1 1.000
#> 642 3 0.000
#> 643 3 0.249
#> 644 1 1.000
#> 645 3 1.000
#> 646 3 0.000
#> 647 1 1.000
#> 648 1 1.000
#> 649 2 0.498
#> 650 1 1.000
#> 651 1 1.000
#> 652 1 1.000
#> 653 1 1.000
#> 654 1 1.000
#> 655 3 0.000
#> 656 1 1.000
#> 657 1 1.000
#> 658 1 1.000
#> 659 3 1.000
#> 660 3 1.000
#> 661 3 0.000
#> 662 1 1.000
#> 663 2 1.000
#> 664 1 1.000
#> 665 1 1.000
#> 666 3 0.000
#> 667 3 0.000
#> 668 3 1.000
#> 669 3 1.000
#> 670 1 1.000
#> 671 2 0.253
#> 672 1 1.000
#> 673 2 0.498
#> 674 3 1.000
#> 675 3 0.000
#> 676 4 0.000
#> 677 3 0.000
#> 678 3 1.000
#> 679 2 0.253
#> 680 1 1.000
#> 681 3 0.498
#> 682 3 1.000
#> 683 3 0.000
#> 684 3 0.747
#> 685 3 0.000
#> 686 3 0.000
#> 687 3 0.000
#> 688 3 0.000
#> 689 3 0.000
#> 690 3 0.000
#> 691 3 0.000
#> 692 1 1.000
#> 693 3 0.000
#> 694 3 0.000
#> 695 2 0.000
#> 696 3 0.000
#> 697 3 1.000
#> 698 1 1.000
#> 699 3 0.000
#> 700 4 0.000
#> 701 3 0.000
#> 702 1 1.000
#> 703 1 1.000
#> 704 3 0.000
#> 705 3 0.000
#> 706 2 1.000
#> 707 2 0.000
#> 708 1 1.000
#> 709 1 1.000
#> 710 4 0.000
#> 711 3 1.000
#> 712 2 0.000
#> 713 1 0.253
#> 714 1 1.000
#> 715 1 1.000
#> 716 1 1.000
#> 717 1 0.747
#> 718 1 1.000
#> 719 2 1.000
#> 720 1 1.000
#> 721 3 1.000
#> 722 2 1.000
#> 723 2 1.000
#> 724 1 1.000
#> 725 2 1.000
#> 726 1 1.000
#> 727 3 1.000
#> 728 3 0.000
#> 729 1 1.000
#> 730 1 1.000
#> 731 3 0.000
#> 732 1 1.000
#> 733 1 0.751
#> 734 3 0.000
#> 735 1 1.000
#> 736 1 1.000
#> 737 1 0.498
#> 738 1 1.000
#> 739 1 1.000
#> 740 3 1.000
#> 741 1 1.000
#> 742 1 1.000
#> 743 1 1.000
#> 744 3 0.000
#> 745 3 1.000
#> 746 1 0.751
#> 747 3 1.000
#> 748 3 0.000
#> 749 3 0.000
#> 750 3 0.000
#> 751 3 0.000
#> 752 1 1.000
#> 753 1 1.000
#> 754 1 1.000
#> 755 1 1.000
#> 756 1 1.000
#> 757 3 1.000
#> 758 1 1.000
#> 759 1 1.000
#> 760 3 0.000
#> 761 2 1.000
#> 762 2 1.000
#> 763 1 1.000
#> 764 1 1.000
#> 765 3 1.000
#> 766 1 1.000
#> 767 1 1.000
#> 768 1 1.000
#> 769 1 1.000
#> 770 1 0.000
#> 771 3 0.000
#> 772 1 1.000
#> 773 1 1.000
#> 774 3 0.751
#> 775 1 1.000
#> 776 2 0.249
#> 777 1 0.751
#> 778 1 1.000
#> 779 2 1.000
#> 780 1 1.000
#> 781 3 1.000
#> 782 1 1.000
#> 783 1 1.000
#> 784 1 1.000
#> 785 1 1.000
#> 786 1 1.000
#> 787 3 0.000
#> 788 1 0.000
#> 789 3 1.000
#> 790 3 0.000
#> 791 1 1.000
#> 792 1 1.000
#> 793 3 0.000
#> 794 2 0.498
#> 795 1 1.000
#> 796 3 0.000
#> 797 1 1.000
#> 798 1 0.502
#> 799 2 1.000
#> 800 1 1.000
#> 801 3 0.000
#> 802 2 0.000
#> 803 3 0.000
#> 804 3 1.000
#> 805 3 0.751
#> 806 2 1.000
#> 807 3 1.000
#> 808 1 1.000
#> 809 3 1.000
#> 810 1 0.751
#> 811 1 1.000
#> 812 1 1.000
#> 813 3 0.000
#> 814 3 0.249
#> 815 1 0.000
#> 816 4 0.000
#> 817 1 1.000
#> 818 1 1.000
#> 819 1 1.000
#> 820 2 0.000
#> 821 1 0.000
#> 822 1 0.751
#> 823 1 0.000
#> 824 1 0.502
#> 825 1 0.751
#> 826 1 1.000
#> 827 1 0.751
#> 828 1 1.000
#> 829 1 1.000
#> 830 1 1.000
#> 831 1 0.747
#> 832 1 0.000
#> 833 1 0.498
#> 834 1 0.000
#> 835 1 0.000
#> 836 1 0.000
#> 837 1 0.498
#> 838 1 0.000
#> 839 1 0.000
#> 840 1 0.000
#> 841 1 1.000
#> 842 1 0.249
#> 843 1 0.000
#> 844 1 0.000
#> 845 1 0.000
#> 846 1 0.751
#> 847 1 1.000
#> 848 1 0.000
#> 849 4 0.253
#> 850 1 0.751
#> 851 4 1.000
#> 852 2 1.000
#> 853 3 0.751
#> 854 2 0.000
#> 855 1 0.000
#> 856 4 0.000
#> 857 4 0.000
#> 858 4 0.000
#> 859 4 0.253
#> 860 1 0.498
#> 861 1 1.000
#> 862 4 1.000
#> 863 1 0.249
#> 864 1 0.000
#> 865 1 0.253
#> 866 1 0.000
#> 867 1 0.000
#> 868 1 0.000
#> 869 1 0.751
#> 870 3 0.000
#> 871 1 1.000
#> 872 1 1.000
#> 873 1 0.747
#> 874 1 0.000
#> 875 1 1.000
#> 876 1 0.498
#> 877 4 0.000
#> 878 1 1.000
#> 879 1 0.498
#> 880 1 0.000
#> 881 1 0.253
#> 882 1 0.751
#> 883 1 0.000
#> 884 1 0.000
#> 885 1 0.000
#> 886 2 0.751
#> 887 1 0.000
#> 888 3 1.000
#> 889 1 1.000
#> 890 4 0.000
#> 891 1 0.751
#> 892 3 1.000
#> 893 1 1.000
#> 894 1 0.000
#> 895 1 1.000
#> 896 2 0.000
#> 897 2 0.253
#> 898 1 0.000
#> 899 1 0.000
#> 900 1 0.000
#> 901 1 0.000
#> 902 1 0.000
#> 903 3 0.751
#> 904 1 0.000
#> 905 1 1.000
#> 906 1 1.000
#> 907 1 1.000
#> 908 1 1.000
#> 909 1 1.000
#> 910 1 0.249
#> 911 1 1.000
#> 912 1 0.751
#> 913 1 0.000
#> 914 1 0.000
#> 915 1 1.000
#> 916 1 1.000
#> 917 1 0.000
#> 918 1 0.000
#> 919 1 0.000
#> 920 1 0.000
#> 921 1 0.000
#> 922 1 0.000
#> 923 1 0.000
#> 924 1 0.502
#> 925 1 1.000
#> 926 1 0.000
#> 927 1 0.000
#> 928 1 0.249
#> 929 2 0.498
#> 930 1 0.502
#> 931 4 0.253
#> 932 1 0.000
#> 933 1 0.751
#> 934 1 0.249
#> 935 2 0.502
#> 936 4 0.000
#> 937 1 0.000
#> 938 2 1.000
#> 939 1 1.000
#> 940 1 0.000
#> 941 1 0.249
#> 942 4 0.000
#> 943 1 0.751
#> 944 1 0.000
#> 945 1 0.000
#> 946 1 0.751
#> 947 1 0.498
#> 948 1 1.000
#> 949 1 0.000
#> 950 1 0.751
#> 951 1 0.249
#> 952 1 0.000
#> 953 1 0.000
#> 954 1 1.000
#> 955 1 1.000
#> 956 2 1.000
#> 957 4 0.000
#> 958 4 0.000
#> 959 1 0.498
#> 960 2 1.000
#> 961 2 0.498
#> 962 1 1.000
#> 963 1 0.000
#> 964 2 1.000
#> 965 3 0.000
#> 966 1 1.000
#> 967 1 0.498
#> 968 1 1.000
#> 969 1 1.000
#> 970 3 0.249
#> 971 1 0.000
#> 972 1 0.000
#> 973 2 1.000
#> 974 1 1.000
#> 975 1 0.000
#> 976 1 0.000
#> 977 1 0.000
#> 978 1 1.000
#> 979 1 0.000
#> 980 1 0.747
#> 981 4 0.000
#> 982 4 0.000
#> 983 2 1.000
#> 984 2 1.000
#> 985 1 1.000
#> 986 1 1.000
#> 987 1 0.502
#> 988 1 1.000
#> 989 1 1.000
#> 990 3 0.000
#> 991 3 1.000
#> 992 4 0.000
#> 993 1 1.000
#> 994 1 1.000
#> 995 1 1.000
#> 996 1 1.000
#> 997 1 0.249
#> 998 3 1.000
#> 999 3 0.498
#> 1000 1 0.249
#> 1001 2 0.000
#> 1002 1 0.747
#> 1003 1 0.502
#> 1004 1 0.000
#> 1005 1 0.000
#> 1006 1 1.000
#> 1007 1 0.498
#> 1008 1 0.000
#> 1009 1 0.000
#> 1010 1 0.000
#> 1011 1 0.502
#> 1012 1 0.000
#> 1013 1 0.000
#> 1014 2 1.000
#> 1015 1 0.000
#> 1016 2 1.000
#> 1017 1 0.000
#> 1018 1 1.000
#> 1019 1 0.000
#> 1020 1 0.498
#> 1021 1 1.000
#> 1022 1 0.498
#> 1023 1 1.000
#> 1024 1 1.000
#> 1025 1 1.000
#> 1026 3 0.253
#> 1027 4 0.000
#> 1028 1 1.000
#> 1029 1 0.249
#> 1030 1 1.000
#> 1031 1 0.000
#> 1032 1 0.000
#> 1033 1 1.000
#> 1034 1 0.000
#> 1035 1 1.000
#> 1036 3 1.000
#> 1037 1 0.000
#> 1038 3 0.000
#> 1039 1 1.000
#> 1040 2 0.000
#> 1041 1 0.000
#> 1042 1 1.000
#> 1043 3 0.000
#> 1044 1 0.249
#> 1045 1 1.000
#> 1046 1 0.000
#> 1047 3 0.000
#> 1048 1 0.000
#> 1049 1 0.000
#> 1050 1 0.000
#> 1051 1 1.000
#> 1052 1 0.000
#> 1053 1 0.249
#> 1054 1 0.000
#> 1055 1 0.000
#> 1056 1 0.000
#> 1057 1 0.000
#> 1058 1 0.000
#> 1059 1 0.000
#> 1060 1 0.000
#> 1061 1 0.000
#> 1062 1 0.000
#> 1063 1 0.000
#> 1064 1 0.000
#> 1065 1 0.000
#> 1066 1 0.000
#> 1067 1 0.502
#> 1068 1 0.000
#> 1069 1 0.000
#> 1070 1 0.000
#> 1071 1 0.000
#> 1072 1 0.000
#> 1073 1 1.000
#> 1074 1 0.000
#> 1075 3 0.000
#> 1076 1 0.000
#> 1077 1 0.000
#> 1078 1 0.000
#> 1079 1 0.000
#> 1080 1 0.000
#> 1081 1 0.000
#> 1082 1 0.000
#> 1083 3 0.000
#> 1084 1 0.498
#> 1085 1 0.502
#> 1086 1 0.000
#> 1087 1 0.000
#> 1088 3 0.000
#> 1089 1 0.000
#> 1090 1 0.000
#> 1091 1 0.751
#> 1092 1 0.000
#> 1093 1 1.000
#> 1094 1 0.751
#> 1095 1 1.000
#> 1096 1 0.000
#> 1097 1 0.751
#> 1098 1 0.000
#> 1099 1 0.000
#> 1100 1 0.000
#> 1101 4 0.000
#> 1102 1 0.000
#> 1103 1 0.000
#> 1104 1 1.000
#> 1105 1 0.000
#> 1106 1 0.000
#> 1107 1 0.249
#> 1108 1 1.000
#> 1109 1 0.000
#> 1110 3 0.000
#> 1111 1 0.000
#> 1112 1 0.000
#> 1113 1 0.000
#> 1114 1 0.000
#> 1115 1 0.502
#> 1116 1 1.000
#> 1117 1 0.000
#> 1118 3 0.000
#> 1119 1 0.000
#> 1120 4 0.000
#> 1121 1 0.000
#> 1122 3 0.000
#> 1123 1 0.249
#> 1124 1 0.000
#> 1125 1 0.747
#> 1126 1 0.000
#> 1127 1 0.747
#> 1128 3 0.000
#> 1129 1 0.000
#> 1130 1 0.747
#> 1131 1 0.751
#> 1132 1 0.000
#> 1133 1 1.000
#> 1134 1 0.000
#> 1135 3 0.000
#> 1136 1 0.000
#> 1137 1 0.000
#> 1138 1 0.000
#> 1139 1 0.000
#> 1140 1 0.249
#> 1141 2 0.000
#> 1142 1 0.000
#> 1143 1 0.000
#> 1144 1 1.000
#> 1145 3 0.000
#> 1146 1 0.000
#> 1147 1 0.000
#> 1148 1 0.253
#> 1149 1 0.249
#> 1150 4 1.000
#> 1151 1 0.498
#> 1152 1 0.000
#> 1153 1 1.000
#> 1154 1 0.000
#> 1155 1 0.502
#> 1156 1 0.000
#> 1157 1 0.000
#> 1158 1 0.000
#> 1159 1 1.000
#> 1160 1 0.000
#> 1161 1 1.000
#> 1162 1 0.502
#> 1163 1 1.000
#> 1164 1 0.000
#> 1165 1 0.000
#> 1166 1 0.000
#> 1167 1 0.000
#> 1168 1 0.000
#> 1169 1 0.000
#> 1170 1 0.000
#> 1171 1 1.000
#> 1172 3 0.000
#> 1173 1 0.249
#> 1174 1 0.502
#> 1175 1 1.000
#> 1176 1 0.498
#> 1177 1 1.000
#> 1178 1 1.000
#> 1179 1 1.000
#> 1180 1 0.000
#> 1181 2 1.000
#> 1182 1 0.000
#> 1183 4 0.000
#> 1184 1 1.000
#> 1185 2 0.000
#> 1186 1 0.000
#> 1187 1 1.000
#> 1188 1 0.000
#> 1189 1 1.000
#> 1190 1 0.249
#> 1191 1 0.751
#> 1192 1 0.000
#> 1193 1 1.000
#> 1194 1 0.000
#> 1195 1 0.000
#> 1196 1 0.747
#> 1197 1 0.000
#> 1198 1 0.502
#> 1199 1 0.000
#> 1200 1 1.000
#> 1201 1 1.000
#> 1202 4 0.000
#> 1203 1 1.000
#> 1204 1 0.000
#> 1205 1 1.000
#> 1206 1 0.000
#> 1207 1 0.000
#> 1208 3 1.000
#> 1209 1 0.000
#> 1210 1 0.000
#> 1211 1 0.000
#> 1212 1 0.249
#> 1213 1 1.000
#> 1214 4 1.000
#> 1215 2 0.747
#> 1216 1 0.000
#> 1217 4 0.249
#> 1218 3 0.000
#> 1219 1 1.000
#> 1220 1 1.000
#> 1221 1 1.000
#> 1222 1 0.000
#> 1223 1 0.747
#> 1224 2 0.000
#> 1225 1 1.000
#> 1226 2 0.000
#> 1227 1 1.000
#> 1228 1 0.000
#> 1229 1 0.000
#> 1230 4 1.000
#> 1231 1 0.000
#> 1232 1 0.000
#> 1233 1 1.000
#> 1234 1 1.000
#> 1235 1 0.747
#> 1236 1 1.000
#> 1237 1 1.000
#> 1238 3 0.000
#> 1239 4 1.000
#> 1240 1 1.000
#> 1241 3 0.000
#> 1242 4 0.000
#> 1243 3 0.000
#> 1244 1 0.000
#> 1245 1 1.000
#> 1246 1 0.000
#> 1247 1 1.000
#> 1248 1 1.000
#> 1249 1 0.000
#> 1250 1 0.000
#> 1251 1 1.000
#> 1252 1 0.000
#> 1253 1 1.000
#> 1254 1 0.000
#> 1255 1 1.000
#> 1256 2 1.000
#> 1257 2 0.498
#> 1258 2 0.000
#> 1259 2 0.000
#> 1260 1 1.000
#> 1261 1 0.000
#> 1262 1 1.000
#> 1263 1 1.000
#> 1264 1 0.000
#> 1265 3 1.000
#> 1266 4 1.000
#> 1267 1 0.000
#> 1268 1 1.000
#> 1269 1 1.000
#> 1270 1 1.000
#> 1271 1 0.751
#> 1272 1 0.249
#> 1273 4 1.000
#> 1274 1 0.000
#> 1275 1 0.000
#> 1276 1 0.502
#> 1277 3 0.000
#> 1278 2 0.000
#> 1279 3 0.000
#> 1280 3 0.000
#> 1281 3 0.000
#> 1282 1 1.000
#> 1283 3 0.000
#> 1284 1 1.000
#> 1285 1 1.000
#> 1286 1 0.249
#> 1287 1 0.249
#> 1288 1 1.000
#> 1289 3 0.000
#> 1290 3 0.000
#> 1291 1 1.000
#> 1292 3 0.000
#> 1293 1 1.000
#> 1294 1 1.000
#> 1295 2 1.000
#> 1296 3 0.000
#> 1297 1 0.249
#> 1298 4 0.000
#> 1299 1 0.747
#> 1300 1 0.498
#> 1301 1 0.000
#> 1302 1 0.000
#> 1303 1 0.751
#> 1304 1 0.000
#> 1305 1 1.000
#> 1306 1 0.000
#> 1307 1 0.000
#> 1308 1 0.000
#> 1309 1 0.000
#> 1310 1 0.000
#> 1311 1 0.751
#> 1312 1 0.751
#> 1313 2 0.253
#> 1314 1 1.000
#> 1315 4 0.249
#> 1316 1 0.249
#> 1317 3 0.000
#> 1318 2 0.502
#> 1319 1 0.000
#> 1320 1 0.000
#> 1321 1 0.498
#> 1322 1 0.000
#> 1323 1 0.000
#> 1324 1 0.249
#> 1325 1 0.000
#> 1326 1 0.502
#> 1327 3 0.000
#> 1328 1 0.000
#> 1329 1 0.000
#> 1330 1 0.000
#> 1331 1 0.000
#> 1332 2 1.000
#> 1333 1 1.000
#> 1334 3 1.000
#> 1335 1 1.000
#> 1336 3 0.000
#> 1337 1 0.000
#> 1338 1 0.000
#> 1339 1 0.000
#> 1340 1 0.253
#> 1341 1 0.502
#> 1342 1 0.000
#> 1343 2 0.000
#> 1344 1 1.000
#> 1345 1 0.000
#> 1346 2 1.000
#> 1347 2 1.000
#> 1348 2 0.000
#> 1349 1 0.751
#> 1350 1 1.000
#> 1351 1 0.000
#> 1352 1 0.000
#> 1353 2 0.751
#> 1354 2 1.000
#> 1355 1 0.751
#> 1356 1 0.000
#> 1357 1 0.000
#> 1358 1 1.000
#> 1359 1 0.502
#> 1360 1 0.498
#> 1361 2 0.249
#> 1362 2 1.000
#> 1363 1 0.000
#> 1364 1 0.000
#> 1365 3 1.000
#> 1366 3 0.000
#> 1367 1 0.000
#> 1368 3 0.000
#> 1369 1 1.000
#> 1370 3 0.000
#> 1371 3 0.000
#> 1372 3 0.000
#> 1373 3 0.000
#> 1374 1 1.000
#> 1375 3 0.000
#> 1376 1 1.000
#> 1377 4 0.000
#> 1378 1 0.000
#> 1379 1 0.000
#> 1380 1 0.000
#> 1381 1 0.000
#> 1382 1 0.000
#> 1383 1 0.249
#> 1384 1 0.000
#> 1385 1 0.000
#> 1386 1 1.000
#> 1387 1 0.000
#> 1388 1 0.000
#> 1389 1 0.751
#> 1390 1 0.000
#> 1391 1 0.000
#> 1392 4 0.000
#> 1393 1 0.000
#> 1394 1 0.000
#> 1395 2 1.000
#> 1396 1 1.000
#> 1397 2 1.000
#> 1398 2 1.000
#> 1399 2 1.000
#> 1400 1 1.000
#> 1401 4 0.000
#> 1402 1 1.000
#> 1403 1 1.000
#> 1404 1 0.000
#> 1405 1 0.000
#> 1406 3 0.000
#> 1407 2 1.000
#> 1408 1 0.000
#> 1409 1 0.000
#> 1410 3 0.000
#> 1411 1 0.000
#> 1412 1 0.000
#> 1413 1 0.000
#> 1414 1 0.000
#> 1415 1 0.000
#> 1416 1 0.000
#> 1417 1 0.000
#> 1418 1 0.000
#> 1419 3 0.000
#> 1420 1 0.000
#> 1421 1 0.000
#> 1422 3 0.502
#> 1423 1 0.000
#> 1424 1 0.000
#> 1425 1 0.000
#> 1426 1 1.000
#> 1427 1 0.000
#> 1428 1 0.000
#> 1429 3 1.000
#> 1430 1 1.000
#> 1431 1 0.000
#> 1432 1 1.000
#> 1433 1 0.751
#> 1434 3 1.000
#> 1435 1 0.000
#> 1436 1 0.000
#> 1437 1 0.000
#> 1438 1 0.000
#> 1439 1 0.000
#> 1440 1 0.000
#> 1441 1 0.000
#> 1442 1 0.000
#> 1443 1 0.253
#> 1444 1 0.000
#> 1445 1 0.000
#> 1446 1 0.000
#> 1447 1 0.000
#> 1448 1 0.000
#> 1449 2 1.000
#> 1450 1 1.000
#> 1451 3 0.000
#> 1452 2 0.000
#> 1453 1 0.000
#> 1454 1 0.000
#> 1455 2 0.000
#> 1456 1 0.000
#> 1457 1 0.498
#> 1458 1 0.000
#> 1459 2 1.000
#> 1460 1 0.000
#> 1461 1 0.000
#> 1462 1 0.000
#> 1463 4 0.502
#> 1464 1 0.498
#> 1465 1 0.000
#> 1466 1 0.000
#> 1467 1 0.000
#> 1468 1 1.000
#> 1469 1 1.000
#> 1470 1 1.000
#> 1471 1 0.000
#> 1472 3 0.000
#> 1473 1 0.249
#> 1474 3 0.000
#> 1475 3 1.000
#> 1476 3 0.502
#> 1477 1 1.000
#> 1478 2 0.000
#> 1479 1 0.000
#> 1480 2 0.000
#> 1481 1 0.249
#> 1482 1 0.000
#> 1483 1 0.502
#> 1484 1 0.502
#> 1485 1 1.000
#> 1486 1 0.000
#> 1487 1 1.000
#> 1488 3 0.502
#> 1489 4 0.000
#> 1490 1 0.000
#> 1491 4 0.000
#> 1492 1 1.000
#> 1493 1 0.000
#> 1494 1 0.000
#> 1495 4 0.000
#> 1496 1 1.000
#> 1497 1 0.000
#> 1498 1 0.000
#> 1499 1 0.249
#> 1500 2 1.000
#> 1501 1 1.000
#> 1502 4 0.000
#> 1503 1 0.000
#> 1504 1 0.249
#> 1505 1 1.000
#> 1506 3 1.000
#> 1507 4 0.000
#> 1508 2 0.751
#> 1509 1 0.000
#> 1510 2 0.000
#> 1511 1 1.000
#> 1512 1 0.000
#> 1513 1 1.000
#> 1514 1 0.498
#> 1515 1 1.000
#> 1516 1 1.000
#> 1517 1 0.000
#> 1518 2 1.000
#> 1519 4 0.249
#> 1520 2 1.000
#> 1521 2 1.000
#> 1522 4 0.000
#> 1523 4 0.000
#> 1524 2 0.000
#> 1525 4 0.000
#> 1526 1 1.000
#> 1527 3 1.000
#> 1528 4 0.000
#> 1529 1 0.502
#> 1530 1 0.000
#> 1531 4 0.000
#> 1532 1 0.000
#> 1533 1 1.000
#> 1534 2 0.000
#> 1535 1 0.498
#> 1536 2 0.000
#> 1537 1 1.000
#> 1538 1 1.000
#> 1539 1 1.000
#> 1540 1 0.000
#> 1541 1 0.253
#> 1542 1 1.000
#> 1543 1 0.000
#> 1544 1 0.000
#> 1545 1 0.000
#> 1546 1 1.000
#> 1547 1 0.000
#> 1548 1 0.249
#> 1549 1 0.000
#> 1550 1 0.000
#> 1551 3 1.000
#> 1552 1 0.751
#> 1553 1 0.498
#> 1554 1 1.000
#> 1555 1 0.000
#> 1556 1 0.000
#> 1557 4 0.000
#> 1558 2 0.502
#> 1559 2 1.000
#> 1560 1 0.000
#> 1561 1 0.000
#> 1562 1 0.000
#> 1563 4 0.000
#> 1564 4 1.000
#> 1565 2 1.000
#> 1566 1 1.000
#> 1567 3 0.751
#> 1568 3 0.000
#> 1569 1 0.000
#> 1570 1 1.000
#> 1571 1 1.000
#> 1572 2 1.000
#> 1573 2 0.000
#> 1574 4 0.000
#> 1575 2 0.502
#> 1576 3 1.000
#> 1577 3 0.000
#> 1578 2 1.000
#> 1579 2 0.249
#> 1580 2 1.000
#> 1581 3 0.000
#> 1582 2 1.000
#> 1583 3 0.000
#> 1584 2 1.000
#> 1585 3 0.000
#> 1586 3 0.000
#> 1587 3 0.000
#> 1588 3 0.000
#> 1589 3 0.000
#> 1590 3 1.000
#> 1591 1 0.249
#> 1592 3 0.000
#> 1593 3 0.000
#> 1594 2 0.751
#> 1595 3 0.000
#> 1596 2 0.249
#> 1597 2 0.000
#> 1598 2 1.000
#> 1599 1 0.498
#> 1600 2 1.000
#> 1601 1 0.000
#> 1602 3 1.000
#> 1603 1 0.000
#> 1604 2 0.253
#> 1605 2 1.000
#> 1606 1 0.000
#> 1607 1 1.000
#> 1608 2 1.000
#> 1609 2 1.000
#> 1610 2 1.000
#> 1611 3 0.000
#> 1612 3 0.498
#> 1613 3 0.000
#> 1614 3 0.000
#> 1615 3 0.000
#> 1616 3 0.000
#> 1617 3 0.000
#> 1618 2 0.000
#> 1619 1 0.000
#> 1620 2 0.000
#> 1621 2 1.000
#> 1622 2 1.000
#> 1623 4 0.000
#> 1624 3 0.000
#> 1625 1 0.502
#> 1626 2 0.249
#> 1627 1 0.000
#> 1628 1 1.000
#> 1629 1 0.000
#> 1630 1 1.000
#> 1631 1 1.000
#> 1632 1 0.249
#> 1633 3 0.000
#> 1634 1 0.000
#> 1635 2 1.000
#> 1636 1 0.000
#> 1637 1 0.000
#> 1638 1 0.000
#> 1639 2 0.249
#> 1640 1 0.000
#> 1641 1 0.000
#> 1642 1 0.751
#> 1643 1 0.000
#> 1644 4 0.000
#> 1645 1 1.000
#> 1646 1 0.000
#> 1647 1 1.000
#> 1648 1 0.000
#> 1649 1 0.502
#> 1650 1 0.000
#> 1651 4 0.000
#> 1652 1 0.000
#> 1653 1 0.000
#> 1654 3 1.000
#> 1655 1 0.000
#> 1656 1 0.000
#> 1657 1 0.000
#> 1658 1 0.000
#> 1659 1 1.000
#> 1660 1 0.000
#> 1661 1 0.000
#> 1662 1 0.000
#> 1663 1 0.000
#> 1664 1 1.000
#> 1665 1 0.000
#> 1666 1 0.000
#> 1667 1 0.000
#> 1668 1 0.000
#> 1669 1 0.502
#> 1670 1 0.000
#> 1671 1 0.000
#> 1672 1 1.000
#> 1673 1 0.000
#> 1674 1 0.000
#> 1675 1 0.000
#> 1676 4 0.000
#> 1677 1 0.000
#> 1678 1 1.000
#> 1679 1 0.249
#> 1680 2 1.000
#> 1681 1 0.000
#> 1682 1 0.000
#> 1683 1 0.000
#> 1684 4 0.751
#> 1685 1 0.000
#> 1686 1 0.498
#> 1687 1 0.000
#> 1688 1 0.000
#> 1689 1 1.000
#> 1690 1 0.751
#> 1691 1 0.000
#> 1692 1 0.000
#> 1693 3 1.000
#> 1694 1 0.000
#> 1695 1 0.000
#> 1696 1 0.000
#> 1697 1 0.000
#> 1698 1 1.000
#> 1699 1 0.000
#> 1700 1 0.000
#> 1701 1 1.000
#> 1702 1 0.000
#> 1703 3 1.000
#> 1704 1 0.000
#> 1705 1 0.000
#> 1706 1 0.000
#> 1707 4 0.000
#> 1708 1 0.000
#> 1709 1 0.000
#> 1710 1 0.253
#> 1711 1 0.000
#> 1712 3 1.000
#> 1713 1 0.000
#> 1714 1 0.000
#> 1715 1 0.249
#> 1716 1 1.000
#> 1717 1 0.000
#> 1718 1 0.253
#> 1719 4 0.000
#> 1720 3 1.000
#> 1721 1 0.249
#> 1722 1 0.253
#> 1723 1 1.000
#> 1724 1 1.000
#> 1725 1 0.000
#> 1726 1 0.000
#> 1727 1 0.000
#> 1728 3 0.000
#> 1729 1 0.000
#> 1730 3 0.000
#> 1731 3 0.000
#> 1732 1 0.000
#> 1733 1 0.000
#> 1734 2 0.000
#> 1735 1 0.000
#> 1736 1 0.747
#> 1737 1 1.000
#> 1738 1 1.000
#> 1739 1 1.000
#> 1740 1 0.000
#> 1741 1 0.000
#> 1742 1 0.000
#> 1743 1 0.000
#> 1744 1 0.000
#> 1745 1 1.000
#> 1746 1 0.000
#> 1747 1 1.000
#> 1748 1 1.000
#> 1749 3 1.000
#> 1750 1 1.000
#> 1751 3 0.000
#> 1752 1 0.000
#> 1753 2 1.000
#> 1754 1 0.000
#> 1755 2 0.000
#> 1756 1 0.000
#> 1757 1 1.000
#> 1758 1 0.000
#> 1759 4 0.000
#> 1760 3 0.000
#> 1761 2 0.000
#> 1762 1 0.000
#> 1763 1 1.000
#> 1764 1 1.000
#> 1765 1 0.000
#> 1766 2 0.000
#> 1767 3 0.000
#> 1768 1 0.498
#> 1769 1 0.000
#> 1770 2 1.000
#> 1771 1 0.000
#> 1772 1 0.000
#> 1773 1 0.000
#> 1774 1 0.000
#> 1775 1 0.000
#> 1776 1 0.000
#> 1777 1 1.000
#> 1778 1 0.000
#> 1779 1 0.000
#> 1780 1 0.000
#> 1781 3 0.000
#> 1782 1 0.000
#> 1783 1 0.000
#> 1784 1 0.000
#> 1785 1 0.751
#> 1786 1 1.000
#> 1787 3 1.000
#> 1788 1 1.000
#> 1789 1 0.000
#> 1790 4 0.000
#> 1791 1 0.498
#> 1792 3 0.000
#> 1793 3 0.000
#> 1794 3 0.502
#> 1795 2 1.000
#> 1796 1 1.000
#> 1797 1 0.000
#> 1798 3 0.000
#> 1799 2 0.000
#> 1800 1 0.000
#> 1801 1 0.000
#> 1802 1 0.000
#> 1803 1 0.751
#> 1804 3 0.751
#> 1805 1 0.000
#> 1806 1 0.000
#> 1807 2 1.000
#> 1808 2 0.498
#> 1809 2 0.000
#> 1810 4 0.249
#> 1811 2 0.747
#> 1812 1 0.000
#> 1813 1 0.000
#> 1814 1 1.000
#> 1815 1 0.000
#> 1816 1 0.000
#> 1817 1 0.000
#> 1818 1 0.000
#> 1819 1 0.249
#> 1820 1 0.000
#> 1821 3 0.000
#> 1822 1 0.000
#> 1823 1 1.000
#> 1824 1 0.000
#> 1825 1 0.000
#> 1826 1 0.000
#> 1827 1 0.000
#> 1828 1 0.000
#> 1829 1 0.000
#> 1830 2 1.000
#> 1831 1 0.000
#> 1832 1 0.000
#> 1833 3 0.000
#> 1834 1 0.498
#> 1835 1 1.000
#> 1836 2 0.000
#> 1837 2 0.751
#> 1838 1 0.000
#> 1839 1 0.000
#> 1840 1 0.000
#> 1841 1 0.000
#> 1842 1 0.000
#> 1843 2 0.000
#> 1844 1 0.000
#> 1845 1 0.000
#> 1846 4 0.000
#> 1847 1 0.000
#> 1848 1 0.000
#> 1849 1 0.751
#> 1850 1 0.000
#> 1851 1 0.000
#> 1852 3 0.000
#> 1853 1 0.249
#> 1854 1 0.000
#> 1855 1 1.000
#> 1856 1 0.000
#> 1857 1 1.000
#> 1858 1 0.751
#> 1859 3 0.000
#> 1860 1 0.000
#> 1861 3 0.000
#> 1862 1 0.000
#> 1863 1 0.000
#> 1864 3 0.000
#> 1865 2 0.498
#> 1866 3 0.000
#> 1867 4 0.000
#> 1868 3 0.249
#> 1869 2 0.000
#> 1870 1 0.000
#> 1871 1 0.000
#> 1872 4 1.000
#> 1873 3 1.000
#> 1874 1 0.000
#> 1875 1 1.000
#> 1876 1 0.000
#> 1877 2 1.000
#> 1878 1 0.000
#> 1879 1 1.000
#> 1880 1 0.000
#> 1881 2 0.253
#> 1882 1 1.000
#> 1883 1 0.000
#> 1884 1 0.000
#> 1885 1 1.000
#> 1886 3 0.249
#> 1887 1 0.000
#> 1888 3 0.000
#> 1889 1 1.000
#> 1890 1 1.000
#> 1891 1 1.000
#> 1892 2 1.000
#> 1893 1 0.000
#> 1894 1 0.000
#> 1895 1 0.000
#> 1896 3 1.000
#> 1897 1 0.000
#> 1898 1 1.000
#> 1899 1 0.000
#> 1900 3 0.000
#> 1901 1 1.000
#> 1902 3 0.000
#> 1903 1 1.000
#> 1904 3 1.000
#> 1905 1 0.498
#> 1906 1 0.000
#> 1907 3 0.000
#> 1908 1 1.000
#> 1909 1 1.000
#> 1910 1 0.000
#> 1911 1 1.000
#> 1912 1 1.000
#> 1913 1 1.000
#> 1914 1 0.000
#> 1915 3 0.000
#> 1916 1 1.000
#> 1917 1 0.249
#> 1918 1 1.000
#> 1919 1 0.000
#> 1920 3 0.000
#> 1921 2 1.000
#> 1922 1 1.000
#> 1923 3 0.000
#> 1924 3 0.000
#> 1925 1 0.751
#> 1926 3 0.000
#> 1927 1 1.000
#> 1928 1 1.000
#> 1929 1 1.000
#> 1930 2 1.000
#> 1931 3 1.000
#> 1932 1 1.000
#> 1933 2 0.000
#> 1934 1 1.000
#> 1935 3 0.000
#> 1936 2 1.000
#> 1937 3 0.000
#> 1938 1 0.751
#> 1939 1 1.000
#> 1940 3 0.000
#> 1941 3 0.000
#> 1942 1 0.000
#> 1943 3 1.000
#> 1944 1 1.000
#> 1945 3 0.000
#> 1946 2 0.000
#> 1947 3 0.000
#> 1948 3 1.000
#> 1949 1 1.000
#> 1950 3 0.000
#> 1951 3 1.000
#> 1952 1 1.000
#> 1953 1 0.000
#> 1954 2 1.000
#> 1955 1 1.000
#> 1956 1 1.000
#> 1957 2 1.000
#> 1958 3 0.000
#> 1959 2 0.253
#> 1960 4 0.000
#> 1961 2 1.000
#> 1962 3 0.000
#> 1963 3 1.000
#> 1964 2 0.498
#> 1965 1 1.000
#> 1966 3 0.000
#> 1967 2 0.000
#> 1968 3 0.000
#> 1969 4 1.000
#> 1970 3 0.000
#> 1971 1 1.000
#> 1972 2 1.000
#> 1973 3 0.000
#> 1974 3 0.000
#> 1975 2 0.000
#> 1976 3 0.000
#> 1977 1 0.249
#> 1978 2 0.751
#> 1979 3 0.747
#> 1980 2 0.502
#> 1981 3 0.000
#> 1982 3 0.000
#> 1983 3 1.000
#> 1984 2 1.000
#> 1985 2 0.000
#> 1986 2 1.000
#> 1987 2 0.502
#> 1988 2 0.000
#> 1989 2 0.000
#> 1990 2 1.000
#> 1991 2 0.000
#> 1992 2 0.000
#> 1993 4 1.000
#> 1994 2 1.000
#> 1995 2 0.000
#> 1996 2 0.000
#> 1997 4 0.502
#> 1998 4 0.000
#> 1999 2 0.253
#> 2000 2 1.000
#> 2001 2 0.747
#> 2002 2 0.000
#> 2003 2 1.000
#> 2004 2 1.000
#> 2005 2 0.751
#> 2006 2 0.000
#> 2007 2 1.000
#> 2008 2 0.253
#> 2009 2 1.000
#> 2010 2 1.000
#> 2011 2 0.000
#> 2012 2 1.000
#> 2013 2 0.253
#> 2014 2 1.000
#> 2015 4 0.000
#> 2016 2 0.000
#> 2017 2 1.000
#> 2018 3 0.502
#> 2019 2 1.000
#> 2020 2 1.000
#> 2021 2 1.000
#> 2022 2 0.000
#> 2023 2 1.000
#> 2024 2 1.000
#> 2025 2 1.000
#> 2026 3 0.249
#> 2027 2 0.249
#> 2028 2 1.000
#> 2029 2 0.498
#> 2030 2 0.000
#> 2031 2 1.000
#> 2032 2 1.000
#> 2033 2 1.000
#> 2034 2 1.000
#> 2035 2 0.000
#> 2036 2 0.498
#> 2037 2 0.502
#> 2038 2 0.498
#> 2039 2 0.751
#> 2040 2 0.249
#> 2041 2 1.000
#> 2042 3 0.249
#> 2043 2 0.000
#> 2044 2 1.000
#> 2045 2 1.000
#> 2046 2 1.000
#> 2047 2 0.000
#> 2048 2 0.000
#> 2049 2 0.000
#> 2050 4 0.000
#> 2051 2 0.000
#> 2052 2 1.000
#> 2053 2 1.000
#> 2054 2 1.000
#> 2055 2 1.000
#> 2056 2 0.000
#> 2057 2 0.000
#> 2058 2 0.000
#> 2059 4 0.751
#> 2060 2 0.000
#> 2061 2 0.000
#> 2062 2 1.000
#> 2063 2 0.000
#> 2064 2 0.000
#> 2065 2 1.000
#> 2066 2 0.253
#> 2067 2 0.000
#> 2068 2 0.000
#> 2069 2 0.000
#> 2070 2 0.000
#> 2071 2 0.000
#> 2072 2 0.000
#> 2073 2 1.000
#> 2074 2 1.000
#> 2075 2 1.000
#> 2076 2 1.000
#> 2077 2 0.000
#> 2078 2 0.498
#> 2079 2 0.000
#> 2080 2 1.000
#> 2081 2 0.000
#> 2082 2 1.000
#> 2083 2 0.000
#> 2084 4 1.000
#> 2085 2 0.000
#> 2086 2 0.000
#> 2087 2 0.502
#> 2088 2 1.000
#> 2089 2 0.000
#> 2090 2 1.000
#> 2091 2 0.502
#> 2092 2 0.000
#> 2093 2 0.000
#> 2094 2 0.000
#> 2095 2 0.249
#> 2096 2 0.000
#> 2097 2 0.000
#> 2098 2 0.000
#> 2099 2 0.000
#> 2100 4 0.000
#> 2101 2 1.000
#> 2102 2 0.751
#> 2103 2 0.253
#> 2104 2 0.000
#> 2105 2 0.000
#> 2106 2 0.000
#> 2107 2 0.000
#> 2108 2 0.751
#> 2109 2 1.000
#> 2110 2 1.000
#> 2111 4 1.000
#> 2112 2 0.000
#> 2113 2 1.000
#> 2114 2 0.502
#> 2115 2 1.000
#> 2116 2 0.751
#> 2117 2 1.000
#> 2118 2 0.498
#> 2119 2 1.000
#> 2120 2 1.000
#> 2121 2 0.000
#> 2122 2 0.000
#> 2123 2 1.000
#> 2124 2 1.000
#> 2125 4 0.000
#> 2126 2 0.000
#> 2127 2 1.000
#> 2128 4 0.000
#> 2129 2 0.000
#> 2130 2 1.000
#> 2131 2 0.000
#> 2132 2 0.000
#> 2133 2 0.751
#> 2134 2 1.000
#> 2135 2 0.000
#> 2136 2 0.000
#> 2137 2 0.000
#> 2138 2 0.751
#> 2139 2 0.000
#> 2140 2 0.000
#> 2141 2 0.000
#> 2142 2 0.751
#> 2143 2 0.000
#> 2144 2 0.000
#> 2145 2 0.000
#> 2146 2 0.000
#> 2147 2 0.751
#> 2148 2 0.000
#> 2149 2 0.000
#> 2150 2 0.000
#> 2151 2 0.000
#> 2152 2 0.502
#> 2153 2 0.000
#> 2154 2 0.747
#> 2155 2 1.000
#> 2156 2 0.000
#> 2157 2 0.000
#> 2158 2 0.751
#> 2159 2 0.000
#> 2160 2 0.000
#> 2161 2 0.000
#> 2162 2 0.000
#> 2163 2 0.000
#> 2164 2 0.000
#> 2165 2 1.000
#> 2166 2 0.000
#> 2167 2 0.000
#> 2168 2 0.000
#> 2169 2 0.000
#> 2170 2 0.000
#> 2171 2 0.000
#> 2172 2 0.249
#> 2173 2 0.000
#> 2174 3 0.751
#> 2175 2 0.502
#> 2176 2 0.751
#> 2177 2 0.253
#> 2178 2 0.751
#> 2179 2 0.000
#> 2180 2 0.747
#> 2181 2 0.000
#> 2182 2 0.000
#> 2183 2 1.000
#> 2184 2 1.000
#> 2185 2 0.000
#> 2186 2 1.000
#> 2187 2 0.000
#> 2188 2 0.000
#> 2189 2 1.000
#> 2190 4 0.000
#> 2191 2 0.000
#> 2192 4 0.000
#> 2193 4 0.751
#> 2194 3 0.000
#> 2195 2 0.000
#> 2196 4 1.000
#> 2197 2 1.000
#> 2198 2 1.000
#> 2199 2 0.000
#> 2200 2 0.000
#> 2201 2 1.000
#> 2202 2 1.000
#> 2203 2 0.000
#> 2204 2 1.000
#> 2205 2 1.000
#> 2206 2 0.000
#> 2207 2 1.000
#> 2208 2 0.000
#> 2209 2 1.000
#> 2210 4 0.000
#> 2211 2 1.000
#> 2212 2 0.000
#> 2213 2 1.000
#> 2214 2 0.000
#> 2215 2 0.249
#> 2216 2 0.253
#> 2217 2 1.000
#> 2218 2 0.000
#> 2219 2 1.000
#> 2220 2 1.000
#> 2221 2 0.000
#> 2222 2 0.000
#> 2223 2 0.249
#> 2224 2 0.751
#> 2225 2 0.000
#> 2226 2 1.000
#> 2227 2 0.498
#> 2228 2 1.000
#> 2229 2 1.000
#> 2230 2 0.000
#> 2231 2 0.000
#> 2232 2 0.000
#> 2233 2 0.000
#> 2234 2 0.000
#> 2235 2 0.000
#> 2236 2 0.000
#> 2237 4 0.000
#> 2238 2 0.000
#> 2239 2 0.498
#> 2240 4 1.000
#> 2241 2 0.751
#> 2242 4 1.000
#> 2243 2 0.249
#> 2244 2 0.000
#> 2245 2 0.000
#> 2246 2 0.000
#> 2247 2 0.000
#> 2248 4 0.000
#> 2249 4 0.000
#> 2250 2 0.000
#> 2251 2 0.000
#> 2252 3 0.747
#> 2253 2 1.000
#> 2254 2 0.000
#> 2255 4 0.000
#> 2256 4 0.751
#> 2257 2 0.000
#> 2258 2 0.751
#> 2259 4 0.502
#> 2260 3 0.751
#> 2261 2 1.000
#> 2262 2 0.000
#> 2263 2 0.000
#> 2264 2 0.249
#> 2265 2 0.000
#> 2266 3 0.253
#> 2267 2 1.000
#> 2268 2 0.000
#> 2269 3 1.000
#> 2270 2 0.000
#> 2271 2 0.000
#> 2272 2 1.000
#> 2273 4 0.000
#> 2274 2 0.000
#> 2275 4 0.000
#> 2276 2 0.000
#> 2277 2 0.000
#> 2278 3 1.000
#> 2279 3 0.498
#> 2280 4 0.000
#> 2281 2 0.000
#> 2282 2 0.000
#> 2283 4 0.000
#> 2284 2 0.000
#> 2285 2 0.000
#> 2286 2 0.000
#> 2287 4 0.000
#> 2288 3 1.000
#> 2289 4 0.747
#> 2290 2 0.000
#> 2291 2 0.000
#> 2292 3 1.000
#> 2293 2 0.498
#> 2294 2 0.000
#> 2295 4 0.000
#> 2296 2 0.000
#> 2297 2 0.000
#> 2298 4 0.502
#> 2299 1 1.000
#> 2300 4 0.000
#> 2301 4 0.000
#> 2302 4 0.000
#> 2303 4 0.000
#> 2304 2 0.000
#> 2305 4 0.000
#> 2306 2 0.747
#> 2307 4 0.000
#> 2308 4 0.000
#> 2309 4 0.000
#> 2310 2 0.000
#> 2311 4 0.000
#> 2312 4 1.000
#> 2313 2 0.000
#> 2314 4 0.000
#> 2315 4 0.000
#> 2316 4 1.000
#> 2317 1 1.000
#> 2318 2 0.000
#> 2319 4 0.000
#> 2320 2 0.000
#> 2321 4 0.000
#> 2322 2 0.000
#> 2323 2 0.000
#> 2324 3 0.751
#> 2325 2 0.000
#> 2326 2 0.000
#> 2327 2 1.000
#> 2328 2 0.000
#> 2329 4 0.000
#> 2330 2 0.000
#> 2331 2 0.000
#> 2332 2 0.000
#> 2333 2 0.000
#> 2334 2 0.000
#> 2335 3 0.751
#> 2336 4 0.000
#> 2337 4 0.000
#> 2338 3 0.498
#> 2339 2 0.000
#> 2340 4 0.751
#> 2341 3 0.000
#> 2342 3 0.751
#> 2343 2 0.000
#> 2344 2 0.000
#> 2345 2 1.000
#> 2346 2 0.000
#> 2347 2 0.000
#> 2348 2 0.000
#> 2349 2 0.000
#> 2350 2 0.000
#> 2351 4 0.000
#> 2352 2 1.000
#> 2353 2 0.000
#> 2354 2 0.000
#> 2355 2 0.000
#> 2356 2 1.000
#> 2357 4 0.000
#> 2358 2 0.253
#> 2359 2 0.751
#> 2360 2 1.000
#> 2361 2 1.000
#> 2362 2 0.000
#> 2363 2 0.000
#> 2364 2 1.000
#> 2365 2 0.000
#> 2366 2 0.751
#> 2367 2 0.249
#> 2368 2 1.000
#> 2369 2 1.000
#> 2370 2 1.000
#> 2371 2 0.000
#> 2372 2 0.000
#> 2373 2 0.000
#> 2374 2 0.498
#> 2375 2 0.249
#> 2376 2 0.000
#> 2377 2 0.000
#> 2378 2 0.000
#> 2379 2 0.000
#> 2380 2 0.498
#> 2381 2 1.000
#> 2382 2 0.000
#> 2383 2 0.000
#> 2384 2 0.000
#> 2385 2 1.000
#> 2386 2 0.000
#> 2387 2 0.000
#> 2388 2 1.000
#> 2389 2 0.000
#> 2390 4 0.000
#> 2391 2 0.502
#> 2392 2 0.000
#> 2393 2 0.000
#> 2394 2 1.000
#> 2395 2 0.249
#> 2396 2 0.000
#> 2397 2 0.000
#> 2398 2 1.000
#> 2399 4 0.000
#> 2400 2 0.249
#> 2401 4 1.000
#> 2402 3 0.751
#> 2403 1 1.000
#> 2404 2 0.000
#> 2405 2 0.000
#> 2406 2 1.000
#> 2407 2 1.000
#> 2408 2 0.747
#> 2409 2 0.502
#> 2410 2 0.249
#> 2411 2 0.000
#> 2412 2 0.000
#> 2413 2 0.000
#> 2414 4 0.000
#> 2415 2 0.000
#> 2416 2 0.000
#> 2417 2 0.000
#> 2418 2 0.000
#> 2419 4 0.000
#> 2420 1 1.000
#> 2421 2 0.000
#> 2422 2 1.000
#> 2423 3 0.751
#> 2424 2 1.000
#> 2425 2 0.751
#> 2426 2 1.000
#> 2427 2 0.000
#> 2428 4 1.000
#> 2429 2 0.000
#> 2430 2 0.000
#> 2431 2 0.000
#> 2432 2 0.000
#> 2433 4 0.751
#> 2434 3 0.498
#> 2435 1 0.249
#> 2436 2 0.502
#> 2437 4 1.000
#> 2438 3 1.000
#> 2439 3 0.751
#> 2440 4 1.000
#> 2441 4 0.253
#> 2442 1 0.000
#> 2443 3 1.000
#> 2444 3 0.249
#> 2445 4 0.000
#> 2446 2 0.000
#> 2447 2 0.000
#> 2448 2 0.000
#> 2449 2 0.000
#> 2450 2 0.000
#> 2451 2 0.751
#> 2452 2 0.253
#> 2453 2 1.000
#> 2454 2 1.000
#> 2455 2 0.000
#> 2456 2 0.000
#> 2457 2 0.000
#> 2458 2 0.751
#> 2459 2 0.000
#> 2460 2 1.000
#> 2461 2 0.000
#> 2462 2 1.000
#> 2463 2 0.000
#> 2464 2 1.000
#> 2465 2 0.249
#> 2466 2 0.253
#> 2467 2 1.000
#> 2468 2 0.498
#> 2469 2 0.249
#> 2470 2 1.000
#> 2471 3 0.249
#> 2472 2 0.253
#> 2473 2 0.249
#> 2474 2 1.000
#> 2475 2 0.000
#> 2476 2 0.498
#> 2477 2 0.751
#> 2478 2 0.000
#> 2479 2 0.249
#> 2480 2 1.000
#> 2481 2 0.000
#> 2482 2 0.000
#> 2483 2 1.000
#> 2484 2 0.000
#> 2485 2 0.498
#> 2486 2 1.000
#> 2487 2 1.000
#> 2488 2 1.000
#> 2489 2 0.502
#> 2490 2 0.249
#> 2491 2 1.000
#> 2492 2 0.000
#> 2493 2 0.747
#> 2494 2 1.000
#> 2495 2 0.000
#> 2496 2 0.000
#> 2497 2 0.000
#> 2498 2 0.000
#> 2499 2 0.000
#> 2500 2 0.000
#> 2501 2 0.000
#> 2502 2 0.000
#> 2503 2 0.000
#> 2504 2 0.000
#> 2505 2 0.253
#> 2506 2 0.000
#> 2507 2 0.000
#> 2508 4 1.000
#> 2509 3 0.502
#> 2510 2 0.249
#> 2511 4 1.000
#> 2512 2 0.000
#> 2513 2 1.000
#> 2514 2 0.000
#> 2515 2 0.000
#> 2516 2 0.000
#> 2517 2 0.249
#> 2518 2 0.000
#> 2519 2 0.249
#> 2520 2 0.000
#> 2521 2 0.000
#> 2522 2 1.000
#> 2523 2 0.000
#> 2524 2 0.498
#> 2525 2 0.000
#> 2526 2 0.000
#> 2527 2 0.000
#> 2528 2 0.000
#> 2529 2 0.751
#> 2530 2 0.000
#> 2531 2 0.000
#> 2532 2 0.000
#> 2533 2 0.000
#> 2534 4 1.000
#> 2535 2 0.000
#> 2536 3 0.000
#> 2537 2 0.000
#> 2538 2 0.751
#> 2539 2 0.249
#> 2540 4 0.000
#> 2541 2 0.502
#> 2542 2 0.000
#> 2543 2 0.000
#> 2544 2 0.000
#> 2545 2 1.000
#> 2546 2 0.000
#> 2547 2 0.000
#> 2548 2 0.502
#> 2549 4 0.000
#> 2550 2 0.000
#> 2551 4 0.000
#> 2552 4 0.000
#> 2553 4 0.000
#> 2554 4 0.000
#> 2555 2 0.000
#> 2556 3 0.751
#> 2557 2 0.249
#> 2558 4 0.000
#> 2559 2 0.000
#> 2560 2 0.000
#> 2561 3 0.000
#> 2562 2 0.249
#> 2563 4 0.751
#> 2564 2 0.000
#> 2565 2 0.000
#> 2566 3 0.000
#> 2567 2 0.000
#> 2568 2 0.000
#> 2569 2 0.000
#> 2570 2 0.000
#> 2571 2 0.000
#> 2572 4 0.000
#> 2573 4 0.000
#> 2574 4 1.000
#> 2575 4 0.751
#> 2576 4 0.502
#> 2577 2 0.000
#> 2578 2 0.000
#> 2579 2 0.000
#> 2580 2 0.249
#> 2581 2 0.000
#> 2582 2 0.000
#> 2583 2 0.000
#> 2584 2 0.000
#> 2585 2 0.000
#> 2586 2 0.000
#> 2587 2 0.000
#> 2588 4 0.000
#> 2589 2 1.000
#> 2590 2 1.000
#> 2591 4 0.000
#> 2592 2 1.000
#> 2593 2 1.000
#> 2594 2 1.000
#> 2595 2 1.000
#> 2596 2 1.000
#> 2597 2 1.000
#> 2598 2 1.000
#> 2599 2 1.000
#> 2600 2 1.000
#> 2601 2 1.000
#> 2602 2 1.000
#> 2603 2 1.000
#> 2604 2 1.000
#> 2605 2 1.000
#> 2606 4 0.000
#> 2607 2 1.000
#> 2608 2 1.000
#> 2609 4 0.000
#> 2610 4 0.000
#> 2611 2 1.000
#> 2612 2 0.000
#> 2613 2 1.000
#> 2614 2 0.000
#> 2615 2 0.000
#> 2616 4 0.000
#> 2617 2 1.000
#> 2618 2 1.000
#> 2619 4 0.000
#> 2620 4 1.000
#> 2621 2 1.000
#> 2622 2 1.000
#> 2623 4 0.000
#> 2624 2 0.751
#> 2625 2 1.000
#> 2626 4 0.000
#> 2627 4 0.000
#> 2628 4 0.000
#> 2629 1 1.000
#> 2630 4 0.000
#> 2631 2 0.000
#> 2632 2 0.000
#> 2633 2 1.000
#> 2634 2 1.000
#> 2635 2 1.000
#> 2636 2 0.000
#> 2637 2 0.502
#> 2638 2 0.000
#> 2639 2 1.000
#> 2640 2 0.751
#> 2641 2 1.000
#> 2642 3 0.000
#> 2643 2 1.000
#> 2644 4 1.000
#> 2645 2 0.000
#> 2646 2 0.000
#> 2647 2 0.751
#> 2648 2 0.000
#> 2649 3 0.000
#> 2650 2 0.000
#> 2651 1 0.000
#> 2652 2 0.000
#> 2653 2 0.000
#> 2654 2 0.498
#> 2655 2 0.249
#> 2656 2 0.000
#> 2657 4 0.000
#> 2658 2 0.000
#> 2659 2 0.000
#> 2660 2 0.000
#> 2661 2 0.000
#> 2662 2 0.747
#> 2663 2 0.000
#> 2664 3 0.751
#> 2665 4 1.000
#> 2666 2 0.000
#> 2667 3 0.249
#> 2668 2 1.000
#> 2669 2 1.000
#> 2670 4 0.000
#> 2671 2 0.000
#> 2672 1 0.000
#> 2673 4 0.249
#> 2674 2 0.502
#> 2675 3 0.751
#> 2676 4 1.000
#> 2677 2 0.000
#> 2678 3 0.751
#> 2679 1 0.000
#> 2680 4 0.000
#> 2681 1 0.000
#> 2682 2 1.000
#> 2683 1 1.000
#> 2684 1 0.000
#> 2685 1 0.000
#> 2686 2 1.000
#> 2687 2 1.000
#> 2688 2 1.000
#> 2689 2 0.751
#> 2690 3 1.000
#> 2691 2 0.000
#> 2692 2 0.000
#> 2693 2 1.000
#> 2694 2 1.000
#> 2695 4 0.000
#> 2696 2 0.000
#> 2697 3 0.000
#> 2698 2 0.502
#> 2699 2 0.751
#> 2700 4 0.000
#> 2701 2 0.751
#> 2702 2 1.000
#> 2703 1 1.000
#> 2704 2 0.000
#> 2705 2 0.249
#> 2706 3 0.498
#> 2707 4 0.000
#> 2708 2 1.000
#> 2709 4 0.000
#> 2710 4 0.000
#> 2711 2 0.751
#> 2712 1 1.000
#> 2713 4 0.751
#> 2714 4 0.000
#> 2715 2 0.000
#> 2716 2 1.000
#> 2717 4 0.000
#> 2718 4 0.000
#> 2719 4 0.000
#> 2720 4 0.000
#> 2721 2 0.000
#> 2722 2 1.000
#> 2723 4 1.000
#> 2724 2 1.000
#> 2725 4 0.000
#> 2726 1 0.249
#> 2727 4 0.000
#> 2728 4 0.000
#> 2729 1 1.000
#> 2730 4 1.000
#> 2731 2 0.502
#> 2732 2 0.249
#> 2733 4 0.498
#> 2734 2 0.000
#> 2735 4 1.000
#> 2736 4 0.000
#> 2737 4 0.747
#> 2738 4 0.000
#> 2739 1 1.000
#> 2740 4 1.000
#> 2741 1 0.000
#> 2742 3 1.000
#> 2743 2 0.000
#> 2744 2 1.000
#> 2745 2 0.000
#> 2746 4 0.249
#> 2747 1 0.249
#> 2748 4 0.249
#> 2749 4 0.000
#> 2750 4 0.253
#> 2751 4 0.498
#> 2752 1 0.000
#> 2753 2 0.000
#> 2754 2 0.000
#> 2755 4 0.751
#> 2756 4 0.000
#> 2757 4 0.000
#> 2758 2 0.751
#> 2759 2 1.000
#> 2760 2 1.000
#> 2761 2 0.249
#> 2762 2 1.000
#> 2763 2 0.249
#> 2764 2 0.249
#> 2765 2 1.000
#> 2766 4 0.000
#> 2767 4 0.000
#> 2768 2 1.000
#> 2769 2 1.000
#> 2770 2 1.000
#> 2771 2 0.502
#> 2772 2 0.751
#> 2773 2 0.000
#> 2774 2 1.000
#> 2775 2 0.253
#> 2776 2 1.000
#> 2777 2 0.000
#> 2778 2 0.000
#> 2779 2 1.000
#> 2780 2 0.751
#> 2781 2 0.000
#> 2782 2 1.000
#> 2783 2 1.000
#> 2784 2 0.747
#> 2785 2 1.000
#> 2786 4 1.000
#> 2787 2 1.000
#> 2788 4 0.000
#> 2789 2 1.000
#> 2790 4 0.000
#> 2791 4 0.000
#> 2792 2 1.000
#> 2793 4 0.000
#> 2794 4 0.000
#> 2795 2 1.000
#> 2796 2 1.000
#> 2797 2 0.000
#> 2798 2 0.249
#> 2799 2 0.000
#> 2800 2 0.498
#> 2801 2 0.751
#> 2802 2 0.000
#> 2803 2 1.000
#> 2804 2 0.000
#> 2805 2 1.000
#> 2806 2 1.000
#> 2807 2 1.000
#> 2808 2 1.000
#> 2809 2 1.000
#> 2810 2 0.249
#> 2811 2 1.000
#> 2812 2 0.000
#> 2813 2 0.751
#> 2814 2 0.498
#> 2815 2 0.000
#> 2816 2 0.000
#> 2817 4 0.000
#> 2818 4 0.000
#> 2819 4 0.000
#> 2820 4 0.000
#> 2821 2 0.751
#> 2822 2 0.751
#> 2823 4 0.000
#> 2824 2 0.000
#> 2825 2 0.498
#> 2826 2 0.751
#> 2827 2 0.502
#> 2828 2 0.751
#> 2829 2 0.253
#> 2830 2 0.000
#> 2831 2 0.751
#> 2832 2 0.751
#> 2833 2 1.000
#> 2834 2 0.000
#> 2835 2 1.000
#> 2836 2 1.000
#> 2837 2 0.751
#> 2838 2 0.000
#> 2839 2 1.000
#> 2840 2 0.751
#> 2841 2 1.000
#> 2842 2 0.751
#> 2843 2 0.000
#> 2844 2 0.000
#> 2845 2 0.000
#> 2846 2 0.502
#> 2847 2 0.000
#> 2848 2 0.000
#> 2849 2 0.000
#> 2850 2 1.000
#> 2851 2 1.000
#> 2852 2 0.000
#> 2853 4 0.000
#> 2854 4 1.000
#> 2855 4 0.000
#> 2856 4 0.000
#> 2857 2 0.751
#> 2858 4 1.000
#> 2859 2 0.502
#> 2860 2 1.000
#> 2861 2 0.502
#> 2862 2 1.000
#> 2863 2 0.000
#> 2864 2 0.000
#> 2865 2 0.000
#> 2866 2 0.498
#> 2867 2 0.751
#> 2868 2 0.000
#> 2869 2 0.000
#> 2870 2 1.000
#> 2871 2 0.751
#> 2872 2 1.000
#> 2873 2 1.000
#> 2874 2 1.000
#> 2875 2 0.000
#> 2876 2 1.000
#> 2877 2 0.000
#> 2878 2 0.498
#> 2879 4 0.000
#> 2880 2 0.502
#> 2881 2 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 2168 1.90e-307 2
#> ATC:skmeans 1745 1.06e-316 3
#> ATC:skmeans 1417 6.08e-269 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013 , Node014 , Node021 , Node022 , Node023 , Node031 , Node032 , Node033 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["01"]
A summary of res and all the functions that can be applied to it:
res
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 10389 rows and 500 columns, randomly sampled from 1273 columns.
#> Top rows (983) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1 0.995 0.998 0.4763 0.524 0.524
#> 3 3 1 0.969 0.984 0.3815 0.802 0.627
#> 4 4 1 0.972 0.989 0.0869 0.918 0.769
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
get_classes(res, k = 2)
#> class p
#> 1 2 1.000
#> 2 2 1.000
#> 3 2 0.000
#> 4 2 1.000
#> 5 1 0.000
#> 6 2 1.000
#> 7 2 1.000
#> 8 2 1.000
#> 9 1 0.000
#> 10 2 1.000
#> 11 2 1.000
#> 12 2 1.000
#> 13 2 1.000
#> 14 2 0.751
#> 15 1 0.000
#> 16 2 1.000
#> 17 1 0.000
#> 18 2 1.000
#> 19 2 1.000
#> 20 2 1.000
#> 21 1 0.751
#> 22 1 1.000
#> 23 2 1.000
#> 24 1 0.000
#> 25 1 0.000
#> 26 1 0.000
#> 27 2 1.000
#> 28 1 0.000
#> 29 1 0.000
#> 30 1 0.000
#> 31 2 1.000
#> 32 1 0.253
#> 33 1 0.000
#> 34 1 0.000
#> 35 1 0.000
#> 36 1 0.000
#> 37 1 0.000
#> 38 2 0.751
#> 39 2 1.000
#> 40 2 1.000
#> 41 1 0.000
#> 42 1 0.000
#> 43 1 0.000
#> 44 1 1.000
#> 45 1 0.000
#> 46 1 0.000
#> 47 1 0.000
#> 48 1 0.000
#> 49 1 0.000
#> 50 1 0.000
#> 51 1 0.000
#> 52 1 0.000
#> 53 1 0.000
#> 54 2 1.000
#> 55 1 0.253
#> 56 1 0.000
#> 57 1 0.000
#> 58 1 0.000
#> 59 1 0.000
#> 60 1 0.000
#> 61 1 0.000
#> 62 1 0.000
#> 63 1 0.000
#> 64 1 1.000
#> 65 1 0.000
#> 66 2 1.000
#> 67 2 1.000
#> 68 2 0.502
#> 69 1 0.000
#> 70 1 0.000
#> 71 1 0.000
#> 72 1 0.000
#> 73 1 0.000
#> 74 1 0.000
#> 75 1 0.000
#> 76 2 1.000
#> 77 1 0.000
#> 78 1 0.000
#> 79 1 0.000
#> 80 2 1.000
#> 81 1 1.000
#> 82 1 0.000
#> 83 1 0.751
#> 84 1 0.000
#> 85 1 0.000
#> 86 1 0.000
#> 87 1 0.000
#> 88 2 1.000
#> 89 2 1.000
#> 90 2 1.000
#> 91 2 1.000
#> 92 1 0.000
#> 93 1 0.000
#> 94 1 0.000
#> 95 1 0.000
#> 96 1 0.000
#> 97 1 0.000
#> 98 1 0.000
#> 99 1 0.000
#> 100 1 0.000
#> 101 1 0.000
#> 102 1 0.000
#> 103 1 0.000
#> 104 1 0.000
#> 105 1 0.000
#> 106 1 0.000
#> 107 1 0.000
#> 108 1 0.000
#> 109 1 0.000
#> 110 1 0.000
#> 111 1 0.000
#> 112 1 0.000
#> 113 1 0.000
#> 114 1 0.000
#> 115 1 0.000
#> 116 1 0.000
#> 117 1 0.000
#> 118 1 0.000
#> 119 1 0.000
#> 120 1 0.000
#> 121 1 0.000
#> 122 1 0.000
#> 123 1 0.000
#> 124 1 0.000
#> 125 1 0.000
#> 126 1 0.000
#> 127 1 0.000
#> 128 1 0.000
#> 129 1 0.000
#> 130 1 0.000
#> 131 1 0.000
#> 132 1 0.000
#> 133 1 0.000
#> 134 1 0.000
#> 135 1 0.000
#> 136 1 0.000
#> 137 1 0.000
#> 138 1 0.000
#> 139 1 0.000
#> 140 1 0.000
#> 141 1 0.000
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 1 0.000
#> 147 1 0.000
#> 148 1 0.000
#> 149 1 0.000
#> 150 1 0.000
#> 151 1 0.000
#> 152 1 0.000
#> 153 1 0.000
#> 154 1 0.000
#> 155 1 0.000
#> 156 1 0.000
#> 157 1 0.000
#> 158 1 0.000
#> 159 1 0.000
#> 160 1 0.000
#> 161 1 0.000
#> 162 1 0.000
#> 163 1 0.000
#> 164 1 0.000
#> 165 1 0.000
#> 166 1 0.000
#> 167 1 0.000
#> 168 1 0.000
#> 169 1 0.000
#> 170 1 0.000
#> 171 1 0.000
#> 172 1 0.000
#> 173 1 0.000
#> 174 1 0.000
#> 175 1 0.000
#> 176 1 0.000
#> 177 1 0.000
#> 178 1 0.000
#> 179 1 0.000
#> 180 1 0.000
#> 181 1 0.000
#> 182 1 0.000
#> 183 1 0.000
#> 184 1 0.000
#> 185 1 0.000
#> 186 1 0.000
#> 187 1 0.000
#> 188 1 0.000
#> 189 1 0.000
#> 190 1 0.000
#> 191 1 0.000
#> 192 1 0.000
#> 193 1 0.000
#> 194 1 0.000
#> 195 1 0.000
#> 196 1 0.000
#> 197 1 0.000
#> 198 1 0.000
#> 199 1 0.000
#> 200 1 0.000
#> 201 1 0.000
#> 202 1 0.000
#> 203 1 0.000
#> 204 1 0.000
#> 205 1 0.000
#> 206 1 0.000
#> 207 1 0.000
#> 208 1 0.000
#> 209 1 0.000
#> 210 1 0.000
#> 211 1 0.000
#> 212 1 0.000
#> 213 1 0.000
#> 214 1 0.000
#> 215 1 0.000
#> 216 1 0.000
#> 217 1 0.000
#> 218 1 0.000
#> 219 1 0.000
#> 220 1 0.000
#> 221 1 0.000
#> 222 1 0.000
#> 223 1 0.000
#> 224 1 0.000
#> 225 1 0.000
#> 226 1 0.000
#> 227 1 0.000
#> 228 1 0.000
#> 229 1 0.000
#> 230 1 0.000
#> 231 1 0.000
#> 232 1 0.000
#> 233 1 0.000
#> 234 1 0.000
#> 235 1 0.000
#> 236 1 0.000
#> 237 1 0.000
#> 238 1 0.000
#> 239 1 0.000
#> 240 1 0.000
#> 241 1 0.000
#> 242 1 0.000
#> 243 1 0.000
#> 244 1 0.000
#> 245 1 0.000
#> 246 1 0.000
#> 247 1 0.000
#> 248 1 0.000
#> 249 1 0.000
#> 250 1 0.000
#> 251 1 0.000
#> 252 1 0.000
#> 253 1 0.000
#> 254 1 0.000
#> 255 1 0.000
#> 256 1 0.000
#> 257 1 0.000
#> 258 1 0.000
#> 259 1 0.000
#> 260 1 0.000
#> 261 1 0.000
#> 262 1 0.000
#> 263 1 0.000
#> 264 1 0.000
#> 265 1 0.000
#> 266 1 0.000
#> 267 1 0.000
#> 268 1 0.000
#> 269 1 0.000
#> 270 1 0.000
#> 271 1 0.000
#> 272 1 0.000
#> 273 1 0.000
#> 274 1 0.000
#> 275 1 0.000
#> 276 1 0.000
#> 277 1 0.000
#> 278 1 0.000
#> 279 1 0.000
#> 280 1 0.000
#> 281 1 0.000
#> 282 1 0.000
#> 283 1 0.000
#> 284 1 0.000
#> 285 1 0.000
#> 286 1 0.000
#> 287 1 0.000
#> 288 1 0.000
#> 289 1 0.000
#> 290 1 0.000
#> 291 1 0.000
#> 292 1 0.000
#> 293 1 0.000
#> 294 1 0.000
#> 295 1 0.000
#> 296 1 0.000
#> 297 1 0.000
#> 298 1 0.000
#> 299 1 0.000
#> 300 1 0.000
#> 301 1 0.000
#> 302 1 0.000
#> 303 1 0.000
#> 304 1 0.000
#> 305 1 0.000
#> 306 1 0.000
#> 307 1 0.000
#> 308 1 0.000
#> 309 1 0.000
#> 310 1 0.000
#> 311 1 0.000
#> 312 1 0.000
#> 313 1 0.000
#> 314 1 0.000
#> 315 1 0.000
#> 316 1 0.000
#> 317 1 0.000
#> 318 1 0.000
#> 319 1 0.000
#> 320 1 0.000
#> 321 1 0.000
#> 322 1 0.000
#> 323 2 1.000
#> 324 1 0.000
#> 325 1 0.000
#> 326 1 0.000
#> 327 1 1.000
#> 328 1 0.000
#> 329 1 0.000
#> 330 1 0.000
#> 331 1 0.000
#> 332 1 0.000
#> 333 1 0.000
#> 334 1 0.000
#> 335 1 0.000
#> 336 1 0.000
#> 337 1 0.000
#> 338 1 0.000
#> 339 1 0.000
#> 340 1 0.249
#> 341 2 1.000
#> 342 1 0.000
#> 343 1 0.000
#> 344 1 0.000
#> 345 1 0.000
#> 346 1 0.000
#> 347 1 0.000
#> 348 1 0.000
#> 349 1 0.000
#> 350 2 1.000
#> 351 1 1.000
#> 352 1 0.000
#> 353 1 0.000
#> 354 1 0.000
#> 355 1 0.000
#> 356 2 1.000
#> 357 1 0.000
#> 358 1 0.000
#> 359 1 0.000
#> 360 1 0.000
#> 361 1 0.000
#> 362 1 0.000
#> 363 1 0.000
#> 364 1 0.000
#> 365 1 0.000
#> 366 1 0.000
#> 367 1 1.000
#> 368 1 0.000
#> 369 1 0.000
#> 370 1 0.000
#> 371 1 0.000
#> 372 1 0.000
#> 373 1 0.000
#> 374 1 0.000
#> 375 1 0.000
#> 376 1 0.000
#> 377 1 0.000
#> 378 1 0.000
#> 379 1 0.000
#> 380 1 1.000
#> 381 1 0.000
#> 382 1 0.000
#> 383 1 0.000
#> 384 1 0.000
#> 385 2 1.000
#> 386 1 0.000
#> 387 1 0.000
#> 388 1 0.000
#> 389 1 0.000
#> 390 1 0.000
#> 391 1 0.000
#> 392 1 0.498
#> 393 1 1.000
#> 394 1 1.000
#> 395 1 1.000
#> 396 1 1.000
#> 397 1 0.000
#> 398 1 0.000
#> 399 1 0.000
#> 400 1 0.000
#> 401 1 0.000
#> 402 1 0.000
#> 403 1 0.000
#> 404 1 0.000
#> 405 1 0.000
#> 406 1 0.000
#> 407 1 0.000
#> 408 1 0.000
#> 409 1 0.000
#> 410 1 0.000
#> 411 2 1.000
#> 412 2 1.000
#> 413 2 1.000
#> 414 1 0.000
#> 415 1 0.502
#> 416 1 0.000
#> 417 1 0.000
#> 418 1 0.000
#> 419 1 0.000
#> 420 1 0.000
#> 421 1 0.000
#> 422 1 0.000
#> 423 1 0.000
#> 424 1 0.000
#> 425 1 0.000
#> 426 1 0.000
#> 427 1 0.000
#> 428 1 0.000
#> 429 1 0.253
#> 430 1 0.000
#> 431 1 0.000
#> 432 1 0.000
#> 433 1 1.000
#> 434 1 0.000
#> 435 1 0.000
#> 436 1 0.000
#> 437 1 0.000
#> 438 1 0.000
#> 439 1 0.000
#> 440 1 0.000
#> 441 1 0.000
#> 442 1 0.000
#> 443 1 0.000
#> 444 1 0.000
#> 445 1 0.000
#> 446 1 0.000
#> 447 1 0.000
#> 448 1 0.751
#> 449 1 0.000
#> 450 1 0.000
#> 451 1 0.000
#> 452 1 0.000
#> 453 1 1.000
#> 454 1 1.000
#> 455 1 0.000
#> 456 2 1.000
#> 457 1 0.000
#> 458 1 0.249
#> 459 1 0.000
#> 460 1 0.000
#> 461 1 0.000
#> 462 1 0.000
#> 463 1 0.000
#> 464 1 0.000
#> 465 1 0.000
#> 466 1 0.000
#> 467 1 0.000
#> 468 1 0.000
#> 469 1 0.000
#> 470 1 0.000
#> 471 1 0.000
#> 472 2 0.000
#> 473 2 0.000
#> 474 2 0.000
#> 475 2 0.000
#> 476 2 0.000
#> 477 2 0.000
#> 478 2 0.000
#> 479 2 0.000
#> 480 2 0.000
#> 481 2 0.000
#> 482 2 0.000
#> 483 2 0.000
#> 484 2 0.000
#> 485 2 0.000
#> 486 2 0.000
#> 487 2 0.000
#> 488 2 0.000
#> 489 2 0.000
#> 490 2 0.000
#> 491 2 0.000
#> 492 2 0.000
#> 493 2 0.000
#> 494 2 0.000
#> 495 2 0.000
#> 496 2 0.000
#> 497 2 0.000
#> 498 2 0.000
#> 499 2 0.000
#> 500 2 0.000
#> 501 2 0.000
#> 502 2 0.000
#> 503 2 0.000
#> 504 2 0.000
#> 505 2 0.000
#> 506 1 0.502
#> 507 2 0.000
#> 508 2 0.000
#> 509 2 0.000
#> 510 2 0.000
#> 511 2 0.000
#> 512 2 0.000
#> 513 1 1.000
#> 514 2 0.000
#> 515 2 0.000
#> 516 2 0.000
#> 517 2 0.000
#> 518 2 0.000
#> 519 2 0.000
#> 520 2 0.000
#> 521 1 0.000
#> 522 2 0.000
#> 523 2 0.000
#> 524 2 0.000
#> 525 2 0.000
#> 526 2 0.000
#> 527 2 0.000
#> 528 2 0.000
#> 529 2 0.000
#> 530 2 0.000
#> 531 2 0.000
#> 532 2 0.000
#> 533 2 0.000
#> 534 2 0.000
#> 535 2 0.000
#> 536 2 0.000
#> 537 2 0.000
#> 538 2 0.000
#> 539 2 0.000
#> 540 2 0.000
#> 541 2 0.000
#> 542 2 0.000
#> 543 2 0.000
#> 544 2 0.000
#> 545 2 0.000
#> 546 2 0.000
#> 547 2 0.000
#> 548 2 0.000
#> 549 2 0.000
#> 550 2 0.000
#> 551 2 0.000
#> 552 1 0.000
#> 553 2 0.000
#> 554 2 0.000
#> 555 2 0.000
#> 556 2 0.000
#> 557 2 0.000
#> 558 2 0.000
#> 559 2 0.000
#> 560 2 0.000
#> 561 2 0.000
#> 562 2 0.000
#> 563 2 0.000
#> 564 2 0.000
#> 565 2 0.000
#> 566 2 0.000
#> 567 2 0.000
#> 568 2 0.000
#> 569 2 0.000
#> 570 2 0.000
#> 571 2 0.000
#> 572 2 0.000
#> 573 2 0.000
#> 574 2 0.000
#> 575 2 0.000
#> 576 2 0.000
#> 577 2 0.000
#> 578 2 0.000
#> 579 2 0.000
#> 580 2 0.000
#> 581 2 0.000
#> 582 2 0.000
#> 583 2 0.000
#> 584 2 0.000
#> 585 2 0.000
#> 586 2 0.000
#> 587 1 0.751
#> 588 1 1.000
#> 589 2 0.000
#> 590 2 0.000
#> 591 2 0.000
#> 592 2 0.000
#> 593 2 0.000
#> 594 2 0.000
#> 595 2 0.000
#> 596 2 0.000
#> 597 1 0.000
#> 598 2 0.000
#> 599 2 0.000
#> 600 2 1.000
#> 601 2 0.000
#> 602 2 0.000
#> 603 2 0.000
#> 604 2 0.000
#> 605 2 0.000
#> 606 2 0.000
#> 607 2 0.000
#> 608 2 0.000
#> 609 2 0.000
#> 610 2 0.000
#> 611 2 0.000
#> 612 2 0.000
#> 613 1 0.000
#> 614 1 0.747
#> 615 1 0.000
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.000
#> 619 2 0.000
#> 620 1 0.000
#> 621 1 0.000
#> 622 1 0.000
#> 623 2 0.000
#> 624 1 0.000
#> 625 2 0.000
#> 626 1 0.751
#> 627 1 0.000
#> 628 2 0.000
#> 629 2 0.000
#> 630 2 0.000
#> 631 2 0.000
#> 632 2 0.000
#> 633 2 0.000
#> 634 2 0.000
#> 635 1 0.498
#> 636 2 0.000
#> 637 2 0.000
#> 638 1 0.000
#> 639 2 0.000
#> 640 2 0.000
#> 641 2 0.000
#> 642 2 0.000
#> 643 2 0.000
#> 644 2 0.000
#> 645 2 0.000
#> 646 2 1.000
#> 647 2 0.000
#> 648 2 0.000
#> 649 2 0.000
#> 650 2 0.000
#> 651 2 0.000
#> 652 2 0.000
#> 653 2 0.000
#> 654 2 0.000
#> 655 2 0.000
#> 656 2 0.000
#> 657 1 0.000
#> 658 2 0.000
#> 659 2 0.000
#> 660 2 0.000
#> 661 2 0.000
#> 662 2 0.000
#> 663 2 0.000
#> 664 2 0.000
#> 665 2 0.000
#> 666 2 0.000
#> 667 1 0.000
#> 668 2 0.000
#> 669 2 0.000
#> 670 2 0.000
#> 671 2 0.000
#> 672 2 0.000
#> 673 2 0.000
#> 674 2 0.000
#> 675 2 0.000
#> 676 2 0.000
#> 677 2 0.000
#> 678 1 0.000
#> 679 2 0.000
#> 680 1 0.000
#> 681 1 0.000
#> 682 2 0.000
#> 683 2 0.000
#> 684 2 0.000
#> 685 2 0.000
#> 686 2 0.000
#> 687 1 0.000
#> 688 1 0.000
#> 689 2 0.000
#> 690 2 0.000
#> 691 1 0.000
#> 692 2 0.000
#> 693 1 0.000
#> 694 1 0.000
#> 695 1 0.000
#> 696 2 0.000
#> 697 2 0.000
#> 698 2 0.000
#> 699 2 0.000
#> 700 2 0.000
#> 701 2 0.000
#> 702 1 0.000
#> 703 1 0.000
#> 704 2 0.000
#> 705 2 0.000
#> 706 2 0.000
#> 707 2 0.000
#> 708 2 0.000
#> 709 2 0.000
#> 710 2 0.000
#> 711 2 0.000
#> 712 2 0.000
#> 713 2 0.000
#> 714 2 0.000
#> 715 2 0.000
#> 716 2 0.000
#> 717 2 0.000
#> 718 2 0.000
#> 719 2 0.000
#> 720 2 0.000
#> 721 2 0.000
#> 722 2 0.000
#> 723 2 0.000
#> 724 2 0.000
#> 725 2 0.000
#> 726 2 0.000
#> 727 2 0.000
#> 728 2 0.000
#> 729 2 0.000
#> 730 2 0.000
#> 731 2 0.000
#> 732 2 0.000
#> 733 2 0.000
#> 734 2 0.000
#> 735 2 0.000
#> 736 2 0.000
#> 737 2 0.000
#> 738 2 0.000
#> 739 2 0.000
#> 740 2 0.000
#> 741 2 0.000
#> 742 2 0.000
#> 743 2 0.000
#> 744 2 0.000
#> 745 2 0.000
#> 746 2 0.000
#> 747 2 0.000
#> 748 2 0.000
#> 749 2 0.000
#> 750 2 0.000
#> 751 2 0.000
#> 752 2 0.000
#> 753 2 0.000
#> 754 2 0.000
#> 755 2 0.000
#> 756 2 0.000
#> 757 2 0.000
#> 758 2 0.000
#> 759 2 0.000
#> 760 2 0.000
#> 761 2 0.000
#> 762 2 0.000
#> 763 2 0.000
#> 764 2 0.000
#> 765 2 0.000
#> 766 2 0.000
#> 767 2 0.000
#> 768 2 0.000
#> 769 2 0.000
#> 770 2 0.000
#> 771 2 0.000
#> 772 2 0.000
#> 773 2 0.000
#> 774 2 0.000
#> 775 2 0.000
#> 776 2 0.000
#> 777 2 0.000
#> 778 2 0.000
#> 779 2 0.000
#> 780 2 0.000
#> 781 2 0.000
#> 782 2 0.000
#> 783 2 0.000
#> 784 2 0.000
#> 785 2 0.000
#> 786 2 0.000
#> 787 2 0.000
#> 788 2 0.000
#> 789 2 0.000
#> 790 2 0.000
#> 791 2 0.000
#> 792 2 0.000
#> 793 2 0.000
#> 794 2 0.000
#> 795 2 0.000
#> 796 2 0.000
#> 797 2 0.000
#> 798 2 0.000
#> 799 2 0.000
#> 800 2 0.000
#> 801 2 0.000
#> 802 2 0.000
#> 803 2 0.000
#> 804 2 0.000
#> 805 2 0.000
#> 806 2 0.000
#> 807 2 0.000
#> 808 2 0.000
#> 809 2 0.000
#> 810 2 0.000
#> 811 2 0.000
#> 812 2 0.000
#> 813 2 0.000
#> 814 2 0.000
#> 815 2 0.000
#> 816 2 0.000
#> 817 1 0.000
#> 818 2 0.000
#> 819 2 0.000
#> 820 2 0.000
#> 821 2 0.000
#> 822 2 0.000
#> 823 2 0.000
#> 824 2 0.000
#> 825 2 0.000
#> 826 2 0.000
#> 827 2 0.000
#> 828 2 0.000
#> 829 2 0.000
#> 830 2 0.000
#> 831 2 0.000
#> 832 2 0.000
#> 833 2 0.000
#> 834 2 0.000
#> 835 2 0.000
#> 836 2 0.000
#> 837 2 0.000
#> 838 2 0.000
#> 839 2 0.000
#> 840 2 0.000
#> 841 2 0.000
#> 842 2 0.000
#> 843 2 0.000
#> 844 2 1.000
#> 845 2 0.000
#> 846 1 0.000
#> 847 1 0.000
#> 848 1 0.000
#> 849 2 0.000
#> 850 2 0.000
#> 851 2 0.000
#> 852 1 0.000
#> 853 2 0.000
#> 854 2 0.000
#> 855 2 0.000
#> 856 2 0.000
#> 857 1 0.000
#> 858 2 0.000
#> 859 2 0.000
#> 860 2 0.000
#> 861 2 0.000
#> 862 2 0.000
#> 863 2 0.000
#> 864 2 0.000
#> 865 2 0.000
#> 866 2 1.000
#> 867 2 0.000
#> 868 2 0.000
#> 869 1 0.000
#> 870 2 0.000
#> 871 2 0.000
#> 872 2 0.000
#> 873 2 0.000
#> 874 1 0.000
#> 875 2 0.000
#> 876 2 0.000
#> 877 2 0.000
#> 878 2 0.000
#> 879 2 0.000
#> 880 2 0.000
#> 881 2 0.000
#> 882 2 0.000
#> 883 2 0.000
#> 884 2 0.000
#> 885 2 0.000
#> 886 2 0.000
#> 887 1 0.249
#> 888 2 0.000
#> 889 2 0.000
#> 890 2 0.000
#> 891 2 0.000
#> 892 2 0.000
#> 893 1 1.000
#> 894 2 0.000
#> 895 1 0.000
#> 896 1 0.000
#> 897 2 0.000
#> 898 2 0.000
#> 899 2 1.000
#> 900 2 0.000
#> 901 2 0.000
#> 902 2 0.000
#> 903 2 0.000
#> 904 2 0.000
#> 905 2 0.000
#> 906 2 0.000
#> 907 2 0.000
#> 908 2 0.000
#> 909 2 0.000
#> 910 1 0.253
#> 911 2 0.000
#> 912 2 0.000
#> 913 2 0.000
#> 914 2 0.000
#> 915 2 0.000
#> 916 2 0.000
#> 917 2 0.000
#> 918 1 0.000
#> 919 1 0.000
#> 920 1 0.000
#> 921 2 0.000
#> 922 2 0.000
#> 923 2 0.000
#> 924 2 0.000
#> 925 2 0.000
#> 926 2 0.000
#> 927 2 0.000
#> 928 2 0.000
#> 929 2 0.000
#> 930 2 0.000
#> 931 2 0.000
#> 932 2 0.000
#> 933 1 0.000
#> 934 2 0.000
#> 935 2 0.000
#> 936 2 0.000
#> 937 2 0.249
#> 938 2 0.000
#> 939 2 0.000
#> 940 2 0.000
#> 941 2 0.000
#> 942 2 0.000
#> 943 2 0.000
#> 944 2 0.000
#> 945 2 0.000
#> 946 2 0.000
#> 947 2 0.000
#> 948 2 0.000
#> 949 2 0.000
#> 950 2 0.000
#> 951 2 0.000
#> 952 2 0.000
#> 953 2 0.000
#> 954 2 0.000
#> 955 2 0.000
#> 956 2 0.000
#> 957 2 0.000
#> 958 1 0.000
#> 959 2 1.000
#> 960 1 0.000
#> 961 1 0.000
#> 962 2 0.000
#> 963 2 0.249
#> 964 2 0.000
#> 965 2 0.000
#> 966 2 0.000
#> 967 2 0.000
#> 968 2 0.000
#> 969 2 0.000
#> 970 2 0.000
#> 971 2 0.000
#> 972 2 0.000
#> 973 2 0.000
#> 974 2 0.000
#> 975 2 0.000
#> 976 2 0.000
#> 977 2 0.000
#> 978 2 0.249
#> 979 1 0.000
#> 980 1 0.000
#> 981 2 0.000
#> 982 2 0.000
#> 983 2 0.000
#> 984 2 0.000
#> 985 2 0.000
#> 986 2 0.000
#> 987 1 0.249
#> 988 2 0.000
#> 989 1 0.000
#> 990 2 0.000
#> 991 2 0.000
#> 992 2 0.000
#> 993 2 0.000
#> 994 2 0.000
#> 995 2 0.000
#> 996 2 0.000
#> 997 1 0.000
#> 998 2 0.000
#> 999 2 1.000
#> 1000 1 0.000
#> 1001 2 0.000
#> 1002 2 0.000
#> 1003 2 0.000
#> 1004 2 0.000
#> 1005 2 0.000
#> 1006 2 0.000
#> 1007 2 0.000
#> 1008 2 0.000
#> 1009 2 0.000
#> 1010 2 0.000
#> 1011 2 0.000
#> 1012 2 0.000
#> 1013 2 0.000
#> 1014 2 0.000
#> 1015 2 0.000
#> 1016 2 0.000
#> 1017 2 0.000
#> 1018 2 0.000
#> 1019 2 0.253
#> 1020 2 0.000
#> 1021 2 0.000
#> 1022 2 0.000
#> 1023 2 0.253
#> 1024 2 0.000
#> 1025 2 0.502
#> 1026 1 0.000
#> 1027 2 0.000
#> 1028 1 0.000
#> 1029 1 0.000
#> 1030 1 0.253
#> 1031 2 0.000
#> 1032 2 0.000
#> 1033 2 0.000
#> 1034 2 0.000
#> 1035 2 0.000
#> 1036 2 0.000
#> 1037 2 0.000
#> 1038 2 0.000
#> 1039 2 0.000
#> 1040 2 0.000
#> 1041 2 0.000
#> 1042 2 0.000
#> 1043 1 0.000
#> 1044 2 0.000
#> 1045 2 0.000
#> 1046 2 0.000
#> 1047 1 0.000
#> 1048 2 0.000
#> 1049 1 0.000
#> 1050 1 0.000
#> 1051 2 0.000
#> 1052 2 0.000
#> 1053 2 0.000
#> 1054 2 0.000
#> 1055 2 0.000
#> 1056 2 0.000
#> 1057 2 0.000
#> 1058 1 0.000
#> 1059 2 0.000
#> 1060 2 0.000
#> 1061 2 0.000
#> 1062 2 1.000
#> 1063 2 0.000
#> 1064 2 0.000
#> 1065 1 0.000
#> 1066 2 0.000
#> 1067 1 0.000
#> 1068 2 0.000
#> 1069 2 0.000
#> 1070 1 0.000
#> 1071 2 0.000
#> 1072 2 0.000
#> 1073 2 0.000
#> 1074 2 0.000
#> 1075 2 0.000
#> 1076 2 0.000
#> 1077 2 0.000
#> 1078 2 0.000
#> 1079 2 0.000
#> 1080 1 0.000
#> 1081 2 0.000
#> 1082 2 0.000
#> 1083 2 0.000
#> 1084 2 0.000
#> 1085 2 0.000
#> 1086 2 0.000
#> 1087 2 0.000
#> 1088 2 0.000
#> 1089 2 0.000
#> 1090 2 0.000
#> 1091 2 0.000
#> 1092 2 0.000
#> 1093 2 0.000
#> 1094 2 0.000
#> 1095 2 0.000
#> 1096 2 0.000
#> 1097 2 0.000
#> 1098 2 0.751
#> 1099 2 0.000
#> 1100 1 0.253
#> 1101 2 0.000
#> 1102 2 0.000
#> 1103 2 0.000
#> 1104 2 0.000
#> 1105 2 0.000
#> 1106 2 0.000
#> 1107 2 0.000
#> 1108 2 0.000
#> 1109 2 0.000
#> 1110 2 0.000
#> 1111 2 0.000
#> 1112 2 0.000
#> 1113 2 0.000
#> 1114 1 0.000
#> 1115 2 0.000
#> 1116 2 0.000
#> 1117 1 0.000
#> 1118 2 0.000
#> 1119 2 0.000
#> 1120 2 0.000
#> 1121 2 0.000
#> 1122 2 0.000
#> 1123 2 0.000
#> 1124 2 0.000
#> 1125 2 0.000
#> 1126 2 0.000
#> 1127 2 0.000
#> 1128 2 0.000
#> 1129 2 0.000
#> 1130 2 0.000
#> 1131 2 0.000
#> 1132 2 0.000
#> 1133 2 0.000
#> 1134 2 0.000
#> 1135 2 0.000
#> 1136 1 0.000
#> 1137 2 0.000
#> 1138 2 0.000
#> 1139 2 0.000
#> 1140 2 0.000
#> 1141 2 0.000
#> 1142 2 0.000
#> 1143 2 0.000
#> 1144 2 0.000
#> 1145 2 0.000
#> 1146 2 0.000
#> 1147 2 0.000
#> 1148 2 0.000
#> 1149 2 0.000
#> 1150 2 0.000
#> 1151 2 0.000
#> 1152 2 0.000
#> 1153 2 0.000
#> 1154 2 0.000
#> 1155 2 0.000
#> 1156 2 1.000
#> 1157 2 0.000
#> 1158 2 0.000
#> 1159 2 0.000
#> 1160 2 0.000
#> 1161 2 0.000
#> 1162 2 0.000
#> 1163 2 0.000
#> 1164 2 0.000
#> 1165 2 0.000
#> 1166 2 0.000
#> 1167 2 0.000
#> 1168 2 0.000
#> 1169 2 0.000
#> 1170 2 0.000
#> 1171 2 0.000
#> 1172 2 0.000
#> 1173 2 0.000
#> 1174 2 0.000
#> 1175 2 0.000
#> 1176 2 0.000
#> 1177 2 0.000
#> 1178 2 0.000
#> 1179 2 0.000
#> 1180 2 0.000
#> 1181 2 0.000
#> 1182 1 0.000
#> 1183 2 0.000
#> 1184 2 0.000
#> 1185 2 0.502
#> 1186 2 0.000
#> 1187 2 0.000
#> 1188 2 0.249
#> 1189 2 0.000
#> 1190 2 0.000
#> 1191 2 0.253
#> 1192 2 0.000
#> 1193 2 0.000
#> 1194 2 0.000
#> 1195 2 0.000
#> 1196 2 0.000
#> 1197 2 0.000
#> 1198 2 0.000
#> 1199 2 0.000
#> 1200 2 0.000
#> 1201 2 1.000
#> 1202 2 0.000
#> 1203 2 0.000
#> 1204 2 0.000
#> 1205 2 0.000
#> 1206 2 0.000
#> 1207 2 0.000
#> 1208 2 0.249
#> 1209 2 0.751
#> 1210 2 0.000
#> 1211 2 0.751
#> 1212 2 0.000
#> 1213 2 0.000
#> 1214 2 0.000
#> 1215 2 0.000
#> 1216 2 0.000
#> 1217 2 0.000
#> 1218 2 0.000
#> 1219 2 0.000
#> 1220 2 0.000
#> 1221 2 0.000
#> 1222 2 0.000
#> 1223 2 0.000
#> 1224 2 0.000
#> 1225 2 0.751
#> 1226 2 0.498
#> 1227 2 0.249
#> 1228 2 0.249
#> 1229 2 0.000
#> 1230 2 0.000
#> 1231 2 0.000
#> 1232 2 0.000
#> 1233 2 0.000
#> 1234 2 0.000
#> 1235 2 0.000
#> 1236 2 0.000
#> 1237 2 0.000
#> 1238 1 0.000
#> 1239 2 0.000
#> 1240 2 0.000
#> 1241 2 0.000
#> 1242 2 0.000
#> 1243 2 0.000
#> 1244 2 0.000
#> 1245 2 0.000
#> 1246 2 0.000
#> 1247 2 0.000
#> 1248 2 0.498
#> 1249 2 0.000
#> 1250 2 0.000
#> 1251 2 0.000
#> 1252 2 0.000
#> 1253 2 0.000
#> 1254 2 0.000
#> 1255 2 1.000
#> 1256 2 0.000
#> 1257 2 0.249
#> 1258 2 0.000
#> 1259 2 0.249
#> 1260 2 0.000
#> 1261 2 0.000
#> 1262 2 0.000
#> 1263 2 0.000
#> 1264 2 0.000
#> 1265 2 0.000
#> 1266 2 0.000
#> 1267 2 0.000
#> 1268 2 0.000
#> 1269 2 0.000
#> 1270 2 0.000
#> 1271 2 0.000
#> 1272 2 0.000
#> 1273 2 0.000
get_classes(res, k = 3)
#> class p
#> 1 2 0.502
#> 2 2 1.000
#> 3 2 0.000
#> 4 2 1.000
#> 5 1 1.000
#> 6 2 1.000
#> 7 2 1.000
#> 8 2 1.000
#> 9 1 1.000
#> 10 2 1.000
#> 11 2 1.000
#> 12 2 1.000
#> 13 2 1.000
#> 14 2 0.498
#> 15 1 0.000
#> 16 2 0.249
#> 17 1 0.498
#> 18 2 0.751
#> 19 2 0.751
#> 20 2 1.000
#> 21 2 1.000
#> 22 2 1.000
#> 23 2 1.000
#> 24 1 0.000
#> 25 1 1.000
#> 26 1 1.000
#> 27 2 1.000
#> 28 1 0.000
#> 29 1 0.000
#> 30 1 0.000
#> 31 2 1.000
#> 32 2 1.000
#> 33 1 0.000
#> 34 1 0.000
#> 35 1 0.000
#> 36 1 0.000
#> 37 1 0.000
#> 38 2 1.000
#> 39 2 1.000
#> 40 2 1.000
#> 41 1 0.000
#> 42 1 1.000
#> 43 1 0.000
#> 44 1 1.000
#> 45 1 0.000
#> 46 1 0.000
#> 47 1 0.000
#> 48 1 0.000
#> 49 1 0.000
#> 50 1 0.000
#> 51 1 0.000
#> 52 1 0.000
#> 53 1 0.000
#> 54 2 0.000
#> 55 1 0.000
#> 56 1 0.000
#> 57 1 0.000
#> 58 1 0.000
#> 59 1 0.000
#> 60 1 0.000
#> 61 1 0.000
#> 62 1 0.000
#> 63 1 0.000
#> 64 2 1.000
#> 65 1 0.000
#> 66 1 1.000
#> 67 2 1.000
#> 68 2 1.000
#> 69 1 0.000
#> 70 1 0.000
#> 71 1 0.000
#> 72 1 0.000
#> 73 1 0.000
#> 74 1 0.000
#> 75 1 0.000
#> 76 2 0.751
#> 77 1 0.000
#> 78 1 0.000
#> 79 1 0.000
#> 80 2 1.000
#> 81 1 1.000
#> 82 1 0.000
#> 83 1 0.000
#> 84 1 0.000
#> 85 1 0.000
#> 86 1 0.000
#> 87 1 0.000
#> 88 2 1.000
#> 89 2 1.000
#> 90 2 1.000
#> 91 2 0.498
#> 92 1 0.000
#> 93 1 0.000
#> 94 1 0.000
#> 95 1 0.000
#> 96 1 0.000
#> 97 1 0.000
#> 98 1 0.000
#> 99 1 0.000
#> 100 1 0.000
#> 101 1 0.000
#> 102 1 0.000
#> 103 1 0.000
#> 104 1 0.000
#> 105 1 0.000
#> 106 1 0.000
#> 107 1 0.000
#> 108 1 0.000
#> 109 1 0.000
#> 110 1 0.000
#> 111 1 0.000
#> 112 1 0.000
#> 113 1 0.000
#> 114 1 0.000
#> 115 1 0.000
#> 116 1 0.000
#> 117 1 0.000
#> 118 1 0.000
#> 119 1 0.000
#> 120 1 0.000
#> 121 1 0.000
#> 122 1 0.000
#> 123 1 0.000
#> 124 1 0.000
#> 125 1 0.000
#> 126 1 0.000
#> 127 1 0.000
#> 128 1 0.000
#> 129 1 0.000
#> 130 1 0.000
#> 131 1 0.000
#> 132 1 0.000
#> 133 1 0.000
#> 134 1 0.000
#> 135 1 0.000
#> 136 1 0.000
#> 137 1 0.000
#> 138 1 0.000
#> 139 1 0.000
#> 140 1 0.000
#> 141 1 0.000
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 1 0.000
#> 147 1 0.000
#> 148 1 0.000
#> 149 1 0.000
#> 150 1 0.000
#> 151 1 0.000
#> 152 1 0.000
#> 153 1 0.000
#> 154 1 0.000
#> 155 1 0.000
#> 156 1 0.000
#> 157 1 0.000
#> 158 1 0.000
#> 159 1 0.000
#> 160 1 0.000
#> 161 1 0.000
#> 162 1 0.000
#> 163 1 0.000
#> 164 1 0.000
#> 165 1 0.000
#> 166 1 0.000
#> 167 1 0.000
#> 168 1 0.000
#> 169 1 0.000
#> 170 1 0.000
#> 171 1 0.000
#> 172 1 0.000
#> 173 1 0.000
#> 174 1 0.000
#> 175 1 0.000
#> 176 1 0.000
#> 177 1 0.000
#> 178 1 0.000
#> 179 1 0.000
#> 180 1 0.000
#> 181 1 0.000
#> 182 1 0.000
#> 183 1 0.000
#> 184 1 0.000
#> 185 1 0.000
#> 186 1 0.000
#> 187 1 0.000
#> 188 1 0.000
#> 189 1 0.000
#> 190 1 0.000
#> 191 1 0.000
#> 192 1 0.000
#> 193 1 0.000
#> 194 1 0.000
#> 195 1 0.000
#> 196 1 0.000
#> 197 1 0.000
#> 198 1 0.000
#> 199 1 0.000
#> 200 1 0.000
#> 201 1 0.000
#> 202 1 0.000
#> 203 1 0.000
#> 204 1 0.000
#> 205 1 0.000
#> 206 1 0.000
#> 207 1 0.000
#> 208 1 0.000
#> 209 1 0.000
#> 210 1 0.000
#> 211 1 0.000
#> 212 1 0.000
#> 213 1 0.000
#> 214 1 0.000
#> 215 1 0.000
#> 216 1 0.000
#> 217 1 0.000
#> 218 1 0.000
#> 219 1 0.000
#> 220 1 0.000
#> 221 1 0.000
#> 222 1 0.000
#> 223 1 0.000
#> 224 1 0.000
#> 225 1 0.000
#> 226 1 0.000
#> 227 1 0.000
#> 228 1 0.000
#> 229 1 0.000
#> 230 1 0.000
#> 231 1 0.000
#> 232 1 0.000
#> 233 1 0.000
#> 234 1 0.000
#> 235 1 0.000
#> 236 1 0.000
#> 237 1 0.000
#> 238 1 0.000
#> 239 1 0.000
#> 240 1 0.000
#> 241 1 0.000
#> 242 1 0.000
#> 243 1 0.000
#> 244 1 0.000
#> 245 1 0.000
#> 246 1 0.000
#> 247 1 0.000
#> 248 1 0.000
#> 249 1 0.000
#> 250 1 0.000
#> 251 1 0.000
#> 252 1 0.000
#> 253 1 0.000
#> 254 1 0.000
#> 255 1 0.000
#> 256 1 0.000
#> 257 1 0.000
#> 258 1 0.000
#> 259 1 0.000
#> 260 1 0.000
#> 261 1 0.000
#> 262 1 0.000
#> 263 1 0.000
#> 264 1 0.000
#> 265 1 0.000
#> 266 1 0.000
#> 267 1 0.000
#> 268 1 0.000
#> 269 1 0.000
#> 270 1 0.000
#> 271 1 0.000
#> 272 1 0.000
#> 273 1 0.000
#> 274 1 0.000
#> 275 1 0.000
#> 276 1 0.000
#> 277 1 0.000
#> 278 1 0.000
#> 279 1 0.000
#> 280 1 0.000
#> 281 1 0.000
#> 282 1 0.000
#> 283 1 0.000
#> 284 1 0.000
#> 285 1 0.000
#> 286 1 0.000
#> 287 1 0.000
#> 288 1 0.000
#> 289 1 0.000
#> 290 1 0.000
#> 291 1 0.000
#> 292 1 0.000
#> 293 1 0.000
#> 294 1 0.000
#> 295 1 0.000
#> 296 1 0.000
#> 297 1 0.000
#> 298 1 0.000
#> 299 1 0.000
#> 300 1 0.000
#> 301 1 0.000
#> 302 1 0.000
#> 303 1 0.000
#> 304 1 0.000
#> 305 1 0.000
#> 306 1 0.000
#> 307 1 0.000
#> 308 1 0.000
#> 309 1 0.000
#> 310 1 0.000
#> 311 1 0.000
#> 312 1 0.000
#> 313 1 0.000
#> 314 1 0.000
#> 315 1 0.000
#> 316 1 0.000
#> 317 1 0.000
#> 318 1 0.000
#> 319 1 0.000
#> 320 1 0.000
#> 321 1 0.000
#> 322 1 0.000
#> 323 2 1.000
#> 324 1 0.000
#> 325 1 0.000
#> 326 1 0.000
#> 327 1 1.000
#> 328 1 0.000
#> 329 1 0.000
#> 330 1 0.249
#> 331 1 0.000
#> 332 1 0.000
#> 333 1 0.000
#> 334 1 0.000
#> 335 1 0.000
#> 336 1 0.000
#> 337 1 0.000
#> 338 1 0.000
#> 339 1 0.000
#> 340 1 0.751
#> 341 2 1.000
#> 342 1 0.000
#> 343 1 0.000
#> 344 1 0.000
#> 345 1 0.000
#> 346 1 0.000
#> 347 1 0.000
#> 348 1 0.000
#> 349 1 0.000
#> 350 2 1.000
#> 351 1 1.000
#> 352 1 0.000
#> 353 1 0.000
#> 354 1 0.000
#> 355 1 0.000
#> 356 2 1.000
#> 357 1 0.000
#> 358 1 0.000
#> 359 1 0.000
#> 360 1 0.000
#> 361 1 0.000
#> 362 1 0.000
#> 363 1 0.000
#> 364 1 0.000
#> 365 1 0.000
#> 366 1 0.000
#> 367 1 0.249
#> 368 1 0.000
#> 369 1 0.000
#> 370 1 0.000
#> 371 1 0.000
#> 372 1 0.000
#> 373 1 0.000
#> 374 1 0.000
#> 375 1 0.000
#> 376 1 0.000
#> 377 1 0.000
#> 378 1 0.000
#> 379 1 0.000
#> 380 1 1.000
#> 381 1 0.000
#> 382 1 0.000
#> 383 1 0.000
#> 384 1 0.000
#> 385 2 1.000
#> 386 1 1.000
#> 387 1 0.000
#> 388 1 0.000
#> 389 1 0.000
#> 390 1 0.000
#> 391 1 0.000
#> 392 1 0.751
#> 393 1 1.000
#> 394 1 1.000
#> 395 1 0.249
#> 396 1 1.000
#> 397 1 0.000
#> 398 1 0.000
#> 399 1 0.000
#> 400 1 0.000
#> 401 1 0.000
#> 402 1 0.000
#> 403 1 0.000
#> 404 1 0.000
#> 405 1 0.000
#> 406 1 0.000
#> 407 1 0.000
#> 408 1 0.000
#> 409 1 0.000
#> 410 1 0.000
#> 411 1 1.000
#> 412 2 1.000
#> 413 2 1.000
#> 414 1 0.000
#> 415 1 1.000
#> 416 1 0.000
#> 417 1 0.000
#> 418 1 0.000
#> 419 1 0.000
#> 420 1 0.000
#> 421 1 0.000
#> 422 1 0.000
#> 423 1 0.000
#> 424 1 0.000
#> 425 1 0.000
#> 426 1 0.000
#> 427 1 0.000
#> 428 1 0.000
#> 429 1 1.000
#> 430 1 0.000
#> 431 1 0.000
#> 432 1 0.000
#> 433 1 1.000
#> 434 1 0.000
#> 435 1 0.000
#> 436 1 0.000
#> 437 1 0.000
#> 438 1 0.000
#> 439 1 0.000
#> 440 1 0.000
#> 441 1 0.000
#> 442 1 0.000
#> 443 1 0.000
#> 444 1 0.000
#> 445 1 0.000
#> 446 1 0.000
#> 447 1 0.000
#> 448 1 0.000
#> 449 1 0.000
#> 450 1 0.000
#> 451 1 0.000
#> 452 1 0.000
#> 453 1 1.000
#> 454 1 1.000
#> 455 1 0.000
#> 456 2 1.000
#> 457 1 0.000
#> 458 1 1.000
#> 459 1 0.000
#> 460 1 0.000
#> 461 1 0.000
#> 462 1 0.000
#> 463 1 0.000
#> 464 1 0.000
#> 465 1 0.000
#> 466 1 0.000
#> 467 1 0.000
#> 468 1 0.000
#> 469 1 0.000
#> 470 1 0.000
#> 471 1 0.000
#> 472 3 0.000
#> 473 2 0.000
#> 474 2 0.000
#> 475 2 0.498
#> 476 2 0.000
#> 477 2 0.000
#> 478 2 0.000
#> 479 2 0.000
#> 480 2 0.000
#> 481 2 1.000
#> 482 2 0.000
#> 483 2 0.000
#> 484 2 0.000
#> 485 2 0.000
#> 486 2 0.000
#> 487 2 0.000
#> 488 2 0.000
#> 489 2 0.000
#> 490 2 0.000
#> 491 2 0.000
#> 492 2 0.000
#> 493 2 0.000
#> 494 3 1.000
#> 495 2 0.000
#> 496 2 0.000
#> 497 2 0.000
#> 498 2 0.000
#> 499 2 0.000
#> 500 2 0.000
#> 501 2 0.000
#> 502 2 0.000
#> 503 2 0.000
#> 504 2 0.000
#> 505 2 0.000
#> 506 1 0.000
#> 507 2 0.000
#> 508 2 0.000
#> 509 2 0.000
#> 510 2 0.000
#> 511 2 0.000
#> 512 2 0.000
#> 513 1 0.000
#> 514 2 0.000
#> 515 2 0.000
#> 516 2 0.000
#> 517 2 0.000
#> 518 2 0.000
#> 519 2 0.000
#> 520 2 0.000
#> 521 1 0.000
#> 522 2 0.000
#> 523 2 0.000
#> 524 2 0.000
#> 525 2 0.000
#> 526 2 0.000
#> 527 2 0.000
#> 528 2 0.000
#> 529 2 0.000
#> 530 2 0.000
#> 531 2 0.000
#> 532 2 0.000
#> 533 2 0.000
#> 534 2 0.000
#> 535 3 0.000
#> 536 2 0.000
#> 537 2 0.000
#> 538 2 0.000
#> 539 2 0.000
#> 540 3 0.000
#> 541 2 0.000
#> 542 2 0.000
#> 543 2 0.000
#> 544 2 0.000
#> 545 2 0.000
#> 546 2 0.000
#> 547 2 0.000
#> 548 2 0.000
#> 549 2 0.000
#> 550 2 0.000
#> 551 2 0.000
#> 552 1 0.000
#> 553 3 0.000
#> 554 2 0.000
#> 555 2 0.000
#> 556 2 0.000
#> 557 2 0.000
#> 558 2 0.000
#> 559 2 0.000
#> 560 3 0.000
#> 561 2 0.000
#> 562 2 0.000
#> 563 2 0.000
#> 564 2 0.000
#> 565 2 0.000
#> 566 2 0.000
#> 567 2 0.000
#> 568 2 0.000
#> 569 2 0.000
#> 570 2 0.000
#> 571 2 0.000
#> 572 2 0.000
#> 573 2 0.000
#> 574 3 0.000
#> 575 2 0.000
#> 576 2 0.000
#> 577 2 0.502
#> 578 2 0.000
#> 579 2 0.000
#> 580 2 0.000
#> 581 2 0.000
#> 582 2 0.000
#> 583 2 0.000
#> 584 2 0.000
#> 585 2 0.000
#> 586 2 0.000
#> 587 1 0.000
#> 588 1 0.000
#> 589 2 0.000
#> 590 2 0.000
#> 591 2 0.000
#> 592 2 0.000
#> 593 2 0.000
#> 594 2 0.000
#> 595 2 0.000
#> 596 2 0.000
#> 597 1 0.000
#> 598 2 1.000
#> 599 2 0.000
#> 600 2 1.000
#> 601 2 0.000
#> 602 2 1.000
#> 603 2 0.000
#> 604 2 0.000
#> 605 2 1.000
#> 606 2 0.502
#> 607 2 0.000
#> 608 2 0.000
#> 609 2 0.000
#> 610 2 0.000
#> 611 2 0.000
#> 612 2 0.000
#> 613 1 0.000
#> 614 2 1.000
#> 615 1 0.000
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.000
#> 619 2 0.000
#> 620 1 0.000
#> 621 1 0.000
#> 622 1 0.000
#> 623 2 0.000
#> 624 1 0.000
#> 625 2 0.000
#> 626 1 0.000
#> 627 1 0.000
#> 628 2 1.000
#> 629 2 0.000
#> 630 2 0.249
#> 631 2 0.000
#> 632 2 0.000
#> 633 2 1.000
#> 634 2 0.000
#> 635 1 1.000
#> 636 2 0.000
#> 637 2 0.000
#> 638 1 0.000
#> 639 2 0.000
#> 640 2 0.000
#> 641 2 0.000
#> 642 2 0.751
#> 643 2 0.000
#> 644 2 0.000
#> 645 2 1.000
#> 646 2 1.000
#> 647 2 1.000
#> 648 3 0.000
#> 649 2 0.000
#> 650 2 0.000
#> 651 2 1.000
#> 652 2 1.000
#> 653 2 0.000
#> 654 2 0.751
#> 655 2 0.000
#> 656 2 0.000
#> 657 1 0.000
#> 658 2 0.000
#> 659 2 0.000
#> 660 3 0.000
#> 661 2 0.000
#> 662 2 0.751
#> 663 2 1.000
#> 664 2 0.000
#> 665 2 0.000
#> 666 2 0.751
#> 667 1 0.000
#> 668 2 0.000
#> 669 2 0.000
#> 670 2 0.000
#> 671 2 0.000
#> 672 2 0.000
#> 673 2 0.000
#> 674 2 1.000
#> 675 2 0.000
#> 676 2 0.000
#> 677 2 0.000
#> 678 1 0.000
#> 679 2 0.000
#> 680 1 0.000
#> 681 1 0.000
#> 682 2 1.000
#> 683 2 0.502
#> 684 2 0.000
#> 685 2 0.249
#> 686 2 0.000
#> 687 1 0.000
#> 688 1 0.000
#> 689 2 0.000
#> 690 2 0.249
#> 691 1 0.000
#> 692 2 1.000
#> 693 1 0.000
#> 694 1 0.000
#> 695 1 0.000
#> 696 2 1.000
#> 697 2 1.000
#> 698 2 1.000
#> 699 2 0.000
#> 700 2 1.000
#> 701 2 1.000
#> 702 1 0.000
#> 703 1 0.000
#> 704 3 0.000
#> 705 3 0.000
#> 706 3 0.000
#> 707 3 0.000
#> 708 3 0.000
#> 709 3 0.000
#> 710 3 0.000
#> 711 3 0.000
#> 712 3 0.000
#> 713 3 0.000
#> 714 3 0.000
#> 715 3 0.000
#> 716 3 0.000
#> 717 3 0.000
#> 718 3 0.000
#> 719 3 0.000
#> 720 3 0.000
#> 721 3 0.000
#> 722 3 0.000
#> 723 3 0.000
#> 724 3 0.000
#> 725 3 0.000
#> 726 3 0.000
#> 727 3 0.000
#> 728 3 0.000
#> 729 3 0.000
#> 730 3 0.000
#> 731 3 0.000
#> 732 3 0.000
#> 733 3 0.000
#> 734 3 0.000
#> 735 3 0.000
#> 736 3 0.000
#> 737 3 0.000
#> 738 3 0.000
#> 739 3 0.000
#> 740 3 0.000
#> 741 3 0.000
#> 742 3 0.000
#> 743 3 0.000
#> 744 3 0.000
#> 745 2 1.000
#> 746 3 0.000
#> 747 3 0.000
#> 748 3 0.000
#> 749 3 0.000
#> 750 3 0.000
#> 751 3 0.000
#> 752 3 0.000
#> 753 3 0.000
#> 754 3 0.000
#> 755 3 0.000
#> 756 3 0.000
#> 757 3 0.000
#> 758 3 0.000
#> 759 3 0.000
#> 760 3 0.000
#> 761 3 0.000
#> 762 3 0.000
#> 763 3 0.000
#> 764 3 0.000
#> 765 3 0.000
#> 766 3 0.000
#> 767 3 0.000
#> 768 3 0.000
#> 769 3 0.000
#> 770 3 0.000
#> 771 3 0.000
#> 772 3 0.000
#> 773 3 0.000
#> 774 3 0.000
#> 775 3 0.000
#> 776 3 0.000
#> 777 3 0.000
#> 778 3 0.000
#> 779 3 0.000
#> 780 3 0.000
#> 781 3 0.000
#> 782 3 0.000
#> 783 3 0.000
#> 784 3 0.000
#> 785 3 0.000
#> 786 2 1.000
#> 787 3 1.000
#> 788 2 1.000
#> 789 2 1.000
#> 790 3 0.000
#> 791 3 1.000
#> 792 3 1.000
#> 793 3 1.000
#> 794 3 0.000
#> 795 3 0.000
#> 796 3 0.000
#> 797 3 0.000
#> 798 3 0.000
#> 799 3 0.000
#> 800 3 0.000
#> 801 3 0.000
#> 802 3 0.000
#> 803 3 0.000
#> 804 3 0.000
#> 805 3 0.000
#> 806 3 1.000
#> 807 3 0.000
#> 808 3 0.498
#> 809 3 0.000
#> 810 3 0.000
#> 811 3 0.000
#> 812 3 0.000
#> 813 3 0.000
#> 814 3 0.000
#> 815 3 0.249
#> 816 3 0.000
#> 817 1 0.000
#> 818 3 0.000
#> 819 2 1.000
#> 820 3 0.000
#> 821 3 0.000
#> 822 3 0.000
#> 823 3 0.000
#> 824 3 0.498
#> 825 3 0.000
#> 826 3 0.000
#> 827 3 0.000
#> 828 3 0.000
#> 829 3 0.000
#> 830 2 1.000
#> 831 3 1.000
#> 832 3 0.000
#> 833 3 0.000
#> 834 3 0.000
#> 835 3 0.000
#> 836 3 0.000
#> 837 3 0.000
#> 838 3 0.000
#> 839 3 0.000
#> 840 3 0.000
#> 841 3 0.000
#> 842 3 0.000
#> 843 3 0.000
#> 844 2 1.000
#> 845 3 0.000
#> 846 1 0.249
#> 847 1 0.000
#> 848 1 0.000
#> 849 3 0.000
#> 850 3 0.000
#> 851 3 1.000
#> 852 1 0.000
#> 853 3 0.000
#> 854 2 1.000
#> 855 3 0.000
#> 856 3 0.000
#> 857 1 0.000
#> 858 3 0.000
#> 859 3 0.000
#> 860 3 0.000
#> 861 3 0.000
#> 862 3 0.000
#> 863 3 0.253
#> 864 3 0.751
#> 865 3 0.000
#> 866 2 1.000
#> 867 2 1.000
#> 868 3 0.000
#> 869 1 0.000
#> 870 3 0.000
#> 871 2 1.000
#> 872 3 0.000
#> 873 3 0.000
#> 874 1 0.502
#> 875 3 1.000
#> 876 3 0.000
#> 877 3 0.000
#> 878 3 1.000
#> 879 3 0.000
#> 880 2 1.000
#> 881 3 0.000
#> 882 3 0.000
#> 883 3 1.000
#> 884 3 1.000
#> 885 3 1.000
#> 886 3 0.000
#> 887 3 0.751
#> 888 3 0.000
#> 889 3 0.000
#> 890 3 0.000
#> 891 3 0.000
#> 892 3 0.000
#> 893 3 0.000
#> 894 2 1.000
#> 895 1 0.000
#> 896 1 0.000
#> 897 3 0.000
#> 898 3 0.000
#> 899 2 1.000
#> 900 3 0.000
#> 901 3 0.000
#> 902 3 0.000
#> 903 3 1.000
#> 904 3 1.000
#> 905 3 0.000
#> 906 3 0.000
#> 907 3 0.000
#> 908 3 0.000
#> 909 3 0.000
#> 910 1 0.747
#> 911 3 0.000
#> 912 3 0.000
#> 913 3 0.000
#> 914 3 0.000
#> 915 3 1.000
#> 916 2 1.000
#> 917 3 1.000
#> 918 3 1.000
#> 919 1 0.000
#> 920 3 1.000
#> 921 3 0.249
#> 922 2 1.000
#> 923 3 0.000
#> 924 3 0.751
#> 925 3 0.000
#> 926 3 0.000
#> 927 3 0.000
#> 928 3 0.000
#> 929 3 0.000
#> 930 3 0.000
#> 931 3 0.000
#> 932 3 0.000
#> 933 1 0.000
#> 934 2 0.502
#> 935 3 0.000
#> 936 2 0.249
#> 937 3 0.000
#> 938 2 1.000
#> 939 2 0.502
#> 940 2 0.000
#> 941 2 1.000
#> 942 2 0.498
#> 943 2 1.000
#> 944 2 0.502
#> 945 2 1.000
#> 946 3 0.000
#> 947 2 1.000
#> 948 2 1.000
#> 949 2 0.502
#> 950 2 1.000
#> 951 2 0.000
#> 952 2 0.249
#> 953 2 0.000
#> 954 2 1.000
#> 955 2 0.498
#> 956 2 1.000
#> 957 2 0.000
#> 958 1 0.000
#> 959 2 1.000
#> 960 1 0.000
#> 961 1 0.000
#> 962 2 1.000
#> 963 2 0.751
#> 964 2 0.000
#> 965 2 0.502
#> 966 2 1.000
#> 967 2 0.751
#> 968 2 0.000
#> 969 2 0.253
#> 970 2 1.000
#> 971 2 0.249
#> 972 2 1.000
#> 973 2 0.751
#> 974 2 1.000
#> 975 2 1.000
#> 976 2 1.000
#> 977 2 0.000
#> 978 2 0.000
#> 979 1 0.000
#> 980 1 0.000
#> 981 2 0.502
#> 982 2 0.000
#> 983 2 0.000
#> 984 2 1.000
#> 985 2 0.000
#> 986 2 0.000
#> 987 1 0.000
#> 988 2 0.000
#> 989 1 1.000
#> 990 2 0.000
#> 991 2 0.747
#> 992 2 1.000
#> 993 2 0.000
#> 994 2 0.751
#> 995 2 0.751
#> 996 2 0.000
#> 997 1 0.000
#> 998 2 0.751
#> 999 2 0.249
#> 1000 1 0.000
#> 1001 2 0.000
#> 1002 2 0.253
#> 1003 2 1.000
#> 1004 2 0.498
#> 1005 2 0.000
#> 1006 2 1.000
#> 1007 2 0.000
#> 1008 3 0.000
#> 1009 2 0.000
#> 1010 2 0.000
#> 1011 2 0.000
#> 1012 3 0.000
#> 1013 2 1.000
#> 1014 2 0.498
#> 1015 3 0.249
#> 1016 2 0.000
#> 1017 2 1.000
#> 1018 2 1.000
#> 1019 2 0.000
#> 1020 2 0.498
#> 1021 2 0.000
#> 1022 2 1.000
#> 1023 2 0.249
#> 1024 2 0.000
#> 1025 2 1.000
#> 1026 1 0.000
#> 1027 2 1.000
#> 1028 1 0.000
#> 1029 1 0.000
#> 1030 1 0.498
#> 1031 2 0.000
#> 1032 2 0.249
#> 1033 3 0.000
#> 1034 2 1.000
#> 1035 3 0.000
#> 1036 2 0.000
#> 1037 2 0.249
#> 1038 2 0.000
#> 1039 2 0.000
#> 1040 2 0.000
#> 1041 2 0.000
#> 1042 2 0.000
#> 1043 1 0.747
#> 1044 2 0.000
#> 1045 3 1.000
#> 1046 2 0.000
#> 1047 1 0.000
#> 1048 2 0.000
#> 1049 1 0.000
#> 1050 1 0.000
#> 1051 3 0.498
#> 1052 2 1.000
#> 1053 2 0.498
#> 1054 2 0.253
#> 1055 2 1.000
#> 1056 2 0.000
#> 1057 2 1.000
#> 1058 1 0.000
#> 1059 2 0.000
#> 1060 2 0.747
#> 1061 2 1.000
#> 1062 2 1.000
#> 1063 2 0.751
#> 1064 2 0.000
#> 1065 1 0.000
#> 1066 2 0.249
#> 1067 1 0.000
#> 1068 2 0.000
#> 1069 2 0.000
#> 1070 1 0.000
#> 1071 2 0.502
#> 1072 2 0.253
#> 1073 2 0.751
#> 1074 2 1.000
#> 1075 2 0.000
#> 1076 2 0.000
#> 1077 2 0.751
#> 1078 2 0.000
#> 1079 2 0.000
#> 1080 1 0.000
#> 1081 2 0.000
#> 1082 2 1.000
#> 1083 2 0.000
#> 1084 2 1.000
#> 1085 2 1.000
#> 1086 2 0.253
#> 1087 2 1.000
#> 1088 2 0.000
#> 1089 2 0.000
#> 1090 2 0.000
#> 1091 2 1.000
#> 1092 2 0.000
#> 1093 2 0.000
#> 1094 2 0.502
#> 1095 2 0.249
#> 1096 2 0.000
#> 1097 2 0.000
#> 1098 2 1.000
#> 1099 2 0.000
#> 1100 1 0.000
#> 1101 2 0.000
#> 1102 2 0.502
#> 1103 2 0.249
#> 1104 2 0.751
#> 1105 2 0.249
#> 1106 2 1.000
#> 1107 2 0.000
#> 1108 2 0.498
#> 1109 2 0.498
#> 1110 2 0.000
#> 1111 2 0.000
#> 1112 2 0.000
#> 1113 2 1.000
#> 1114 1 0.000
#> 1115 2 0.249
#> 1116 2 0.000
#> 1117 1 0.000
#> 1118 2 0.249
#> 1119 2 0.000
#> 1120 2 0.249
#> 1121 2 0.000
#> 1122 2 0.000
#> 1123 2 0.751
#> 1124 2 0.249
#> 1125 2 0.000
#> 1126 2 0.000
#> 1127 2 0.498
#> 1128 2 0.000
#> 1129 2 0.000
#> 1130 2 0.000
#> 1131 2 0.000
#> 1132 2 0.000
#> 1133 2 0.000
#> 1134 2 0.000
#> 1135 2 0.000
#> 1136 1 0.000
#> 1137 2 0.249
#> 1138 2 0.000
#> 1139 2 0.000
#> 1140 2 0.000
#> 1141 2 0.751
#> 1142 2 0.502
#> 1143 3 0.000
#> 1144 2 1.000
#> 1145 2 0.000
#> 1146 2 1.000
#> 1147 2 0.000
#> 1148 2 0.000
#> 1149 2 0.000
#> 1150 2 0.000
#> 1151 2 0.000
#> 1152 2 0.000
#> 1153 2 0.000
#> 1154 2 0.000
#> 1155 2 0.000
#> 1156 1 1.000
#> 1157 2 0.249
#> 1158 2 0.000
#> 1159 2 0.000
#> 1160 2 0.000
#> 1161 2 0.000
#> 1162 2 0.000
#> 1163 2 0.000
#> 1164 2 0.000
#> 1165 2 0.000
#> 1166 2 0.000
#> 1167 2 0.000
#> 1168 2 0.000
#> 1169 2 0.000
#> 1170 2 0.000
#> 1171 2 0.502
#> 1172 2 0.249
#> 1173 2 0.000
#> 1174 2 0.253
#> 1175 2 0.000
#> 1176 2 1.000
#> 1177 3 0.498
#> 1178 2 1.000
#> 1179 3 1.000
#> 1180 3 0.000
#> 1181 2 1.000
#> 1182 1 0.502
#> 1183 2 1.000
#> 1184 2 1.000
#> 1185 3 0.249
#> 1186 2 1.000
#> 1187 3 1.000
#> 1188 3 1.000
#> 1189 2 1.000
#> 1190 2 1.000
#> 1191 3 1.000
#> 1192 2 1.000
#> 1193 2 1.000
#> 1194 3 1.000
#> 1195 2 1.000
#> 1196 2 1.000
#> 1197 2 1.000
#> 1198 3 0.000
#> 1199 2 1.000
#> 1200 2 1.000
#> 1201 2 1.000
#> 1202 2 1.000
#> 1203 3 1.000
#> 1204 2 1.000
#> 1205 2 1.000
#> 1206 2 1.000
#> 1207 2 1.000
#> 1208 2 1.000
#> 1209 3 0.000
#> 1210 2 1.000
#> 1211 2 1.000
#> 1212 2 1.000
#> 1213 2 1.000
#> 1214 2 1.000
#> 1215 2 1.000
#> 1216 2 1.000
#> 1217 3 1.000
#> 1218 2 1.000
#> 1219 2 1.000
#> 1220 2 1.000
#> 1221 3 1.000
#> 1222 2 1.000
#> 1223 2 1.000
#> 1224 3 1.000
#> 1225 3 0.249
#> 1226 3 1.000
#> 1227 2 1.000
#> 1228 2 1.000
#> 1229 2 1.000
#> 1230 2 0.000
#> 1231 2 1.000
#> 1232 2 1.000
#> 1233 2 1.000
#> 1234 2 1.000
#> 1235 2 1.000
#> 1236 2 1.000
#> 1237 2 0.000
#> 1238 1 0.000
#> 1239 2 1.000
#> 1240 2 1.000
#> 1241 2 1.000
#> 1242 2 1.000
#> 1243 2 1.000
#> 1244 2 1.000
#> 1245 2 1.000
#> 1246 2 1.000
#> 1247 2 1.000
#> 1248 2 1.000
#> 1249 2 1.000
#> 1250 2 1.000
#> 1251 2 1.000
#> 1252 3 1.000
#> 1253 2 1.000
#> 1254 2 1.000
#> 1255 2 1.000
#> 1256 2 1.000
#> 1257 2 1.000
#> 1258 2 1.000
#> 1259 2 1.000
#> 1260 3 1.000
#> 1261 2 1.000
#> 1262 2 1.000
#> 1263 2 1.000
#> 1264 3 0.000
#> 1265 3 1.000
#> 1266 2 1.000
#> 1267 3 1.000
#> 1268 2 1.000
#> 1269 3 0.000
#> 1270 3 1.000
#> 1271 2 1.000
#> 1272 2 1.000
#> 1273 2 1.000
get_classes(res, k = 4)
#> class p
#> 1 2 1.000
#> 2 2 1.000
#> 3 2 0.249
#> 4 2 0.000
#> 5 1 1.000
#> 6 2 1.000
#> 7 2 1.000
#> 8 2 1.000
#> 9 1 1.000
#> 10 2 1.000
#> 11 2 1.000
#> 12 2 1.000
#> 13 2 1.000
#> 14 2 0.249
#> 15 1 0.000
#> 16 2 0.498
#> 17 1 1.000
#> 18 2 1.000
#> 19 2 0.751
#> 20 2 1.000
#> 21 2 1.000
#> 22 2 1.000
#> 23 2 1.000
#> 24 1 1.000
#> 25 1 1.000
#> 26 2 1.000
#> 27 2 1.000
#> 28 1 1.000
#> 29 1 0.000
#> 30 1 0.000
#> 31 2 1.000
#> 32 2 1.000
#> 33 4 0.502
#> 34 1 0.000
#> 35 1 0.000
#> 36 1 0.253
#> 37 1 0.000
#> 38 2 0.000
#> 39 2 1.000
#> 40 2 1.000
#> 41 1 0.000
#> 42 1 1.000
#> 43 4 0.249
#> 44 1 1.000
#> 45 1 0.000
#> 46 1 0.000
#> 47 4 0.000
#> 48 1 0.000
#> 49 4 0.000
#> 50 1 0.000
#> 51 1 0.000
#> 52 1 0.249
#> 53 1 0.000
#> 54 2 1.000
#> 55 1 0.747
#> 56 1 0.502
#> 57 1 0.000
#> 58 1 0.000
#> 59 1 0.000
#> 60 1 0.000
#> 61 4 0.000
#> 62 1 0.000
#> 63 1 0.498
#> 64 1 1.000
#> 65 1 0.000
#> 66 1 1.000
#> 67 4 1.000
#> 68 4 0.000
#> 69 1 0.000
#> 70 1 0.000
#> 71 1 0.000
#> 72 1 0.000
#> 73 4 0.000
#> 74 1 0.000
#> 75 1 0.249
#> 76 2 1.000
#> 77 1 0.000
#> 78 1 0.000
#> 79 1 0.000
#> 80 2 1.000
#> 81 1 1.000
#> 82 1 0.000
#> 83 1 1.000
#> 84 1 0.000
#> 85 1 0.000
#> 86 1 0.000
#> 87 1 1.000
#> 88 1 1.000
#> 89 2 1.000
#> 90 2 1.000
#> 91 2 1.000
#> 92 1 0.000
#> 93 1 0.000
#> 94 1 0.000
#> 95 1 0.000
#> 96 1 0.000
#> 97 1 0.000
#> 98 1 0.000
#> 99 1 0.000
#> 100 1 0.000
#> 101 1 0.000
#> 102 1 0.000
#> 103 1 0.000
#> 104 1 0.000
#> 105 1 0.000
#> 106 1 0.000
#> 107 1 0.000
#> 108 1 0.000
#> 109 1 0.000
#> 110 1 0.000
#> 111 1 0.000
#> 112 1 0.000
#> 113 1 0.751
#> 114 1 0.000
#> 115 1 0.000
#> 116 1 0.000
#> 117 1 0.000
#> 118 1 0.000
#> 119 1 0.000
#> 120 1 0.000
#> 121 1 0.000
#> 122 1 0.000
#> 123 1 0.000
#> 124 1 0.000
#> 125 1 0.000
#> 126 1 0.000
#> 127 1 0.000
#> 128 1 0.000
#> 129 1 0.000
#> 130 1 0.000
#> 131 1 0.000
#> 132 1 0.000
#> 133 1 0.000
#> 134 1 0.000
#> 135 1 0.000
#> 136 1 0.000
#> 137 1 0.000
#> 138 1 0.000
#> 139 4 0.000
#> 140 1 0.000
#> 141 1 0.000
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 1 0.000
#> 147 1 0.000
#> 148 1 0.000
#> 149 1 0.000
#> 150 1 1.000
#> 151 1 0.000
#> 152 1 0.000
#> 153 1 0.000
#> 154 1 0.000
#> 155 1 0.000
#> 156 1 0.000
#> 157 1 0.000
#> 158 1 0.000
#> 159 1 0.000
#> 160 1 0.000
#> 161 1 0.000
#> 162 1 0.000
#> 163 1 0.000
#> 164 1 0.000
#> 165 1 0.000
#> 166 1 0.000
#> 167 1 0.000
#> 168 1 0.000
#> 169 1 0.000
#> 170 1 0.000
#> 171 1 0.000
#> 172 1 0.000
#> 173 1 0.000
#> 174 1 0.000
#> 175 1 0.000
#> 176 1 0.000
#> 177 1 0.000
#> 178 1 0.000
#> 179 1 0.000
#> 180 1 0.000
#> 181 1 0.000
#> 182 1 0.000
#> 183 1 0.000
#> 184 1 0.000
#> 185 1 0.000
#> 186 1 0.000
#> 187 1 0.000
#> 188 1 0.000
#> 189 1 0.000
#> 190 1 0.000
#> 191 1 0.000
#> 192 1 0.000
#> 193 1 0.000
#> 194 1 0.000
#> 195 1 0.000
#> 196 1 0.000
#> 197 1 0.000
#> 198 1 0.000
#> 199 4 1.000
#> 200 1 0.000
#> 201 4 0.751
#> 202 1 0.000
#> 203 1 0.000
#> 204 1 0.000
#> 205 1 0.000
#> 206 1 0.000
#> 207 1 0.000
#> 208 1 0.000
#> 209 1 0.000
#> 210 1 0.000
#> 211 1 0.000
#> 212 1 0.000
#> 213 1 0.000
#> 214 1 0.000
#> 215 1 0.000
#> 216 1 0.000
#> 217 1 0.000
#> 218 1 0.000
#> 219 1 0.000
#> 220 1 0.000
#> 221 1 0.000
#> 222 1 0.000
#> 223 1 0.000
#> 224 1 0.000
#> 225 1 0.000
#> 226 1 0.000
#> 227 1 0.000
#> 228 1 0.000
#> 229 1 0.000
#> 230 1 0.000
#> 231 1 0.000
#> 232 1 0.000
#> 233 1 0.000
#> 234 1 0.000
#> 235 1 0.000
#> 236 1 0.000
#> 237 1 0.000
#> 238 1 0.000
#> 239 1 0.000
#> 240 1 0.000
#> 241 1 0.000
#> 242 1 0.000
#> 243 1 0.000
#> 244 1 0.000
#> 245 1 0.000
#> 246 1 0.000
#> 247 1 0.000
#> 248 1 0.000
#> 249 1 0.000
#> 250 1 0.000
#> 251 1 0.000
#> 252 1 0.000
#> 253 1 0.000
#> 254 1 0.000
#> 255 1 0.000
#> 256 1 0.000
#> 257 1 0.000
#> 258 1 0.000
#> 259 1 0.000
#> 260 1 0.000
#> 261 1 0.000
#> 262 1 0.000
#> 263 1 0.000
#> 264 1 0.000
#> 265 4 1.000
#> 266 1 0.000
#> 267 1 0.000
#> 268 1 0.000
#> 269 1 0.498
#> 270 1 0.000
#> 271 1 0.000
#> 272 1 0.000
#> 273 1 0.000
#> 274 1 0.000
#> 275 1 0.000
#> 276 1 0.000
#> 277 1 0.000
#> 278 1 0.000
#> 279 1 0.000
#> 280 1 0.000
#> 281 1 0.000
#> 282 1 0.000
#> 283 1 0.000
#> 284 1 0.000
#> 285 1 0.000
#> 286 1 0.000
#> 287 1 0.000
#> 288 1 0.000
#> 289 1 0.000
#> 290 1 0.000
#> 291 1 0.000
#> 292 1 0.000
#> 293 1 0.000
#> 294 1 0.000
#> 295 1 0.000
#> 296 1 0.000
#> 297 1 0.000
#> 298 1 0.000
#> 299 1 0.000
#> 300 1 0.000
#> 301 1 0.000
#> 302 1 0.000
#> 303 1 0.000
#> 304 1 0.000
#> 305 1 0.000
#> 306 1 0.000
#> 307 1 0.000
#> 308 1 0.000
#> 309 1 0.000
#> 310 1 0.000
#> 311 1 0.000
#> 312 1 0.000
#> 313 1 0.000
#> 314 1 0.000
#> 315 1 0.000
#> 316 1 0.000
#> 317 1 0.000
#> 318 1 0.000
#> 319 1 0.000
#> 320 1 0.000
#> 321 1 0.000
#> 322 1 0.000
#> 323 2 1.000
#> 324 4 0.000
#> 325 1 0.000
#> 326 1 0.000
#> 327 1 1.000
#> 328 1 0.000
#> 329 1 0.000
#> 330 1 1.000
#> 331 1 0.000
#> 332 1 0.000
#> 333 1 0.000
#> 334 1 0.000
#> 335 1 0.000
#> 336 1 0.000
#> 337 1 0.000
#> 338 1 0.000
#> 339 1 0.000
#> 340 1 0.751
#> 341 2 1.000
#> 342 1 0.000
#> 343 1 0.000
#> 344 1 0.000
#> 345 1 0.000
#> 346 1 0.000
#> 347 1 0.000
#> 348 1 0.000
#> 349 1 0.000
#> 350 1 1.000
#> 351 1 1.000
#> 352 1 0.498
#> 353 1 0.000
#> 354 1 0.000
#> 355 1 0.000
#> 356 2 1.000
#> 357 1 1.000
#> 358 1 0.000
#> 359 4 0.000
#> 360 4 0.000
#> 361 4 0.000
#> 362 1 0.000
#> 363 1 0.000
#> 364 1 0.000
#> 365 1 0.000
#> 366 1 0.000
#> 367 1 1.000
#> 368 1 0.000
#> 369 1 0.000
#> 370 1 0.000
#> 371 1 0.000
#> 372 1 0.000
#> 373 1 0.000
#> 374 1 0.000
#> 375 1 0.000
#> 376 1 0.000
#> 377 4 0.000
#> 378 1 0.000
#> 379 1 0.000
#> 380 1 1.000
#> 381 1 0.000
#> 382 1 0.000
#> 383 1 0.000
#> 384 1 1.000
#> 385 2 1.000
#> 386 1 1.000
#> 387 1 0.000
#> 388 1 0.000
#> 389 1 0.000
#> 390 1 1.000
#> 391 1 0.000
#> 392 1 1.000
#> 393 1 1.000
#> 394 1 1.000
#> 395 1 0.502
#> 396 1 1.000
#> 397 1 0.000
#> 398 1 0.000
#> 399 1 0.000
#> 400 1 0.000
#> 401 1 0.000
#> 402 1 0.000
#> 403 1 0.000
#> 404 1 0.000
#> 405 1 0.000
#> 406 1 0.000
#> 407 1 0.000
#> 408 1 0.000
#> 409 1 0.000
#> 410 1 0.498
#> 411 1 1.000
#> 412 1 1.000
#> 413 1 1.000
#> 414 1 0.000
#> 415 1 1.000
#> 416 1 0.000
#> 417 1 0.000
#> 418 1 0.000
#> 419 1 1.000
#> 420 1 1.000
#> 421 1 0.000
#> 422 1 0.000
#> 423 4 1.000
#> 424 1 0.000
#> 425 1 0.000
#> 426 1 0.000
#> 427 1 0.000
#> 428 1 0.000
#> 429 1 1.000
#> 430 1 0.000
#> 431 4 0.000
#> 432 1 0.000
#> 433 1 1.000
#> 434 1 0.000
#> 435 1 0.000
#> 436 1 0.000
#> 437 1 0.000
#> 438 1 1.000
#> 439 1 0.000
#> 440 1 0.000
#> 441 1 0.000
#> 442 1 0.000
#> 443 1 0.000
#> 444 4 0.000
#> 445 4 0.000
#> 446 1 0.000
#> 447 1 0.000
#> 448 1 0.000
#> 449 1 0.000
#> 450 1 0.000
#> 451 1 0.000
#> 452 1 0.000
#> 453 1 1.000
#> 454 1 0.751
#> 455 1 0.000
#> 456 2 1.000
#> 457 1 0.000
#> 458 1 1.000
#> 459 1 0.000
#> 460 1 0.000
#> 461 1 0.000
#> 462 1 0.000
#> 463 1 0.000
#> 464 1 0.000
#> 465 1 0.000
#> 466 1 0.000
#> 467 1 0.000
#> 468 1 0.000
#> 469 1 0.000
#> 470 1 0.000
#> 471 1 0.000
#> 472 3 0.000
#> 473 2 0.000
#> 474 2 0.000
#> 475 2 0.000
#> 476 2 0.000
#> 477 2 0.000
#> 478 2 0.000
#> 479 2 0.000
#> 480 2 0.000
#> 481 2 0.000
#> 482 2 0.000
#> 483 2 0.000
#> 484 2 0.000
#> 485 2 0.000
#> 486 2 0.000
#> 487 2 0.000
#> 488 2 0.000
#> 489 2 0.000
#> 490 2 0.000
#> 491 2 0.000
#> 492 2 0.000
#> 493 2 0.000
#> 494 4 0.000
#> 495 2 0.000
#> 496 2 0.000
#> 497 2 0.000
#> 498 2 0.000
#> 499 2 0.000
#> 500 2 0.000
#> 501 2 0.000
#> 502 2 0.000
#> 503 2 0.000
#> 504 2 0.000
#> 505 2 0.000
#> 506 1 0.000
#> 507 2 0.000
#> 508 2 0.000
#> 509 2 0.000
#> 510 2 0.000
#> 511 2 0.000
#> 512 2 0.000
#> 513 1 0.000
#> 514 2 0.000
#> 515 2 0.000
#> 516 2 0.000
#> 517 2 0.000
#> 518 2 0.000
#> 519 2 0.000
#> 520 2 0.000
#> 521 1 0.000
#> 522 2 0.000
#> 523 2 0.000
#> 524 2 0.000
#> 525 2 0.000
#> 526 2 0.000
#> 527 2 0.000
#> 528 2 0.000
#> 529 2 0.000
#> 530 2 0.000
#> 531 2 0.000
#> 532 2 0.000
#> 533 2 0.000
#> 534 2 0.000
#> 535 3 0.000
#> 536 2 0.000
#> 537 2 0.000
#> 538 2 0.000
#> 539 2 0.000
#> 540 3 0.000
#> 541 2 0.000
#> 542 2 0.000
#> 543 2 0.000
#> 544 2 0.000
#> 545 2 0.000
#> 546 2 0.000
#> 547 2 0.000
#> 548 2 0.000
#> 549 2 0.000
#> 550 2 0.000
#> 551 2 0.000
#> 552 1 0.000
#> 553 3 0.000
#> 554 2 0.000
#> 555 2 0.000
#> 556 2 0.000
#> 557 2 0.000
#> 558 2 0.000
#> 559 2 0.000
#> 560 4 0.000
#> 561 2 0.000
#> 562 2 0.000
#> 563 2 0.000
#> 564 2 0.000
#> 565 2 0.000
#> 566 2 0.000
#> 567 2 0.000
#> 568 2 0.000
#> 569 2 0.000
#> 570 2 0.000
#> 571 2 0.000
#> 572 2 0.000
#> 573 2 0.000
#> 574 3 0.000
#> 575 2 0.000
#> 576 2 0.000
#> 577 4 0.000
#> 578 2 0.000
#> 579 2 0.000
#> 580 2 0.000
#> 581 2 0.000
#> 582 2 0.000
#> 583 2 0.000
#> 584 2 0.000
#> 585 2 0.000
#> 586 2 0.000
#> 587 1 0.498
#> 588 1 0.000
#> 589 2 0.000
#> 590 2 0.000
#> 591 2 0.000
#> 592 2 0.000
#> 593 2 0.000
#> 594 2 0.000
#> 595 2 0.000
#> 596 2 0.000
#> 597 1 0.000
#> 598 2 0.249
#> 599 2 0.000
#> 600 1 1.000
#> 601 2 0.000
#> 602 4 0.000
#> 603 2 0.000
#> 604 2 0.000
#> 605 2 1.000
#> 606 2 0.000
#> 607 2 0.000
#> 608 2 0.000
#> 609 2 0.000
#> 610 2 0.000
#> 611 2 0.000
#> 612 2 0.000
#> 613 1 0.000
#> 614 2 1.000
#> 615 1 0.000
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.000
#> 619 2 0.000
#> 620 1 0.000
#> 621 1 1.000
#> 622 1 0.000
#> 623 2 0.000
#> 624 1 0.000
#> 625 2 0.000
#> 626 1 0.000
#> 627 1 0.000
#> 628 2 1.000
#> 629 2 0.498
#> 630 2 0.498
#> 631 2 0.000
#> 632 2 0.000
#> 633 4 0.000
#> 634 2 0.000
#> 635 1 1.000
#> 636 2 0.000
#> 637 2 0.000
#> 638 1 0.000
#> 639 2 0.000
#> 640 2 0.000
#> 641 2 0.000
#> 642 2 0.000
#> 643 2 0.000
#> 644 2 0.000
#> 645 4 1.000
#> 646 2 1.000
#> 647 2 1.000
#> 648 3 0.000
#> 649 2 0.000
#> 650 2 0.000
#> 651 4 0.000
#> 652 2 1.000
#> 653 2 0.000
#> 654 2 0.498
#> 655 2 0.000
#> 656 2 0.000
#> 657 1 0.000
#> 658 2 0.000
#> 659 2 0.000
#> 660 3 0.000
#> 661 2 0.000
#> 662 2 0.751
#> 663 4 0.000
#> 664 2 0.000
#> 665 2 0.000
#> 666 2 1.000
#> 667 1 1.000
#> 668 2 0.000
#> 669 2 0.000
#> 670 2 0.000
#> 671 2 0.000
#> 672 2 0.000
#> 673 2 0.000
#> 674 4 0.249
#> 675 2 0.000
#> 676 2 0.000
#> 677 2 0.000
#> 678 1 0.000
#> 679 2 0.000
#> 680 1 0.000
#> 681 1 0.000
#> 682 4 0.000
#> 683 2 0.000
#> 684 2 0.000
#> 685 2 0.249
#> 686 2 0.000
#> 687 1 0.000
#> 688 1 0.000
#> 689 2 0.000
#> 690 2 0.000
#> 691 1 0.000
#> 692 2 0.000
#> 693 1 0.000
#> 694 1 0.000
#> 695 1 0.000
#> 696 2 0.000
#> 697 2 0.000
#> 698 2 0.253
#> 699 2 0.000
#> 700 2 0.000
#> 701 4 0.000
#> 702 1 0.751
#> 703 1 0.000
#> 704 3 0.000
#> 705 3 0.000
#> 706 3 0.000
#> 707 3 0.000
#> 708 3 0.000
#> 709 3 0.000
#> 710 3 0.000
#> 711 3 0.000
#> 712 3 0.000
#> 713 3 0.000
#> 714 3 0.000
#> 715 3 0.000
#> 716 3 0.000
#> 717 3 0.000
#> 718 3 0.000
#> 719 3 0.000
#> 720 3 0.000
#> 721 3 0.000
#> 722 3 0.000
#> 723 3 0.000
#> 724 3 0.000
#> 725 3 0.000
#> 726 3 0.000
#> 727 3 0.000
#> 728 3 0.000
#> 729 3 0.000
#> 730 3 0.000
#> 731 3 0.000
#> 732 3 0.000
#> 733 3 0.000
#> 734 3 0.000
#> 735 3 0.000
#> 736 3 0.000
#> 737 3 0.000
#> 738 3 0.000
#> 739 3 0.000
#> 740 3 0.000
#> 741 3 0.000
#> 742 3 0.000
#> 743 3 0.000
#> 744 3 0.000
#> 745 2 1.000
#> 746 3 0.000
#> 747 3 0.000
#> 748 3 0.000
#> 749 3 0.000
#> 750 3 0.000
#> 751 3 0.000
#> 752 3 0.000
#> 753 3 0.000
#> 754 3 0.000
#> 755 3 0.000
#> 756 3 0.000
#> 757 3 0.000
#> 758 3 0.000
#> 759 3 0.000
#> 760 3 0.000
#> 761 3 0.000
#> 762 3 0.000
#> 763 3 0.000
#> 764 3 0.000
#> 765 3 0.000
#> 766 3 0.000
#> 767 3 0.000
#> 768 3 0.000
#> 769 3 0.000
#> 770 3 0.000
#> 771 3 0.000
#> 772 3 0.000
#> 773 3 0.000
#> 774 3 0.000
#> 775 3 0.000
#> 776 3 0.000
#> 777 3 0.000
#> 778 3 0.000
#> 779 3 0.000
#> 780 3 0.000
#> 781 3 0.000
#> 782 3 0.000
#> 783 3 0.000
#> 784 3 0.000
#> 785 3 0.000
#> 786 2 0.000
#> 787 2 1.000
#> 788 2 0.000
#> 789 2 0.000
#> 790 3 0.000
#> 791 3 0.502
#> 792 4 0.249
#> 793 2 1.000
#> 794 3 0.000
#> 795 3 0.000
#> 796 3 0.000
#> 797 3 0.000
#> 798 3 0.000
#> 799 3 0.000
#> 800 3 0.000
#> 801 3 0.000
#> 802 3 0.000
#> 803 3 0.000
#> 804 3 0.000
#> 805 3 0.000
#> 806 3 1.000
#> 807 3 0.000
#> 808 3 0.000
#> 809 3 0.000
#> 810 3 0.000
#> 811 3 0.000
#> 812 3 0.000
#> 813 3 0.000
#> 814 3 0.000
#> 815 3 0.000
#> 816 3 0.000
#> 817 1 0.000
#> 818 3 0.000
#> 819 2 0.000
#> 820 3 0.000
#> 821 3 0.000
#> 822 3 0.000
#> 823 3 0.000
#> 824 3 0.000
#> 825 3 0.000
#> 826 3 0.000
#> 827 3 0.000
#> 828 3 0.000
#> 829 3 0.000
#> 830 2 1.000
#> 831 3 1.000
#> 832 3 0.000
#> 833 3 0.000
#> 834 3 0.000
#> 835 3 0.000
#> 836 3 0.000
#> 837 3 0.000
#> 838 3 0.000
#> 839 3 0.000
#> 840 3 0.000
#> 841 3 0.000
#> 842 3 0.000
#> 843 3 0.000
#> 844 2 1.000
#> 845 3 0.000
#> 846 1 0.253
#> 847 1 0.000
#> 848 1 0.000
#> 849 3 0.000
#> 850 3 0.000
#> 851 2 1.000
#> 852 1 0.000
#> 853 3 0.000
#> 854 2 0.000
#> 855 3 0.000
#> 856 3 0.000
#> 857 1 0.000
#> 858 3 0.000
#> 859 3 0.000
#> 860 3 0.000
#> 861 4 0.000
#> 862 3 0.000
#> 863 4 0.000
#> 864 3 0.000
#> 865 3 0.000
#> 866 1 1.000
#> 867 4 0.000
#> 868 3 0.000
#> 869 1 0.000
#> 870 3 0.000
#> 871 2 0.502
#> 872 4 0.000
#> 873 3 0.000
#> 874 1 0.000
#> 875 2 1.000
#> 876 3 0.000
#> 877 3 0.000
#> 878 2 1.000
#> 879 3 0.000
#> 880 2 0.000
#> 881 3 0.000
#> 882 3 0.000
#> 883 2 1.000
#> 884 3 0.000
#> 885 4 0.000
#> 886 4 0.000
#> 887 3 1.000
#> 888 3 0.000
#> 889 3 0.000
#> 890 3 0.000
#> 891 4 0.000
#> 892 3 0.000
#> 893 3 0.000
#> 894 2 0.000
#> 895 1 0.000
#> 896 1 0.000
#> 897 3 0.000
#> 898 3 0.000
#> 899 2 1.000
#> 900 3 0.000
#> 901 3 0.000
#> 902 3 0.000
#> 903 3 0.000
#> 904 3 1.000
#> 905 3 0.000
#> 906 3 0.000
#> 907 3 0.000
#> 908 3 0.000
#> 909 3 0.000
#> 910 1 1.000
#> 911 3 0.000
#> 912 3 0.000
#> 913 3 0.000
#> 914 4 0.249
#> 915 3 1.000
#> 916 2 0.000
#> 917 2 1.000
#> 918 4 0.253
#> 919 1 0.000
#> 920 3 0.000
#> 921 3 0.000
#> 922 2 1.000
#> 923 3 0.000
#> 924 4 0.000
#> 925 3 0.000
#> 926 3 0.000
#> 927 3 0.000
#> 928 3 0.000
#> 929 3 0.000
#> 930 3 0.000
#> 931 3 0.000
#> 932 3 0.000
#> 933 1 0.000
#> 934 2 0.000
#> 935 3 0.000
#> 936 2 0.000
#> 937 3 0.000
#> 938 4 1.000
#> 939 2 0.000
#> 940 2 0.000
#> 941 2 1.000
#> 942 2 0.000
#> 943 2 0.000
#> 944 2 0.000
#> 945 2 0.000
#> 946 3 0.000
#> 947 2 0.000
#> 948 4 0.000
#> 949 2 0.000
#> 950 4 1.000
#> 951 2 0.000
#> 952 2 0.000
#> 953 2 0.000
#> 954 4 0.000
#> 955 2 0.000
#> 956 2 0.498
#> 957 2 0.000
#> 958 1 0.000
#> 959 2 1.000
#> 960 1 0.249
#> 961 1 0.000
#> 962 2 0.000
#> 963 2 0.000
#> 964 2 0.000
#> 965 2 0.000
#> 966 2 0.000
#> 967 2 0.000
#> 968 2 0.000
#> 969 2 0.000
#> 970 2 0.000
#> 971 2 0.000
#> 972 2 0.000
#> 973 2 0.000
#> 974 2 1.000
#> 975 2 0.000
#> 976 2 0.000
#> 977 2 0.000
#> 978 2 0.000
#> 979 1 0.000
#> 980 1 0.000
#> 981 2 0.000
#> 982 2 0.000
#> 983 2 0.000
#> 984 4 0.000
#> 985 2 0.000
#> 986 2 0.000
#> 987 1 0.747
#> 988 2 0.000
#> 989 2 1.000
#> 990 2 0.000
#> 991 2 0.000
#> 992 2 0.000
#> 993 2 0.000
#> 994 2 0.000
#> 995 2 0.000
#> 996 2 0.000
#> 997 1 0.000
#> 998 2 0.000
#> 999 2 1.000
#> 1000 1 0.000
#> 1001 2 0.000
#> 1002 2 0.000
#> 1003 2 0.000
#> 1004 2 0.000
#> 1005 2 0.000
#> 1006 2 0.000
#> 1007 2 0.000
#> 1008 3 0.000
#> 1009 2 0.000
#> 1010 2 0.000
#> 1011 2 0.000
#> 1012 3 0.000
#> 1013 2 0.000
#> 1014 2 0.000
#> 1015 3 0.249
#> 1016 2 0.000
#> 1017 4 0.502
#> 1018 2 1.000
#> 1019 2 0.249
#> 1020 2 0.000
#> 1021 2 0.000
#> 1022 2 1.000
#> 1023 2 0.000
#> 1024 2 0.000
#> 1025 2 1.000
#> 1026 1 0.000
#> 1027 2 0.000
#> 1028 1 0.000
#> 1029 1 0.000
#> 1030 1 0.751
#> 1031 2 0.000
#> 1032 2 0.000
#> 1033 3 0.000
#> 1034 2 0.000
#> 1035 3 0.249
#> 1036 2 0.000
#> 1037 2 0.000
#> 1038 2 0.000
#> 1039 2 0.000
#> 1040 2 0.000
#> 1041 2 0.000
#> 1042 2 0.000
#> 1043 1 1.000
#> 1044 2 0.000
#> 1045 4 0.000
#> 1046 2 0.000
#> 1047 1 0.000
#> 1048 2 0.000
#> 1049 1 0.000
#> 1050 1 0.000
#> 1051 3 0.000
#> 1052 2 0.000
#> 1053 2 0.000
#> 1054 2 0.000
#> 1055 2 0.000
#> 1056 2 0.000
#> 1057 2 0.000
#> 1058 1 0.000
#> 1059 2 0.000
#> 1060 2 0.000
#> 1061 2 0.000
#> 1062 1 1.000
#> 1063 2 0.000
#> 1064 2 0.000
#> 1065 1 0.000
#> 1066 2 0.000
#> 1067 1 0.000
#> 1068 2 0.000
#> 1069 2 0.000
#> 1070 1 0.000
#> 1071 2 0.498
#> 1072 2 0.000
#> 1073 2 0.498
#> 1074 2 0.000
#> 1075 2 0.000
#> 1076 2 0.000
#> 1077 2 0.000
#> 1078 2 0.000
#> 1079 2 0.000
#> 1080 1 1.000
#> 1081 2 0.000
#> 1082 2 0.000
#> 1083 2 0.000
#> 1084 2 0.000
#> 1085 2 0.000
#> 1086 2 0.000
#> 1087 2 0.751
#> 1088 2 0.000
#> 1089 2 0.000
#> 1090 2 0.000
#> 1091 2 0.000
#> 1092 2 0.000
#> 1093 2 0.000
#> 1094 2 0.000
#> 1095 2 0.000
#> 1096 2 0.000
#> 1097 2 0.000
#> 1098 2 1.000
#> 1099 2 0.000
#> 1100 1 1.000
#> 1101 2 0.000
#> 1102 2 0.000
#> 1103 2 0.000
#> 1104 2 0.000
#> 1105 2 0.000
#> 1106 4 0.000
#> 1107 2 0.000
#> 1108 2 0.000
#> 1109 2 0.000
#> 1110 2 0.000
#> 1111 2 0.000
#> 1112 2 0.000
#> 1113 2 1.000
#> 1114 1 0.000
#> 1115 2 0.000
#> 1116 2 0.000
#> 1117 1 0.000
#> 1118 2 0.000
#> 1119 2 0.000
#> 1120 2 0.000
#> 1121 2 0.000
#> 1122 2 0.000
#> 1123 2 0.000
#> 1124 2 0.000
#> 1125 2 0.000
#> 1126 2 0.000
#> 1127 2 0.000
#> 1128 2 0.000
#> 1129 2 0.000
#> 1130 2 0.000
#> 1131 2 0.000
#> 1132 2 0.000
#> 1133 2 0.000
#> 1134 2 0.000
#> 1135 2 0.000
#> 1136 1 0.000
#> 1137 2 0.000
#> 1138 2 0.000
#> 1139 2 0.000
#> 1140 2 0.000
#> 1141 2 0.000
#> 1142 2 0.000
#> 1143 3 0.000
#> 1144 2 0.000
#> 1145 2 0.000
#> 1146 2 0.000
#> 1147 2 0.000
#> 1148 2 0.000
#> 1149 2 0.000
#> 1150 2 0.000
#> 1151 2 0.000
#> 1152 2 0.000
#> 1153 2 0.000
#> 1154 2 0.000
#> 1155 2 0.000
#> 1156 1 1.000
#> 1157 2 0.000
#> 1158 2 0.000
#> 1159 2 0.000
#> 1160 2 0.502
#> 1161 2 0.000
#> 1162 2 0.000
#> 1163 2 0.000
#> 1164 2 0.000
#> 1165 2 0.000
#> 1166 2 0.000
#> 1167 2 0.000
#> 1168 2 0.000
#> 1169 2 0.000
#> 1170 2 0.000
#> 1171 2 0.000
#> 1172 2 0.000
#> 1173 2 0.000
#> 1174 2 0.000
#> 1175 2 0.000
#> 1176 2 0.000
#> 1177 4 0.000
#> 1178 4 0.000
#> 1179 4 0.000
#> 1180 4 0.000
#> 1181 4 0.000
#> 1182 4 0.000
#> 1183 4 0.000
#> 1184 4 0.000
#> 1185 4 0.000
#> 1186 4 0.000
#> 1187 4 0.000
#> 1188 4 0.000
#> 1189 4 1.000
#> 1190 4 0.000
#> 1191 4 0.000
#> 1192 4 0.000
#> 1193 4 0.000
#> 1194 4 0.000
#> 1195 4 0.000
#> 1196 4 0.000
#> 1197 4 0.000
#> 1198 4 0.000
#> 1199 4 0.000
#> 1200 4 0.000
#> 1201 2 1.000
#> 1202 4 0.000
#> 1203 4 0.000
#> 1204 4 0.000
#> 1205 4 0.000
#> 1206 4 1.000
#> 1207 4 1.000
#> 1208 4 1.000
#> 1209 4 0.000
#> 1210 2 1.000
#> 1211 4 0.000
#> 1212 4 1.000
#> 1213 4 1.000
#> 1214 4 0.000
#> 1215 2 1.000
#> 1216 4 1.000
#> 1217 4 0.000
#> 1218 4 0.000
#> 1219 4 0.000
#> 1220 4 0.000
#> 1221 4 0.000
#> 1222 4 0.000
#> 1223 4 0.000
#> 1224 4 0.000
#> 1225 4 0.000
#> 1226 4 0.000
#> 1227 4 0.000
#> 1228 4 0.000
#> 1229 4 0.000
#> 1230 2 0.000
#> 1231 4 1.000
#> 1232 2 1.000
#> 1233 4 0.000
#> 1234 2 1.000
#> 1235 4 1.000
#> 1236 2 1.000
#> 1237 2 0.000
#> 1238 4 0.000
#> 1239 4 1.000
#> 1240 4 0.000
#> 1241 4 0.000
#> 1242 4 0.000
#> 1243 4 0.000
#> 1244 4 0.000
#> 1245 4 0.000
#> 1246 4 0.000
#> 1247 4 0.000
#> 1248 4 0.249
#> 1249 4 0.000
#> 1250 4 0.000
#> 1251 2 1.000
#> 1252 4 0.000
#> 1253 4 1.000
#> 1254 4 0.253
#> 1255 4 0.253
#> 1256 4 0.751
#> 1257 4 0.000
#> 1258 4 1.000
#> 1259 4 0.000
#> 1260 4 0.000
#> 1261 4 0.000
#> 1262 4 1.000
#> 1263 4 0.000
#> 1264 4 0.000
#> 1265 4 0.000
#> 1266 4 0.000
#> 1267 4 0.000
#> 1268 4 0.000
#> 1269 4 0.000
#> 1270 4 0.000
#> 1271 4 0.000
#> 1272 4 0.000
#> 1273 4 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 1164 3.94e-196 2
#> ATC:skmeans 925 2.01e-298 3
#> ATC:skmeans 1078 0.00e+00 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["011"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9529 rows and 485 columns.
#> Top rows (953) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.964 0.985 0.5009 0.500 0.500
#> 3 3 0.973 0.956 0.981 0.3054 0.766 0.566
#> 4 4 0.865 0.891 0.946 0.0861 0.911 0.755
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.634 0.80737 0.16 0.84
#> 2 2 0.000 0.98375 0.00 1.00
#> 3 2 0.000 0.98375 0.00 1.00
#> 4 2 0.000 0.98375 0.00 1.00
#> 5 2 0.242 0.94722 0.04 0.96
#> 6 2 0.141 0.96631 0.02 0.98
#> 7 2 0.141 0.96631 0.02 0.98
#> 8 2 0.000 0.98375 0.00 1.00
#> 9 2 0.000 0.98375 0.00 1.00
#> 10 2 0.000 0.98375 0.00 1.00
#> 11 1 0.000 0.98629 1.00 0.00
#> 12 2 0.000 0.98375 0.00 1.00
#> 13 2 0.000 0.98375 0.00 1.00
#> 14 2 0.000 0.98375 0.00 1.00
#> 15 1 0.000 0.98629 1.00 0.00
#> 16 1 0.000 0.98629 1.00 0.00
#> 17 2 0.000 0.98375 0.00 1.00
#> 18 2 0.000 0.98375 0.00 1.00
#> 19 2 0.000 0.98375 0.00 1.00
#> 20 1 0.000 0.98629 1.00 0.00
#> 21 2 0.000 0.98375 0.00 1.00
#> 22 2 0.795 0.68663 0.24 0.76
#> 23 2 0.000 0.98375 0.00 1.00
#> 24 1 0.000 0.98629 1.00 0.00
#> 25 2 0.141 0.96631 0.02 0.98
#> 26 2 0.000 0.98375 0.00 1.00
#> 27 2 0.000 0.98375 0.00 1.00
#> 28 2 0.000 0.98375 0.00 1.00
#> 29 2 0.000 0.98375 0.00 1.00
#> 30 2 0.000 0.98375 0.00 1.00
#> 31 1 0.000 0.98629 1.00 0.00
#> 32 1 0.000 0.98629 1.00 0.00
#> 33 1 0.000 0.98629 1.00 0.00
#> 34 1 0.000 0.98629 1.00 0.00
#> 35 1 0.141 0.96786 0.98 0.02
#> 36 2 0.000 0.98375 0.00 1.00
#> 37 2 0.000 0.98375 0.00 1.00
#> 38 2 0.000 0.98375 0.00 1.00
#> 39 1 0.000 0.98629 1.00 0.00
#> 40 2 0.529 0.85851 0.12 0.88
#> 41 1 0.000 0.98629 1.00 0.00
#> 42 1 0.469 0.88335 0.90 0.10
#> 43 2 0.995 0.15670 0.46 0.54
#> 44 1 0.000 0.98629 1.00 0.00
#> 45 2 0.000 0.98375 0.00 1.00
#> 46 1 0.000 0.98629 1.00 0.00
#> 47 2 0.141 0.96631 0.02 0.98
#> 48 2 0.000 0.98375 0.00 1.00
#> 49 1 0.000 0.98629 1.00 0.00
#> 50 1 0.000 0.98629 1.00 0.00
#> 51 1 0.000 0.98629 1.00 0.00
#> 52 2 0.000 0.98375 0.00 1.00
#> 53 2 0.000 0.98375 0.00 1.00
#> 54 2 0.000 0.98375 0.00 1.00
#> 55 2 0.000 0.98375 0.00 1.00
#> 56 2 0.000 0.98375 0.00 1.00
#> 57 2 0.000 0.98375 0.00 1.00
#> 58 2 0.000 0.98375 0.00 1.00
#> 59 2 0.000 0.98375 0.00 1.00
#> 60 2 0.000 0.98375 0.00 1.00
#> 61 2 0.000 0.98375 0.00 1.00
#> 62 2 0.000 0.98375 0.00 1.00
#> 63 2 0.000 0.98375 0.00 1.00
#> 64 2 0.000 0.98375 0.00 1.00
#> 65 2 0.000 0.98375 0.00 1.00
#> 66 2 0.000 0.98375 0.00 1.00
#> 67 2 0.000 0.98375 0.00 1.00
#> 68 2 0.000 0.98375 0.00 1.00
#> 69 2 0.000 0.98375 0.00 1.00
#> 70 2 0.000 0.98375 0.00 1.00
#> 71 2 0.000 0.98375 0.00 1.00
#> 72 2 0.000 0.98375 0.00 1.00
#> 73 2 0.000 0.98375 0.00 1.00
#> 74 2 0.000 0.98375 0.00 1.00
#> 75 2 0.000 0.98375 0.00 1.00
#> 76 2 0.000 0.98375 0.00 1.00
#> 77 2 0.000 0.98375 0.00 1.00
#> 78 2 0.000 0.98375 0.00 1.00
#> 79 1 0.242 0.94849 0.96 0.04
#> 80 2 0.000 0.98375 0.00 1.00
#> 81 2 0.000 0.98375 0.00 1.00
#> 82 2 0.000 0.98375 0.00 1.00
#> 83 2 0.000 0.98375 0.00 1.00
#> 84 2 0.000 0.98375 0.00 1.00
#> 85 2 0.000 0.98375 0.00 1.00
#> 86 2 0.000 0.98375 0.00 1.00
#> 87 2 0.000 0.98375 0.00 1.00
#> 88 2 0.000 0.98375 0.00 1.00
#> 89 1 0.000 0.98629 1.00 0.00
#> 90 2 0.000 0.98375 0.00 1.00
#> 91 2 0.000 0.98375 0.00 1.00
#> 92 1 0.000 0.98629 1.00 0.00
#> 93 1 0.000 0.98629 1.00 0.00
#> 94 2 0.000 0.98375 0.00 1.00
#> 95 2 0.000 0.98375 0.00 1.00
#> 96 1 0.760 0.72156 0.78 0.22
#> 97 1 0.000 0.98629 1.00 0.00
#> 98 2 0.000 0.98375 0.00 1.00
#> 99 2 0.000 0.98375 0.00 1.00
#> 100 1 0.000 0.98629 1.00 0.00
#> 101 2 0.000 0.98375 0.00 1.00
#> 102 2 0.000 0.98375 0.00 1.00
#> 103 2 0.000 0.98375 0.00 1.00
#> 104 2 0.000 0.98375 0.00 1.00
#> 105 2 0.000 0.98375 0.00 1.00
#> 106 1 0.000 0.98629 1.00 0.00
#> 107 1 0.000 0.98629 1.00 0.00
#> 108 1 0.000 0.98629 1.00 0.00
#> 109 1 0.000 0.98629 1.00 0.00
#> 110 1 0.000 0.98629 1.00 0.00
#> 111 2 0.904 0.53062 0.32 0.68
#> 112 2 0.000 0.98375 0.00 1.00
#> 113 1 0.242 0.94851 0.96 0.04
#> 114 2 0.000 0.98375 0.00 1.00
#> 115 2 0.141 0.96629 0.02 0.98
#> 116 2 0.795 0.68413 0.24 0.76
#> 117 2 0.000 0.98375 0.00 1.00
#> 118 2 0.000 0.98375 0.00 1.00
#> 119 1 0.000 0.98629 1.00 0.00
#> 120 2 0.000 0.98375 0.00 1.00
#> 121 2 0.000 0.98375 0.00 1.00
#> 122 2 0.141 0.96629 0.02 0.98
#> 123 2 0.000 0.98375 0.00 1.00
#> 124 2 0.000 0.98375 0.00 1.00
#> 125 2 0.000 0.98375 0.00 1.00
#> 126 2 0.000 0.98375 0.00 1.00
#> 127 2 0.000 0.98375 0.00 1.00
#> 128 2 0.000 0.98375 0.00 1.00
#> 129 2 0.000 0.98375 0.00 1.00
#> 130 2 0.000 0.98375 0.00 1.00
#> 131 2 0.000 0.98375 0.00 1.00
#> 132 2 0.000 0.98375 0.00 1.00
#> 133 2 0.000 0.98375 0.00 1.00
#> 134 2 0.000 0.98375 0.00 1.00
#> 135 2 0.000 0.98375 0.00 1.00
#> 136 2 0.000 0.98375 0.00 1.00
#> 137 2 0.000 0.98375 0.00 1.00
#> 138 2 0.000 0.98375 0.00 1.00
#> 139 2 0.000 0.98375 0.00 1.00
#> 140 2 0.000 0.98375 0.00 1.00
#> 141 1 0.760 0.72189 0.78 0.22
#> 142 1 0.000 0.98629 1.00 0.00
#> 143 1 0.000 0.98629 1.00 0.00
#> 144 1 0.000 0.98629 1.00 0.00
#> 145 1 0.000 0.98629 1.00 0.00
#> 146 2 0.000 0.98375 0.00 1.00
#> 147 1 0.000 0.98629 1.00 0.00
#> 148 1 0.000 0.98629 1.00 0.00
#> 149 1 0.000 0.98629 1.00 0.00
#> 150 1 0.000 0.98629 1.00 0.00
#> 151 2 0.000 0.98375 0.00 1.00
#> 152 1 0.000 0.98629 1.00 0.00
#> 153 1 0.000 0.98629 1.00 0.00
#> 154 2 0.000 0.98375 0.00 1.00
#> 155 1 0.141 0.96789 0.98 0.02
#> 156 1 0.000 0.98629 1.00 0.00
#> 157 1 0.000 0.98629 1.00 0.00
#> 158 2 0.000 0.98375 0.00 1.00
#> 159 2 0.000 0.98375 0.00 1.00
#> 160 2 0.000 0.98375 0.00 1.00
#> 161 2 0.000 0.98375 0.00 1.00
#> 162 1 0.000 0.98629 1.00 0.00
#> 163 2 0.000 0.98375 0.00 1.00
#> 164 2 0.000 0.98375 0.00 1.00
#> 165 2 0.000 0.98375 0.00 1.00
#> 166 1 0.000 0.98629 1.00 0.00
#> 167 2 0.000 0.98375 0.00 1.00
#> 168 2 0.000 0.98375 0.00 1.00
#> 169 2 0.000 0.98375 0.00 1.00
#> 170 2 0.000 0.98375 0.00 1.00
#> 171 1 0.000 0.98629 1.00 0.00
#> 172 2 0.000 0.98375 0.00 1.00
#> 173 2 0.242 0.94704 0.04 0.96
#> 174 2 0.000 0.98375 0.00 1.00
#> 175 1 0.000 0.98629 1.00 0.00
#> 176 2 0.000 0.98375 0.00 1.00
#> 177 1 0.000 0.98629 1.00 0.00
#> 178 2 0.000 0.98375 0.00 1.00
#> 179 2 0.000 0.98375 0.00 1.00
#> 180 1 0.000 0.98629 1.00 0.00
#> 181 1 0.000 0.98629 1.00 0.00
#> 182 2 0.000 0.98375 0.00 1.00
#> 183 1 0.000 0.98629 1.00 0.00
#> 184 1 0.000 0.98629 1.00 0.00
#> 185 2 0.242 0.94736 0.04 0.96
#> 186 1 0.000 0.98629 1.00 0.00
#> 187 1 0.000 0.98629 1.00 0.00
#> 188 1 0.000 0.98629 1.00 0.00
#> 189 1 0.000 0.98629 1.00 0.00
#> 190 1 0.000 0.98629 1.00 0.00
#> 191 2 0.000 0.98375 0.00 1.00
#> 192 1 0.000 0.98629 1.00 0.00
#> 193 1 0.000 0.98629 1.00 0.00
#> 194 2 0.000 0.98375 0.00 1.00
#> 195 2 0.000 0.98375 0.00 1.00
#> 196 2 0.000 0.98375 0.00 1.00
#> 197 2 0.000 0.98375 0.00 1.00
#> 198 2 0.000 0.98375 0.00 1.00
#> 199 2 0.000 0.98375 0.00 1.00
#> 200 2 0.000 0.98375 0.00 1.00
#> 201 2 0.000 0.98375 0.00 1.00
#> 202 2 0.000 0.98375 0.00 1.00
#> 203 2 0.000 0.98375 0.00 1.00
#> 204 2 0.000 0.98375 0.00 1.00
#> 205 2 0.000 0.98375 0.00 1.00
#> 206 2 0.000 0.98375 0.00 1.00
#> 207 2 0.000 0.98375 0.00 1.00
#> 208 2 0.141 0.96624 0.02 0.98
#> 209 2 0.000 0.98375 0.00 1.00
#> 210 2 0.000 0.98375 0.00 1.00
#> 211 2 0.000 0.98375 0.00 1.00
#> 212 2 0.000 0.98375 0.00 1.00
#> 213 2 0.000 0.98375 0.00 1.00
#> 214 2 0.000 0.98375 0.00 1.00
#> 215 2 0.000 0.98375 0.00 1.00
#> 216 2 0.000 0.98375 0.00 1.00
#> 217 2 0.529 0.85746 0.12 0.88
#> 218 2 0.000 0.98375 0.00 1.00
#> 219 2 0.000 0.98375 0.00 1.00
#> 220 2 0.000 0.98375 0.00 1.00
#> 221 2 0.000 0.98375 0.00 1.00
#> 222 2 0.943 0.43780 0.36 0.64
#> 223 1 0.000 0.98629 1.00 0.00
#> 224 1 0.469 0.88450 0.90 0.10
#> 225 2 0.000 0.98375 0.00 1.00
#> 226 1 0.000 0.98629 1.00 0.00
#> 227 1 0.981 0.27922 0.58 0.42
#> 228 1 0.000 0.98629 1.00 0.00
#> 229 2 0.584 0.83185 0.14 0.86
#> 230 1 0.000 0.98629 1.00 0.00
#> 231 1 0.000 0.98629 1.00 0.00
#> 232 1 0.943 0.44394 0.64 0.36
#> 233 2 1.000 -0.00754 0.50 0.50
#> 234 2 0.995 0.14504 0.46 0.54
#> 235 1 0.000 0.98629 1.00 0.00
#> 236 1 0.000 0.98629 1.00 0.00
#> 237 2 0.000 0.98375 0.00 1.00
#> 238 1 0.000 0.98629 1.00 0.00
#> 239 1 0.000 0.98629 1.00 0.00
#> 240 1 0.000 0.98629 1.00 0.00
#> 241 1 0.000 0.98629 1.00 0.00
#> 242 2 0.000 0.98375 0.00 1.00
#> 243 2 0.000 0.98375 0.00 1.00
#> 244 1 0.943 0.44408 0.64 0.36
#> 245 1 0.634 0.80967 0.84 0.16
#> 246 1 0.760 0.72125 0.78 0.22
#> 247 2 0.000 0.98375 0.00 1.00
#> 248 1 0.958 0.39246 0.62 0.38
#> 249 1 0.469 0.88499 0.90 0.10
#> 250 2 0.000 0.98375 0.00 1.00
#> 251 2 0.000 0.98375 0.00 1.00
#> 252 2 0.000 0.98375 0.00 1.00
#> 253 2 0.141 0.96625 0.02 0.98
#> 254 2 0.000 0.98375 0.00 1.00
#> 255 1 0.000 0.98629 1.00 0.00
#> 256 1 0.000 0.98629 1.00 0.00
#> 257 1 0.000 0.98629 1.00 0.00
#> 258 2 0.000 0.98375 0.00 1.00
#> 259 2 0.000 0.98375 0.00 1.00
#> 260 2 0.000 0.98375 0.00 1.00
#> 261 1 0.000 0.98629 1.00 0.00
#> 262 2 0.000 0.98375 0.00 1.00
#> 263 2 0.000 0.98375 0.00 1.00
#> 264 1 0.402 0.90759 0.92 0.08
#> 265 2 0.000 0.98375 0.00 1.00
#> 266 1 0.000 0.98629 1.00 0.00
#> 267 2 0.000 0.98375 0.00 1.00
#> 268 1 0.000 0.98629 1.00 0.00
#> 269 1 0.000 0.98629 1.00 0.00
#> 270 2 0.000 0.98375 0.00 1.00
#> 271 2 0.000 0.98375 0.00 1.00
#> 272 2 0.000 0.98375 0.00 1.00
#> 273 1 0.000 0.98629 1.00 0.00
#> 274 1 0.000 0.98629 1.00 0.00
#> 275 2 0.000 0.98375 0.00 1.00
#> 276 2 0.000 0.98375 0.00 1.00
#> 277 2 0.000 0.98375 0.00 1.00
#> 278 2 0.000 0.98375 0.00 1.00
#> 279 1 0.000 0.98629 1.00 0.00
#> 280 2 0.999 0.07381 0.48 0.52
#> 281 1 0.000 0.98629 1.00 0.00
#> 282 1 0.000 0.98629 1.00 0.00
#> 283 1 0.000 0.98629 1.00 0.00
#> 284 1 0.000 0.98629 1.00 0.00
#> 285 1 0.000 0.98629 1.00 0.00
#> 286 1 0.000 0.98629 1.00 0.00
#> 287 1 0.000 0.98629 1.00 0.00
#> 288 1 0.000 0.98629 1.00 0.00
#> 289 1 0.000 0.98629 1.00 0.00
#> 290 1 0.000 0.98629 1.00 0.00
#> 291 1 0.242 0.94888 0.96 0.04
#> 292 1 0.000 0.98629 1.00 0.00
#> 293 1 0.000 0.98629 1.00 0.00
#> 294 1 0.000 0.98629 1.00 0.00
#> 295 1 0.000 0.98629 1.00 0.00
#> 296 1 0.000 0.98629 1.00 0.00
#> 297 1 0.000 0.98629 1.00 0.00
#> 298 1 0.000 0.98629 1.00 0.00
#> 299 1 0.000 0.98629 1.00 0.00
#> 300 1 0.000 0.98629 1.00 0.00
#> 301 1 0.000 0.98629 1.00 0.00
#> 302 1 0.000 0.98629 1.00 0.00
#> 303 1 0.000 0.98629 1.00 0.00
#> 304 1 0.000 0.98629 1.00 0.00
#> 305 1 0.000 0.98629 1.00 0.00
#> 306 1 0.000 0.98629 1.00 0.00
#> 307 1 0.000 0.98629 1.00 0.00
#> 308 2 0.000 0.98375 0.00 1.00
#> 309 2 0.000 0.98375 0.00 1.00
#> 310 1 0.000 0.98629 1.00 0.00
#> 311 1 0.000 0.98629 1.00 0.00
#> 312 1 0.000 0.98629 1.00 0.00
#> 313 1 0.000 0.98629 1.00 0.00
#> 314 1 0.000 0.98629 1.00 0.00
#> 315 1 0.000 0.98629 1.00 0.00
#> 316 1 0.000 0.98629 1.00 0.00
#> 317 1 0.000 0.98629 1.00 0.00
#> 318 1 0.000 0.98629 1.00 0.00
#> 319 2 0.000 0.98375 0.00 1.00
#> 320 1 0.827 0.65479 0.74 0.26
#> 321 1 0.000 0.98629 1.00 0.00
#> 322 2 0.000 0.98375 0.00 1.00
#> 323 1 0.000 0.98629 1.00 0.00
#> 324 1 0.000 0.98629 1.00 0.00
#> 325 1 0.000 0.98629 1.00 0.00
#> 326 1 0.000 0.98629 1.00 0.00
#> 327 1 0.000 0.98629 1.00 0.00
#> 328 1 0.000 0.98629 1.00 0.00
#> 329 1 0.000 0.98629 1.00 0.00
#> 330 1 0.000 0.98629 1.00 0.00
#> 331 1 0.000 0.98629 1.00 0.00
#> 332 1 0.000 0.98629 1.00 0.00
#> 333 1 0.000 0.98629 1.00 0.00
#> 334 1 0.000 0.98629 1.00 0.00
#> 335 1 0.000 0.98629 1.00 0.00
#> 336 1 0.000 0.98629 1.00 0.00
#> 337 1 0.000 0.98629 1.00 0.00
#> 338 1 0.000 0.98629 1.00 0.00
#> 339 1 0.000 0.98629 1.00 0.00
#> 340 1 0.000 0.98629 1.00 0.00
#> 341 1 0.000 0.98629 1.00 0.00
#> 342 1 0.000 0.98629 1.00 0.00
#> 343 1 0.000 0.98629 1.00 0.00
#> 344 1 0.000 0.98629 1.00 0.00
#> 345 1 0.000 0.98629 1.00 0.00
#> 346 2 0.000 0.98375 0.00 1.00
#> 347 2 0.000 0.98375 0.00 1.00
#> 348 2 0.000 0.98375 0.00 1.00
#> 349 2 0.000 0.98375 0.00 1.00
#> 350 1 0.000 0.98629 1.00 0.00
#> 351 1 0.000 0.98629 1.00 0.00
#> 352 1 0.000 0.98629 1.00 0.00
#> 353 1 0.000 0.98629 1.00 0.00
#> 354 1 0.000 0.98629 1.00 0.00
#> 355 1 0.000 0.98629 1.00 0.00
#> 356 1 0.000 0.98629 1.00 0.00
#> 357 1 0.000 0.98629 1.00 0.00
#> 358 1 0.000 0.98629 1.00 0.00
#> 359 1 0.000 0.98629 1.00 0.00
#> 360 1 0.000 0.98629 1.00 0.00
#> 361 1 0.000 0.98629 1.00 0.00
#> 362 1 0.000 0.98629 1.00 0.00
#> 363 1 0.000 0.98629 1.00 0.00
#> 364 2 0.000 0.98375 0.00 1.00
#> 365 1 0.000 0.98629 1.00 0.00
#> 366 1 0.000 0.98629 1.00 0.00
#> 367 1 0.000 0.98629 1.00 0.00
#> 368 1 0.000 0.98629 1.00 0.00
#> 369 1 0.000 0.98629 1.00 0.00
#> 370 1 0.000 0.98629 1.00 0.00
#> 371 1 0.000 0.98629 1.00 0.00
#> 372 1 0.000 0.98629 1.00 0.00
#> 373 1 0.000 0.98629 1.00 0.00
#> 374 1 0.000 0.98629 1.00 0.00
#> 375 1 0.000 0.98629 1.00 0.00
#> 376 1 0.000 0.98629 1.00 0.00
#> 377 1 0.000 0.98629 1.00 0.00
#> 378 1 0.000 0.98629 1.00 0.00
#> 379 1 0.000 0.98629 1.00 0.00
#> 380 1 0.000 0.98629 1.00 0.00
#> 381 1 0.000 0.98629 1.00 0.00
#> 382 1 0.000 0.98629 1.00 0.00
#> 383 1 0.000 0.98629 1.00 0.00
#> 384 1 0.000 0.98629 1.00 0.00
#> 385 1 0.000 0.98629 1.00 0.00
#> 386 1 0.000 0.98629 1.00 0.00
#> 387 1 0.000 0.98629 1.00 0.00
#> 388 2 0.000 0.98375 0.00 1.00
#> 389 1 0.000 0.98629 1.00 0.00
#> 390 1 0.000 0.98629 1.00 0.00
#> 391 1 0.000 0.98629 1.00 0.00
#> 392 2 0.000 0.98375 0.00 1.00
#> 393 1 0.000 0.98629 1.00 0.00
#> 394 1 0.000 0.98629 1.00 0.00
#> 395 1 0.000 0.98629 1.00 0.00
#> 396 1 0.000 0.98629 1.00 0.00
#> 397 1 0.000 0.98629 1.00 0.00
#> 398 2 0.000 0.98375 0.00 1.00
#> 399 1 0.000 0.98629 1.00 0.00
#> 400 1 0.000 0.98629 1.00 0.00
#> 401 1 0.000 0.98629 1.00 0.00
#> 402 1 0.000 0.98629 1.00 0.00
#> 403 1 0.000 0.98629 1.00 0.00
#> 404 1 0.000 0.98629 1.00 0.00
#> 405 1 0.000 0.98629 1.00 0.00
#> 406 2 0.000 0.98375 0.00 1.00
#> 407 2 0.242 0.94692 0.04 0.96
#> 408 2 0.000 0.98375 0.00 1.00
#> 409 2 0.000 0.98375 0.00 1.00
#> 410 1 0.000 0.98629 1.00 0.00
#> 411 1 0.000 0.98629 1.00 0.00
#> 412 1 0.000 0.98629 1.00 0.00
#> 413 1 0.000 0.98629 1.00 0.00
#> 414 2 0.000 0.98375 0.00 1.00
#> 415 2 0.000 0.98375 0.00 1.00
#> 416 2 0.000 0.98375 0.00 1.00
#> 417 2 0.000 0.98375 0.00 1.00
#> 418 2 0.000 0.98375 0.00 1.00
#> 419 2 0.000 0.98375 0.00 1.00
#> 420 2 0.000 0.98375 0.00 1.00
#> 421 1 0.000 0.98629 1.00 0.00
#> 422 2 0.242 0.94727 0.04 0.96
#> 423 2 0.000 0.98375 0.00 1.00
#> 424 1 0.000 0.98629 1.00 0.00
#> 425 2 0.000 0.98375 0.00 1.00
#> 426 1 0.000 0.98629 1.00 0.00
#> 427 2 0.000 0.98375 0.00 1.00
#> 428 2 0.000 0.98375 0.00 1.00
#> 429 2 0.000 0.98375 0.00 1.00
#> 430 1 0.000 0.98629 1.00 0.00
#> 431 2 0.000 0.98375 0.00 1.00
#> 432 2 0.000 0.98375 0.00 1.00
#> 433 2 0.000 0.98375 0.00 1.00
#> 434 2 0.000 0.98375 0.00 1.00
#> 435 2 0.000 0.98375 0.00 1.00
#> 436 2 0.000 0.98375 0.00 1.00
#> 437 2 0.000 0.98375 0.00 1.00
#> 438 2 0.000 0.98375 0.00 1.00
#> 439 2 0.000 0.98375 0.00 1.00
#> 440 2 0.000 0.98375 0.00 1.00
#> 441 1 0.000 0.98629 1.00 0.00
#> 442 2 0.000 0.98375 0.00 1.00
#> 443 1 0.000 0.98629 1.00 0.00
#> 444 2 0.000 0.98375 0.00 1.00
#> 445 2 0.000 0.98375 0.00 1.00
#> 446 1 0.000 0.98629 1.00 0.00
#> 447 1 0.000 0.98629 1.00 0.00
#> 448 2 0.000 0.98375 0.00 1.00
#> 449 2 0.000 0.98375 0.00 1.00
#> 450 1 0.000 0.98629 1.00 0.00
#> 451 1 0.000 0.98629 1.00 0.00
#> 452 1 0.000 0.98629 1.00 0.00
#> 453 1 0.000 0.98629 1.00 0.00
#> 454 2 0.000 0.98375 0.00 1.00
#> 455 1 0.000 0.98629 1.00 0.00
#> 456 1 0.000 0.98629 1.00 0.00
#> 457 2 0.000 0.98375 0.00 1.00
#> 458 2 0.000 0.98375 0.00 1.00
#> 459 1 0.000 0.98629 1.00 0.00
#> 460 1 0.000 0.98629 1.00 0.00
#> 461 2 0.000 0.98375 0.00 1.00
#> 462 2 0.000 0.98375 0.00 1.00
#> 463 2 0.000 0.98375 0.00 1.00
#> 464 2 0.141 0.96628 0.02 0.98
#> 465 2 0.000 0.98375 0.00 1.00
#> 466 2 0.000 0.98375 0.00 1.00
#> 467 2 0.000 0.98375 0.00 1.00
#> 468 2 0.000 0.98375 0.00 1.00
#> 469 2 0.000 0.98375 0.00 1.00
#> 470 1 0.000 0.98629 1.00 0.00
#> 471 1 0.000 0.98629 1.00 0.00
#> 472 2 0.000 0.98375 0.00 1.00
#> 473 1 0.000 0.98629 1.00 0.00
#> 474 1 0.000 0.98629 1.00 0.00
#> 475 2 0.000 0.98375 0.00 1.00
#> 476 1 0.000 0.98629 1.00 0.00
#> 477 2 0.000 0.98375 0.00 1.00
#> 478 1 0.000 0.98629 1.00 0.00
#> 479 2 0.000 0.98375 0.00 1.00
#> 480 2 0.000 0.98375 0.00 1.00
#> 481 1 0.000 0.98629 1.00 0.00
#> 482 1 0.000 0.98629 1.00 0.00
#> 483 1 0.000 0.98629 1.00 0.00
#> 484 2 0.000 0.98375 0.00 1.00
#> 485 2 0.000 0.98375 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.9661 0.00 0.00 1.00
#> 2 3 0.0000 0.9661 0.00 0.00 1.00
#> 3 3 0.0000 0.9661 0.00 0.00 1.00
#> 4 3 0.0000 0.9661 0.00 0.00 1.00
#> 5 3 0.0000 0.9661 0.00 0.00 1.00
#> 6 3 0.0000 0.9661 0.00 0.00 1.00
#> 7 3 0.0000 0.9661 0.00 0.00 1.00
#> 8 3 0.0000 0.9661 0.00 0.00 1.00
#> 9 3 0.0000 0.9661 0.00 0.00 1.00
#> 10 3 0.0000 0.9661 0.00 0.00 1.00
#> 11 3 0.5216 0.6458 0.26 0.00 0.74
#> 12 3 0.0000 0.9661 0.00 0.00 1.00
#> 13 3 0.0000 0.9661 0.00 0.00 1.00
#> 14 3 0.0000 0.9661 0.00 0.00 1.00
#> 15 3 0.0000 0.9661 0.00 0.00 1.00
#> 16 1 0.0000 0.9781 1.00 0.00 0.00
#> 17 2 0.0892 0.9715 0.00 0.98 0.02
#> 18 3 0.0000 0.9661 0.00 0.00 1.00
#> 19 3 0.0000 0.9661 0.00 0.00 1.00
#> 20 3 0.0000 0.9661 0.00 0.00 1.00
#> 21 3 0.0000 0.9661 0.00 0.00 1.00
#> 22 3 0.0000 0.9661 0.00 0.00 1.00
#> 23 3 0.0000 0.9661 0.00 0.00 1.00
#> 24 1 0.0000 0.9781 1.00 0.00 0.00
#> 25 3 0.0000 0.9661 0.00 0.00 1.00
#> 26 3 0.0000 0.9661 0.00 0.00 1.00
#> 27 3 0.0000 0.9661 0.00 0.00 1.00
#> 28 3 0.0000 0.9661 0.00 0.00 1.00
#> 29 3 0.0000 0.9661 0.00 0.00 1.00
#> 30 3 0.0000 0.9661 0.00 0.00 1.00
#> 31 3 0.0000 0.9661 0.00 0.00 1.00
#> 32 1 0.0000 0.9781 1.00 0.00 0.00
#> 33 3 0.0000 0.9661 0.00 0.00 1.00
#> 34 1 0.0000 0.9781 1.00 0.00 0.00
#> 35 1 0.0000 0.9781 1.00 0.00 0.00
#> 36 3 0.0000 0.9661 0.00 0.00 1.00
#> 37 3 0.0000 0.9661 0.00 0.00 1.00
#> 38 3 0.0000 0.9661 0.00 0.00 1.00
#> 39 3 0.5948 0.4387 0.36 0.00 0.64
#> 40 3 0.0000 0.9661 0.00 0.00 1.00
#> 41 1 0.0000 0.9781 1.00 0.00 0.00
#> 42 3 0.0000 0.9661 0.00 0.00 1.00
#> 43 3 0.0000 0.9661 0.00 0.00 1.00
#> 44 3 0.0000 0.9661 0.00 0.00 1.00
#> 45 3 0.0000 0.9661 0.00 0.00 1.00
#> 46 1 0.0000 0.9781 1.00 0.00 0.00
#> 47 3 0.0000 0.9661 0.00 0.00 1.00
#> 48 3 0.0000 0.9661 0.00 0.00 1.00
#> 49 1 0.0000 0.9781 1.00 0.00 0.00
#> 50 3 0.0000 0.9661 0.00 0.00 1.00
#> 51 1 0.0000 0.9781 1.00 0.00 0.00
#> 52 3 0.0000 0.9661 0.00 0.00 1.00
#> 53 3 0.0000 0.9661 0.00 0.00 1.00
#> 54 3 0.0000 0.9661 0.00 0.00 1.00
#> 55 3 0.0000 0.9661 0.00 0.00 1.00
#> 56 3 0.0000 0.9661 0.00 0.00 1.00
#> 57 3 0.0000 0.9661 0.00 0.00 1.00
#> 58 3 0.0000 0.9661 0.00 0.00 1.00
#> 59 3 0.0000 0.9661 0.00 0.00 1.00
#> 60 3 0.0000 0.9661 0.00 0.00 1.00
#> 61 3 0.0000 0.9661 0.00 0.00 1.00
#> 62 3 0.0000 0.9661 0.00 0.00 1.00
#> 63 3 0.0000 0.9661 0.00 0.00 1.00
#> 64 3 0.0000 0.9661 0.00 0.00 1.00
#> 65 3 0.0000 0.9661 0.00 0.00 1.00
#> 66 3 0.0000 0.9661 0.00 0.00 1.00
#> 67 3 0.0000 0.9661 0.00 0.00 1.00
#> 68 3 0.0000 0.9661 0.00 0.00 1.00
#> 69 3 0.0000 0.9661 0.00 0.00 1.00
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#> 436 2 0.0000 0.9912 0.00 1.00 0.00
#> 437 3 0.0000 0.9661 0.00 0.00 1.00
#> 438 3 0.0000 0.9661 0.00 0.00 1.00
#> 439 3 0.0000 0.9661 0.00 0.00 1.00
#> 440 2 0.0000 0.9912 0.00 1.00 0.00
#> 441 1 0.0000 0.9781 1.00 0.00 0.00
#> 442 2 0.0000 0.9912 0.00 1.00 0.00
#> 443 1 0.0000 0.9781 1.00 0.00 0.00
#> 444 3 0.5397 0.6462 0.00 0.28 0.72
#> 445 2 0.0000 0.9912 0.00 1.00 0.00
#> 446 1 0.0000 0.9781 1.00 0.00 0.00
#> 447 1 0.0000 0.9781 1.00 0.00 0.00
#> 448 3 0.0000 0.9661 0.00 0.00 1.00
#> 449 2 0.0000 0.9912 0.00 1.00 0.00
#> 450 1 0.0000 0.9781 1.00 0.00 0.00
#> 451 1 0.0000 0.9781 1.00 0.00 0.00
#> 452 1 0.0000 0.9781 1.00 0.00 0.00
#> 453 1 0.0000 0.9781 1.00 0.00 0.00
#> 454 2 0.0000 0.9912 0.00 1.00 0.00
#> 455 1 0.0000 0.9781 1.00 0.00 0.00
#> 456 1 0.2537 0.8982 0.92 0.00 0.08
#> 457 3 0.0000 0.9661 0.00 0.00 1.00
#> 458 3 0.2537 0.9038 0.00 0.08 0.92
#> 459 1 0.0000 0.9781 1.00 0.00 0.00
#> 460 1 0.0000 0.9781 1.00 0.00 0.00
#> 461 3 0.0000 0.9661 0.00 0.00 1.00
#> 462 2 0.0000 0.9912 0.00 1.00 0.00
#> 463 2 0.0000 0.9912 0.00 1.00 0.00
#> 464 3 0.0000 0.9661 0.00 0.00 1.00
#> 465 2 0.0000 0.9912 0.00 1.00 0.00
#> 466 2 0.0000 0.9912 0.00 1.00 0.00
#> 467 3 0.0000 0.9661 0.00 0.00 1.00
#> 468 3 0.0000 0.9661 0.00 0.00 1.00
#> 469 2 0.0000 0.9912 0.00 1.00 0.00
#> 470 1 0.0000 0.9781 1.00 0.00 0.00
#> 471 1 0.0000 0.9781 1.00 0.00 0.00
#> 472 2 0.0000 0.9912 0.00 1.00 0.00
#> 473 1 0.0000 0.9781 1.00 0.00 0.00
#> 474 1 0.0000 0.9781 1.00 0.00 0.00
#> 475 3 0.0000 0.9661 0.00 0.00 1.00
#> 476 3 0.0000 0.9661 0.00 0.00 1.00
#> 477 2 0.2959 0.8798 0.00 0.90 0.10
#> 478 1 0.0000 0.9781 1.00 0.00 0.00
#> 479 3 0.0000 0.9661 0.00 0.00 1.00
#> 480 3 0.0000 0.9661 0.00 0.00 1.00
#> 481 1 0.0000 0.9781 1.00 0.00 0.00
#> 482 1 0.0000 0.9781 1.00 0.00 0.00
#> 483 3 0.3340 0.8504 0.12 0.00 0.88
#> 484 3 0.2066 0.9218 0.00 0.06 0.94
#> 485 3 0.0000 0.9661 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 2 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 3 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 4 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 5 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 6 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 7 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 8 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 9 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 10 3 0.3400 0.7564 0.00 0.00 0.82 0.18
#> 11 3 0.0707 0.9515 0.02 0.00 0.98 0.00
#> 12 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 13 3 0.4522 0.5325 0.00 0.00 0.68 0.32
#> 14 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 15 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 16 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 17 2 0.3821 0.8535 0.00 0.84 0.04 0.12
#> 18 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 19 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 20 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 21 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 22 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 23 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 24 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 25 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 26 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 27 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 28 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 29 4 0.3975 0.6793 0.00 0.00 0.24 0.76
#> 30 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 31 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 32 1 0.1637 0.9160 0.94 0.00 0.06 0.00
#> 33 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 34 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 35 4 0.2011 0.8456 0.08 0.00 0.00 0.92
#> 36 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 37 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 38 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 39 4 0.4755 0.7050 0.20 0.00 0.04 0.76
#> 40 4 0.2921 0.7975 0.00 0.00 0.14 0.86
#> 41 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 42 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 43 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 44 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 45 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 46 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 47 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 48 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 49 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 50 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 51 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 52 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 53 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 54 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 55 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 56 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 57 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 58 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 59 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 60 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 61 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 62 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 63 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 64 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 65 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 66 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 67 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 68 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 69 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 70 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 71 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 72 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 73 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 74 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 75 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 76 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 77 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 78 3 0.3172 0.7824 0.00 0.00 0.84 0.16
#> 79 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 80 3 0.4731 0.7025 0.00 0.06 0.78 0.16
#> 81 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 82 3 0.2921 0.8117 0.00 0.00 0.86 0.14
#> 83 3 0.4907 0.2913 0.00 0.00 0.58 0.42
#> 84 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 85 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 86 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 87 2 0.3801 0.7957 0.00 0.78 0.00 0.22
#> 88 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 89 1 0.3172 0.7962 0.84 0.16 0.00 0.00
#> 90 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 91 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 92 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 93 2 0.2011 0.8271 0.08 0.92 0.00 0.00
#> 94 2 0.2647 0.8700 0.00 0.88 0.00 0.12
#> 95 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 96 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 97 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 98 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 99 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 100 2 0.4713 0.4518 0.36 0.64 0.00 0.00
#> 101 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 102 2 0.0707 0.8940 0.00 0.98 0.00 0.02
#> 103 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 104 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 105 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 106 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 107 3 0.1211 0.9239 0.04 0.00 0.96 0.00
#> 108 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 109 4 0.2647 0.8093 0.12 0.00 0.00 0.88
#> 110 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 111 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 112 2 0.0707 0.8940 0.00 0.98 0.00 0.02
#> 113 1 0.4610 0.7450 0.80 0.10 0.00 0.10
#> 114 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 115 2 0.2345 0.8792 0.00 0.90 0.00 0.10
#> 116 2 0.1211 0.8916 0.00 0.96 0.00 0.04
#> 117 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 118 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 119 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 120 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 121 2 0.3801 0.7957 0.00 0.78 0.00 0.22
#> 122 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 123 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 124 2 0.2011 0.8841 0.00 0.92 0.00 0.08
#> 125 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 126 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 127 2 0.2647 0.8700 0.00 0.88 0.00 0.12
#> 128 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 129 2 0.0707 0.8940 0.00 0.98 0.00 0.02
#> 130 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 131 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 132 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 133 2 0.3975 0.7739 0.00 0.76 0.00 0.24
#> 134 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 135 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 136 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 137 2 0.4277 0.7238 0.00 0.72 0.00 0.28
#> 138 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 139 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 140 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 141 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 142 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 143 2 0.4406 0.5187 0.30 0.70 0.00 0.00
#> 144 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 145 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 146 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 147 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 148 2 0.2345 0.8048 0.10 0.90 0.00 0.00
#> 149 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 150 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 151 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 152 1 0.0707 0.9556 0.98 0.02 0.00 0.00
#> 153 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 154 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 155 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 156 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 157 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 158 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 159 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 160 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 161 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 162 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 163 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 164 2 0.4994 0.3116 0.00 0.52 0.00 0.48
#> 165 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 166 1 0.4977 0.1845 0.54 0.46 0.00 0.00
#> 167 2 0.7179 0.2830 0.00 0.48 0.38 0.14
#> 168 2 0.3606 0.8529 0.00 0.84 0.02 0.14
#> 169 2 0.3801 0.7957 0.00 0.78 0.00 0.22
#> 170 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 171 2 0.4855 0.3255 0.40 0.60 0.00 0.00
#> 172 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 173 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 174 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 175 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 176 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 177 1 0.4907 0.2222 0.58 0.00 0.00 0.42
#> 178 4 0.0707 0.8867 0.00 0.02 0.00 0.98
#> 179 2 0.3801 0.7957 0.00 0.78 0.00 0.22
#> 180 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 181 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 182 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 183 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 184 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 185 2 0.1637 0.8892 0.00 0.94 0.00 0.06
#> 186 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 187 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 188 1 0.1211 0.9365 0.96 0.04 0.00 0.00
#> 189 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 190 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 191 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 192 2 0.4790 0.3841 0.38 0.62 0.00 0.00
#> 193 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 194 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 195 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 196 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 197 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 198 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 199 2 0.0707 0.8940 0.00 0.98 0.00 0.02
#> 200 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 201 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 202 2 0.0707 0.8940 0.00 0.98 0.00 0.02
#> 203 2 0.3610 0.8154 0.00 0.80 0.00 0.20
#> 204 2 0.3610 0.8154 0.00 0.80 0.00 0.20
#> 205 2 0.1637 0.8883 0.00 0.94 0.00 0.06
#> 206 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 207 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 208 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 209 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 210 2 0.3975 0.7745 0.00 0.76 0.00 0.24
#> 211 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 212 2 0.1637 0.8887 0.00 0.94 0.00 0.06
#> 213 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 214 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 215 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 216 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 217 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 218 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 219 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 220 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 221 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 222 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 223 1 0.3172 0.8029 0.84 0.16 0.00 0.00
#> 224 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 225 2 0.4277 0.7245 0.00 0.72 0.00 0.28
#> 226 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 227 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 228 1 0.3975 0.6894 0.76 0.24 0.00 0.00
#> 229 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 230 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 231 1 0.2345 0.8736 0.90 0.10 0.00 0.00
#> 232 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 233 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 234 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 235 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 236 1 0.2011 0.8955 0.92 0.08 0.00 0.00
#> 237 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 238 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 239 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 240 1 0.4855 0.3776 0.60 0.40 0.00 0.00
#> 241 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 242 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 243 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 244 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 245 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 246 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 247 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 248 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 249 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 250 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 251 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 252 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 253 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 254 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 255 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 256 2 0.4977 0.1033 0.46 0.54 0.00 0.00
#> 257 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 258 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 259 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 260 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 261 2 0.4855 0.3092 0.40 0.60 0.00 0.00
#> 262 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 263 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 264 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 265 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 266 1 0.3801 0.7181 0.78 0.22 0.00 0.00
#> 267 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 268 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 269 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 270 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 271 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 272 2 0.2011 0.8841 0.00 0.92 0.00 0.08
#> 273 2 0.4948 0.1760 0.44 0.56 0.00 0.00
#> 274 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 275 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 276 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 277 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 278 4 0.0707 0.8865 0.00 0.02 0.00 0.98
#> 279 2 0.4994 0.0296 0.48 0.52 0.00 0.00
#> 280 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 281 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 282 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 283 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 284 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 285 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 286 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 287 1 0.2921 0.8264 0.86 0.14 0.00 0.00
#> 288 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 289 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 290 1 0.4790 0.4269 0.62 0.38 0.00 0.00
#> 291 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 292 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 293 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 294 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 295 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 296 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 297 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 298 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 299 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 300 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 301 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 302 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 303 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 304 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 305 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 306 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 307 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 308 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 309 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 310 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 311 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 312 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 313 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 314 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 315 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 316 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 317 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 318 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 319 2 0.2011 0.8841 0.00 0.92 0.00 0.08
#> 320 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 321 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 322 2 0.4134 0.7511 0.00 0.74 0.00 0.26
#> 323 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 324 1 0.3400 0.7753 0.82 0.18 0.00 0.00
#> 325 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 326 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 327 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 328 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 329 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 330 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 331 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 332 1 0.2647 0.8465 0.88 0.00 0.00 0.12
#> 333 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 334 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 335 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 336 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 337 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 338 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 339 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 340 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 341 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 342 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 343 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 344 1 0.0707 0.9555 0.98 0.00 0.02 0.00
#> 345 1 0.1637 0.9165 0.94 0.06 0.00 0.00
#> 346 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 347 2 0.3610 0.8154 0.00 0.80 0.00 0.20
#> 348 2 0.2011 0.8841 0.00 0.92 0.00 0.08
#> 349 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 350 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 351 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 352 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 353 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 354 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 355 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 356 2 0.4948 0.1777 0.44 0.56 0.00 0.00
#> 357 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 358 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 359 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 360 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 361 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 362 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 363 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 364 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 365 1 0.2345 0.8736 0.90 0.10 0.00 0.00
#> 366 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 367 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 368 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 369 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 370 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 371 1 0.2011 0.8948 0.92 0.00 0.00 0.08
#> 372 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 373 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 374 1 0.2345 0.8736 0.90 0.10 0.00 0.00
#> 375 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 376 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 377 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 378 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 379 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 380 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 381 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 382 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 383 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 384 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 385 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 386 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 387 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 388 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 389 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 390 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 391 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 392 2 0.1211 0.8917 0.00 0.96 0.00 0.04
#> 393 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 394 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 395 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 396 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 397 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 398 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 399 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 400 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 401 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 402 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 403 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 404 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 405 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 406 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 407 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 408 2 0.4522 0.6635 0.00 0.68 0.00 0.32
#> 409 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 410 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 411 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 412 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 413 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 414 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 415 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 416 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 417 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 418 4 0.3172 0.7765 0.00 0.00 0.16 0.84
#> 419 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 420 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 421 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 422 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 423 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 424 1 0.2011 0.8953 0.92 0.00 0.00 0.08
#> 425 4 0.2647 0.8174 0.00 0.00 0.12 0.88
#> 426 4 0.3172 0.7682 0.16 0.00 0.00 0.84
#> 427 4 0.0707 0.8934 0.00 0.00 0.02 0.98
#> 428 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 429 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 430 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 431 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 432 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 433 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 434 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 435 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 436 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 437 4 0.2345 0.8352 0.00 0.00 0.10 0.90
#> 438 4 0.3610 0.7373 0.00 0.00 0.20 0.80
#> 439 4 0.2345 0.8352 0.00 0.00 0.10 0.90
#> 440 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 441 4 0.4713 0.4717 0.36 0.00 0.00 0.64
#> 442 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 443 4 0.5000 0.0541 0.50 0.00 0.00 0.50
#> 444 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 445 2 0.2345 0.8785 0.00 0.90 0.00 0.10
#> 446 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 447 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 448 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 449 2 0.4134 0.7525 0.00 0.74 0.00 0.26
#> 450 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 451 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 452 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 453 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 454 2 0.3172 0.8474 0.00 0.84 0.00 0.16
#> 455 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 456 4 0.2647 0.8093 0.12 0.00 0.00 0.88
#> 457 4 0.4406 0.5347 0.00 0.00 0.30 0.70
#> 458 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 459 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 460 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 461 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 462 2 0.4406 0.5965 0.00 0.70 0.00 0.30
#> 463 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 464 4 0.0707 0.8934 0.00 0.00 0.02 0.98
#> 465 2 0.0000 0.8955 0.00 1.00 0.00 0.00
#> 466 2 0.3400 0.8329 0.00 0.82 0.00 0.18
#> 467 4 0.5000 0.0342 0.00 0.00 0.50 0.50
#> 468 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 469 2 0.2921 0.8601 0.00 0.86 0.00 0.14
#> 470 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 471 1 0.0707 0.9558 0.98 0.00 0.00 0.02
#> 472 2 0.2011 0.8841 0.00 0.92 0.00 0.08
#> 473 4 0.4624 0.5187 0.34 0.00 0.00 0.66
#> 474 4 0.3172 0.7669 0.16 0.00 0.00 0.84
#> 475 4 0.1637 0.8674 0.00 0.00 0.06 0.94
#> 476 4 0.3935 0.8023 0.06 0.00 0.10 0.84
#> 477 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 478 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 479 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 480 3 0.0000 0.9782 0.00 0.00 1.00 0.00
#> 481 1 0.4624 0.4511 0.66 0.00 0.00 0.34
#> 482 1 0.0000 0.9738 1.00 0.00 0.00 0.00
#> 483 4 0.3335 0.8005 0.12 0.00 0.02 0.86
#> 484 4 0.0000 0.9037 0.00 0.00 0.00 1.00
#> 485 4 0.2921 0.7975 0.00 0.00 0.14 0.86
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 476 3.77e-03 2
#> ATC:skmeans 480 5.10e-07 3
#> ATC:skmeans 466 3.65e-34 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node011. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0113"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7277 rows and 119 columns.
#> Top rows (728) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.982 0.963 0.984 0.4984 0.499 0.499
#> 3 3 1.000 0.964 0.985 0.3433 0.724 0.501
#> 4 4 0.814 0.855 0.898 0.0957 0.927 0.781
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.000 0.999 1.00 0.00
#> 2 1 0.000 0.999 1.00 0.00
#> 3 2 0.999 0.131 0.48 0.52
#> 4 1 0.000 0.999 1.00 0.00
#> 5 1 0.000 0.999 1.00 0.00
#> 6 1 0.000 0.999 1.00 0.00
#> 7 1 0.000 0.999 1.00 0.00
#> 8 1 0.000 0.999 1.00 0.00
#> 9 1 0.000 0.999 1.00 0.00
#> 10 2 0.242 0.935 0.04 0.96
#> 11 1 0.000 0.999 1.00 0.00
#> 12 1 0.000 0.999 1.00 0.00
#> 13 2 0.000 0.964 0.00 1.00
#> 14 1 0.000 0.999 1.00 0.00
#> 15 1 0.000 0.999 1.00 0.00
#> 16 2 0.722 0.762 0.20 0.80
#> 17 1 0.000 0.999 1.00 0.00
#> 18 2 0.722 0.767 0.20 0.80
#> 19 1 0.000 0.999 1.00 0.00
#> 20 1 0.000 0.999 1.00 0.00
#> 21 1 0.000 0.999 1.00 0.00
#> 22 1 0.000 0.999 1.00 0.00
#> 23 1 0.000 0.999 1.00 0.00
#> 24 1 0.000 0.999 1.00 0.00
#> 25 1 0.000 0.999 1.00 0.00
#> 26 2 0.000 0.964 0.00 1.00
#> 27 1 0.000 0.999 1.00 0.00
#> 28 1 0.000 0.999 1.00 0.00
#> 29 1 0.000 0.999 1.00 0.00
#> 30 1 0.000 0.999 1.00 0.00
#> 31 1 0.000 0.999 1.00 0.00
#> 32 2 0.327 0.918 0.06 0.94
#> 33 2 0.000 0.964 0.00 1.00
#> 34 2 0.000 0.964 0.00 1.00
#> 35 2 0.904 0.568 0.32 0.68
#> 36 2 0.904 0.564 0.32 0.68
#> 37 1 0.000 0.999 1.00 0.00
#> 38 1 0.000 0.999 1.00 0.00
#> 39 1 0.000 0.999 1.00 0.00
#> 40 1 0.000 0.999 1.00 0.00
#> 41 1 0.000 0.999 1.00 0.00
#> 42 1 0.000 0.999 1.00 0.00
#> 43 1 0.000 0.999 1.00 0.00
#> 44 1 0.000 0.999 1.00 0.00
#> 45 1 0.000 0.999 1.00 0.00
#> 46 1 0.000 0.999 1.00 0.00
#> 47 1 0.000 0.999 1.00 0.00
#> 48 1 0.000 0.999 1.00 0.00
#> 49 1 0.000 0.999 1.00 0.00
#> 50 1 0.000 0.999 1.00 0.00
#> 51 1 0.000 0.999 1.00 0.00
#> 52 1 0.000 0.999 1.00 0.00
#> 53 1 0.000 0.999 1.00 0.00
#> 54 1 0.000 0.999 1.00 0.00
#> 55 1 0.000 0.999 1.00 0.00
#> 56 1 0.000 0.999 1.00 0.00
#> 57 1 0.000 0.999 1.00 0.00
#> 58 1 0.000 0.999 1.00 0.00
#> 59 1 0.000 0.999 1.00 0.00
#> 60 1 0.000 0.999 1.00 0.00
#> 61 1 0.000 0.999 1.00 0.00
#> 62 1 0.000 0.999 1.00 0.00
#> 63 2 0.000 0.964 0.00 1.00
#> 64 1 0.000 0.999 1.00 0.00
#> 65 1 0.141 0.979 0.98 0.02
#> 66 1 0.000 0.999 1.00 0.00
#> 67 1 0.000 0.999 1.00 0.00
#> 68 2 0.000 0.964 0.00 1.00
#> 69 1 0.000 0.999 1.00 0.00
#> 70 2 0.402 0.901 0.08 0.92
#> 71 1 0.000 0.999 1.00 0.00
#> 72 2 0.469 0.883 0.10 0.90
#> 73 2 0.000 0.964 0.00 1.00
#> 74 1 0.000 0.999 1.00 0.00
#> 75 2 0.000 0.964 0.00 1.00
#> 76 2 0.000 0.964 0.00 1.00
#> 77 1 0.000 0.999 1.00 0.00
#> 78 2 0.529 0.862 0.12 0.88
#> 79 2 0.000 0.964 0.00 1.00
#> 80 2 0.000 0.964 0.00 1.00
#> 81 2 0.000 0.964 0.00 1.00
#> 82 2 0.000 0.964 0.00 1.00
#> 83 2 0.000 0.964 0.00 1.00
#> 84 2 0.000 0.964 0.00 1.00
#> 85 2 0.000 0.964 0.00 1.00
#> 86 2 0.000 0.964 0.00 1.00
#> 87 2 0.000 0.964 0.00 1.00
#> 88 2 0.000 0.964 0.00 1.00
#> 89 2 0.000 0.964 0.00 1.00
#> 90 2 0.000 0.964 0.00 1.00
#> 91 2 0.000 0.964 0.00 1.00
#> 92 2 0.000 0.964 0.00 1.00
#> 93 2 0.000 0.964 0.00 1.00
#> 94 2 0.000 0.964 0.00 1.00
#> 95 2 0.000 0.964 0.00 1.00
#> 96 2 0.000 0.964 0.00 1.00
#> 97 2 0.000 0.964 0.00 1.00
#> 98 2 0.000 0.964 0.00 1.00
#> 99 1 0.000 0.999 1.00 0.00
#> 100 2 0.000 0.964 0.00 1.00
#> 101 1 0.000 0.999 1.00 0.00
#> 102 2 0.000 0.964 0.00 1.00
#> 103 2 0.000 0.964 0.00 1.00
#> 104 2 0.000 0.964 0.00 1.00
#> 105 2 0.000 0.964 0.00 1.00
#> 106 1 0.000 0.999 1.00 0.00
#> 107 2 0.000 0.964 0.00 1.00
#> 108 2 0.000 0.964 0.00 1.00
#> 109 2 0.000 0.964 0.00 1.00
#> 110 2 0.000 0.964 0.00 1.00
#> 111 2 0.000 0.964 0.00 1.00
#> 112 1 0.141 0.979 0.98 0.02
#> 113 2 0.000 0.964 0.00 1.00
#> 114 2 0.000 0.964 0.00 1.00
#> 115 2 0.000 0.964 0.00 1.00
#> 116 1 0.000 0.999 1.00 0.00
#> 117 2 0.000 0.964 0.00 1.00
#> 118 2 0.000 0.964 0.00 1.00
#> 119 2 0.000 0.964 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 1 0.0000 0.988 1.00 0.00 0.00
#> 2 1 0.0000 0.988 1.00 0.00 0.00
#> 3 1 0.6176 0.767 0.78 0.12 0.10
#> 4 1 0.0000 0.988 1.00 0.00 0.00
#> 5 1 0.0000 0.988 1.00 0.00 0.00
#> 6 1 0.0000 0.988 1.00 0.00 0.00
#> 7 1 0.0000 0.988 1.00 0.00 0.00
#> 8 1 0.0000 0.988 1.00 0.00 0.00
#> 9 1 0.0000 0.988 1.00 0.00 0.00
#> 10 3 0.2959 0.886 0.00 0.10 0.90
#> 11 1 0.0000 0.988 1.00 0.00 0.00
#> 12 1 0.0000 0.988 1.00 0.00 0.00
#> 13 2 0.0000 0.974 0.00 1.00 0.00
#> 14 1 0.0000 0.988 1.00 0.00 0.00
#> 15 1 0.0000 0.988 1.00 0.00 0.00
#> 16 1 0.5016 0.687 0.76 0.24 0.00
#> 17 1 0.0000 0.988 1.00 0.00 0.00
#> 18 3 0.0000 0.990 0.00 0.00 1.00
#> 19 1 0.0000 0.988 1.00 0.00 0.00
#> 20 1 0.0000 0.988 1.00 0.00 0.00
#> 21 1 0.0000 0.988 1.00 0.00 0.00
#> 22 1 0.0000 0.988 1.00 0.00 0.00
#> 23 1 0.0000 0.988 1.00 0.00 0.00
#> 24 1 0.0000 0.988 1.00 0.00 0.00
#> 25 1 0.0000 0.988 1.00 0.00 0.00
#> 26 2 0.0000 0.974 0.00 1.00 0.00
#> 27 1 0.0000 0.988 1.00 0.00 0.00
#> 28 1 0.1529 0.952 0.96 0.00 0.04
#> 29 3 0.0000 0.990 0.00 0.00 1.00
#> 30 1 0.0000 0.988 1.00 0.00 0.00
#> 31 1 0.0000 0.988 1.00 0.00 0.00
#> 32 3 0.0000 0.990 0.00 0.00 1.00
#> 33 2 0.0000 0.974 0.00 1.00 0.00
#> 34 2 0.0000 0.974 0.00 1.00 0.00
#> 35 3 0.0000 0.990 0.00 0.00 1.00
#> 36 3 0.0000 0.990 0.00 0.00 1.00
#> 37 1 0.0892 0.971 0.98 0.00 0.02
#> 38 3 0.0000 0.990 0.00 0.00 1.00
#> 39 1 0.0000 0.988 1.00 0.00 0.00
#> 40 1 0.0000 0.988 1.00 0.00 0.00
#> 41 1 0.0000 0.988 1.00 0.00 0.00
#> 42 1 0.0000 0.988 1.00 0.00 0.00
#> 43 1 0.0000 0.988 1.00 0.00 0.00
#> 44 1 0.0000 0.988 1.00 0.00 0.00
#> 45 1 0.0000 0.988 1.00 0.00 0.00
#> 46 1 0.0000 0.988 1.00 0.00 0.00
#> 47 1 0.0000 0.988 1.00 0.00 0.00
#> 48 1 0.0000 0.988 1.00 0.00 0.00
#> 49 1 0.0892 0.971 0.98 0.00 0.02
#> 50 1 0.0000 0.988 1.00 0.00 0.00
#> 51 1 0.0000 0.988 1.00 0.00 0.00
#> 52 3 0.0000 0.990 0.00 0.00 1.00
#> 53 1 0.0000 0.988 1.00 0.00 0.00
#> 54 3 0.0000 0.990 0.00 0.00 1.00
#> 55 3 0.0000 0.990 0.00 0.00 1.00
#> 56 3 0.0000 0.990 0.00 0.00 1.00
#> 57 3 0.0000 0.990 0.00 0.00 1.00
#> 58 3 0.0000 0.990 0.00 0.00 1.00
#> 59 3 0.0000 0.990 0.00 0.00 1.00
#> 60 3 0.0000 0.990 0.00 0.00 1.00
#> 61 3 0.0000 0.990 0.00 0.00 1.00
#> 62 3 0.0000 0.990 0.00 0.00 1.00
#> 63 3 0.0000 0.990 0.00 0.00 1.00
#> 64 3 0.0000 0.990 0.00 0.00 1.00
#> 65 3 0.0000 0.990 0.00 0.00 1.00
#> 66 3 0.0000 0.990 0.00 0.00 1.00
#> 67 3 0.0000 0.990 0.00 0.00 1.00
#> 68 3 0.0000 0.990 0.00 0.00 1.00
#> 69 3 0.0000 0.990 0.00 0.00 1.00
#> 70 3 0.0000 0.990 0.00 0.00 1.00
#> 71 3 0.0000 0.990 0.00 0.00 1.00
#> 72 3 0.0000 0.990 0.00 0.00 1.00
#> 73 3 0.0000 0.990 0.00 0.00 1.00
#> 74 3 0.0000 0.990 0.00 0.00 1.00
#> 75 2 0.2537 0.898 0.00 0.92 0.08
#> 76 2 0.0000 0.974 0.00 1.00 0.00
#> 77 3 0.0000 0.990 0.00 0.00 1.00
#> 78 3 0.0000 0.990 0.00 0.00 1.00
#> 79 2 0.6045 0.393 0.00 0.62 0.38
#> 80 2 0.0000 0.974 0.00 1.00 0.00
#> 81 2 0.0000 0.974 0.00 1.00 0.00
#> 82 2 0.0000 0.974 0.00 1.00 0.00
#> 83 2 0.0000 0.974 0.00 1.00 0.00
#> 84 2 0.0000 0.974 0.00 1.00 0.00
#> 85 2 0.0000 0.974 0.00 1.00 0.00
#> 86 2 0.0000 0.974 0.00 1.00 0.00
#> 87 2 0.0000 0.974 0.00 1.00 0.00
#> 88 2 0.0000 0.974 0.00 1.00 0.00
#> 89 2 0.0000 0.974 0.00 1.00 0.00
#> 90 2 0.0000 0.974 0.00 1.00 0.00
#> 91 2 0.0000 0.974 0.00 1.00 0.00
#> 92 2 0.0000 0.974 0.00 1.00 0.00
#> 93 2 0.0000 0.974 0.00 1.00 0.00
#> 94 2 0.0000 0.974 0.00 1.00 0.00
#> 95 2 0.0000 0.974 0.00 1.00 0.00
#> 96 2 0.0000 0.974 0.00 1.00 0.00
#> 97 2 0.0000 0.974 0.00 1.00 0.00
#> 98 2 0.0000 0.974 0.00 1.00 0.00
#> 99 1 0.0000 0.988 1.00 0.00 0.00
#> 100 2 0.0000 0.974 0.00 1.00 0.00
#> 101 1 0.0000 0.988 1.00 0.00 0.00
#> 102 2 0.0000 0.974 0.00 1.00 0.00
#> 103 2 0.6244 0.231 0.00 0.56 0.44
#> 104 2 0.2066 0.920 0.00 0.94 0.06
#> 105 2 0.0000 0.974 0.00 1.00 0.00
#> 106 1 0.0000 0.988 1.00 0.00 0.00
#> 107 3 0.4291 0.778 0.00 0.18 0.82
#> 108 2 0.0000 0.974 0.00 1.00 0.00
#> 109 3 0.0000 0.990 0.00 0.00 1.00
#> 110 2 0.0000 0.974 0.00 1.00 0.00
#> 111 3 0.2066 0.932 0.00 0.06 0.94
#> 112 3 0.0000 0.990 0.00 0.00 1.00
#> 113 2 0.0000 0.974 0.00 1.00 0.00
#> 114 2 0.0000 0.974 0.00 1.00 0.00
#> 115 2 0.0000 0.974 0.00 1.00 0.00
#> 116 1 0.0000 0.988 1.00 0.00 0.00
#> 117 2 0.0000 0.974 0.00 1.00 0.00
#> 118 2 0.0000 0.974 0.00 1.00 0.00
#> 119 2 0.0000 0.974 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 2 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 3 4 0.6930 0.684 0.14 0.06 0.12 0.68
#> 4 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 5 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 6 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 7 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 8 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 9 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 10 4 0.5636 0.457 0.00 0.06 0.26 0.68
#> 11 4 0.4134 0.753 0.26 0.00 0.00 0.74
#> 12 1 0.3172 0.690 0.84 0.00 0.00 0.16
#> 13 4 0.4624 0.361 0.00 0.34 0.00 0.66
#> 14 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 15 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 16 4 0.1637 0.617 0.06 0.00 0.00 0.94
#> 17 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 18 3 0.4277 0.746 0.00 0.00 0.72 0.28
#> 19 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 20 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 21 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 22 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 23 4 0.4790 0.812 0.38 0.00 0.00 0.62
#> 24 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 25 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 26 2 0.4406 0.708 0.00 0.70 0.00 0.30
#> 27 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 28 1 0.2011 0.853 0.92 0.00 0.08 0.00
#> 29 3 0.2647 0.798 0.12 0.00 0.88 0.00
#> 30 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 31 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 32 3 0.4406 0.733 0.00 0.00 0.70 0.30
#> 33 2 0.4713 0.661 0.00 0.64 0.00 0.36
#> 34 2 0.4624 0.673 0.00 0.66 0.00 0.34
#> 35 3 0.1211 0.893 0.00 0.00 0.96 0.04
#> 36 3 0.4624 0.698 0.00 0.00 0.66 0.34
#> 37 1 0.5062 0.452 0.68 0.00 0.02 0.30
#> 38 3 0.1211 0.893 0.00 0.00 0.96 0.04
#> 39 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 40 1 0.1211 0.912 0.96 0.00 0.04 0.00
#> 41 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 42 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 43 1 0.0707 0.939 0.98 0.00 0.00 0.02
#> 44 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 45 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 46 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 47 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 48 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 49 1 0.1211 0.913 0.96 0.00 0.04 0.00
#> 50 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 51 1 0.2011 0.853 0.92 0.00 0.08 0.00
#> 52 3 0.2647 0.795 0.12 0.00 0.88 0.00
#> 53 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 54 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 55 3 0.0707 0.901 0.00 0.00 0.98 0.02
#> 56 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 57 3 0.4624 0.698 0.00 0.00 0.66 0.34
#> 58 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 59 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 60 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 61 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 62 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 63 3 0.2345 0.865 0.00 0.00 0.90 0.10
#> 64 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 65 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 66 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 67 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 68 3 0.4624 0.698 0.00 0.00 0.66 0.34
#> 69 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 70 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 71 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 72 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 73 3 0.4624 0.698 0.00 0.00 0.66 0.34
#> 74 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 75 2 0.1637 0.875 0.00 0.94 0.06 0.00
#> 76 2 0.4522 0.691 0.00 0.68 0.00 0.32
#> 77 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 78 3 0.0707 0.900 0.00 0.00 0.98 0.02
#> 79 2 0.5915 0.286 0.00 0.56 0.40 0.04
#> 80 2 0.0707 0.906 0.00 0.98 0.00 0.02
#> 81 2 0.2011 0.885 0.00 0.92 0.00 0.08
#> 82 2 0.1211 0.900 0.00 0.96 0.00 0.04
#> 83 2 0.2647 0.864 0.00 0.88 0.00 0.12
#> 84 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 85 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 86 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 87 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 88 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 89 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 90 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 91 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 92 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 93 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 94 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 95 2 0.1211 0.898 0.00 0.96 0.00 0.04
#> 96 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 97 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 98 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 99 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 100 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 101 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 102 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 103 2 0.3801 0.708 0.00 0.78 0.22 0.00
#> 104 2 0.1637 0.875 0.00 0.94 0.06 0.00
#> 105 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 106 1 0.0000 0.962 1.00 0.00 0.00 0.00
#> 107 3 0.4936 0.575 0.00 0.28 0.70 0.02
#> 108 2 0.0707 0.906 0.00 0.98 0.00 0.02
#> 109 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 110 2 0.0000 0.912 0.00 1.00 0.00 0.00
#> 111 3 0.5271 0.678 0.00 0.02 0.64 0.34
#> 112 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> 113 2 0.0707 0.906 0.00 0.98 0.00 0.02
#> 114 2 0.4522 0.707 0.00 0.68 0.00 0.32
#> 115 2 0.0707 0.906 0.00 0.98 0.00 0.02
#> 116 4 0.4713 0.805 0.36 0.00 0.00 0.64
#> 117 2 0.2345 0.877 0.00 0.90 0.00 0.10
#> 118 2 0.2647 0.866 0.00 0.88 0.00 0.12
#> 119 2 0.4277 0.745 0.00 0.72 0.00 0.28
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 118 2.58e-06 2
#> ATC:skmeans 117 5.39e-09 3
#> ATC:skmeans 115 7.83e-08 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["012"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9007 rows and 448 columns.
#> Top rows (901) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.886 0.950 0.977 0.5001 0.499 0.499
#> 3 3 1.000 0.988 0.995 0.3252 0.755 0.547
#> 4 4 0.789 0.447 0.552 0.0969 0.847 0.594
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.760 0.748 0.22 0.78
#> 2 2 0.722 0.776 0.20 0.80
#> 3 2 0.722 0.776 0.20 0.80
#> 4 1 0.327 0.926 0.94 0.06
#> 5 2 0.722 0.776 0.20 0.80
#> 6 2 0.722 0.776 0.20 0.80
#> 7 2 0.634 0.821 0.16 0.84
#> 8 2 0.722 0.776 0.20 0.80
#> 9 2 0.722 0.776 0.20 0.80
#> 10 2 0.722 0.776 0.20 0.80
#> 11 2 0.722 0.776 0.20 0.80
#> 12 2 0.722 0.776 0.20 0.80
#> 13 2 0.855 0.652 0.28 0.72
#> 14 2 0.722 0.776 0.20 0.80
#> 15 2 0.722 0.776 0.20 0.80
#> 16 2 0.722 0.776 0.20 0.80
#> 17 2 0.722 0.776 0.20 0.80
#> 18 2 0.722 0.776 0.20 0.80
#> 19 2 0.722 0.776 0.20 0.80
#> 20 2 0.722 0.776 0.20 0.80
#> 21 2 0.722 0.776 0.20 0.80
#> 22 2 0.722 0.776 0.20 0.80
#> 23 1 0.000 0.988 1.00 0.00
#> 24 2 0.760 0.748 0.22 0.78
#> 25 2 0.722 0.776 0.20 0.80
#> 26 1 0.943 0.405 0.64 0.36
#> 27 1 0.000 0.988 1.00 0.00
#> 28 1 0.000 0.988 1.00 0.00
#> 29 1 0.855 0.592 0.72 0.28
#> 30 2 0.722 0.776 0.20 0.80
#> 31 1 0.141 0.969 0.98 0.02
#> 32 1 0.981 0.228 0.58 0.42
#> 33 1 0.000 0.988 1.00 0.00
#> 34 1 0.827 0.632 0.74 0.26
#> 35 1 0.000 0.988 1.00 0.00
#> 36 1 0.402 0.903 0.92 0.08
#> 37 1 0.141 0.969 0.98 0.02
#> 38 1 0.000 0.988 1.00 0.00
#> 39 1 0.000 0.988 1.00 0.00
#> 40 1 0.000 0.988 1.00 0.00
#> 41 1 0.000 0.988 1.00 0.00
#> 42 1 0.000 0.988 1.00 0.00
#> 43 1 0.000 0.988 1.00 0.00
#> 44 1 0.000 0.988 1.00 0.00
#> 45 1 0.000 0.988 1.00 0.00
#> 46 1 0.000 0.988 1.00 0.00
#> 47 1 0.000 0.988 1.00 0.00
#> 48 1 0.000 0.988 1.00 0.00
#> 49 1 0.000 0.988 1.00 0.00
#> 50 1 0.000 0.988 1.00 0.00
#> 51 1 0.000 0.988 1.00 0.00
#> 52 1 0.000 0.988 1.00 0.00
#> 53 1 0.000 0.988 1.00 0.00
#> 54 1 0.000 0.988 1.00 0.00
#> 55 1 0.000 0.988 1.00 0.00
#> 56 1 0.000 0.988 1.00 0.00
#> 57 1 0.000 0.988 1.00 0.00
#> 58 1 0.000 0.988 1.00 0.00
#> 59 1 0.000 0.988 1.00 0.00
#> 60 1 0.000 0.988 1.00 0.00
#> 61 1 0.000 0.988 1.00 0.00
#> 62 1 0.000 0.988 1.00 0.00
#> 63 1 0.000 0.988 1.00 0.00
#> 64 1 0.000 0.988 1.00 0.00
#> 65 1 0.000 0.988 1.00 0.00
#> 66 1 0.000 0.988 1.00 0.00
#> 67 1 0.000 0.988 1.00 0.00
#> 68 1 0.000 0.988 1.00 0.00
#> 69 1 0.000 0.988 1.00 0.00
#> 70 1 0.000 0.988 1.00 0.00
#> 71 1 0.000 0.988 1.00 0.00
#> 72 1 0.000 0.988 1.00 0.00
#> 73 1 0.000 0.988 1.00 0.00
#> 74 1 0.000 0.988 1.00 0.00
#> 75 1 0.000 0.988 1.00 0.00
#> 76 1 0.000 0.988 1.00 0.00
#> 77 1 0.000 0.988 1.00 0.00
#> 78 1 0.000 0.988 1.00 0.00
#> 79 1 0.000 0.988 1.00 0.00
#> 80 1 0.000 0.988 1.00 0.00
#> 81 1 0.000 0.988 1.00 0.00
#> 82 1 0.000 0.988 1.00 0.00
#> 83 1 0.000 0.988 1.00 0.00
#> 84 1 0.000 0.988 1.00 0.00
#> 85 1 0.000 0.988 1.00 0.00
#> 86 1 0.000 0.988 1.00 0.00
#> 87 1 0.000 0.988 1.00 0.00
#> 88 1 0.000 0.988 1.00 0.00
#> 89 1 0.000 0.988 1.00 0.00
#> 90 1 0.000 0.988 1.00 0.00
#> 91 1 0.000 0.988 1.00 0.00
#> 92 1 0.000 0.988 1.00 0.00
#> 93 1 0.000 0.988 1.00 0.00
#> 94 1 0.000 0.988 1.00 0.00
#> 95 1 0.000 0.988 1.00 0.00
#> 96 1 0.000 0.988 1.00 0.00
#> 97 1 0.000 0.988 1.00 0.00
#> 98 1 0.000 0.988 1.00 0.00
#> 99 1 0.000 0.988 1.00 0.00
#> 100 1 0.000 0.988 1.00 0.00
#> 101 1 0.000 0.988 1.00 0.00
#> 102 1 0.000 0.988 1.00 0.00
#> 103 1 0.000 0.988 1.00 0.00
#> 104 1 0.000 0.988 1.00 0.00
#> 105 1 0.000 0.988 1.00 0.00
#> 106 1 0.000 0.988 1.00 0.00
#> 107 1 0.000 0.988 1.00 0.00
#> 108 1 0.000 0.988 1.00 0.00
#> 109 1 0.000 0.988 1.00 0.00
#> 110 1 0.000 0.988 1.00 0.00
#> 111 1 0.000 0.988 1.00 0.00
#> 112 1 0.000 0.988 1.00 0.00
#> 113 1 0.000 0.988 1.00 0.00
#> 114 1 0.000 0.988 1.00 0.00
#> 115 1 0.000 0.988 1.00 0.00
#> 116 1 0.000 0.988 1.00 0.00
#> 117 1 0.000 0.988 1.00 0.00
#> 118 1 0.000 0.988 1.00 0.00
#> 119 1 0.000 0.988 1.00 0.00
#> 120 1 0.000 0.988 1.00 0.00
#> 121 1 0.000 0.988 1.00 0.00
#> 122 1 0.000 0.988 1.00 0.00
#> 123 1 0.000 0.988 1.00 0.00
#> 124 1 0.000 0.988 1.00 0.00
#> 125 1 0.000 0.988 1.00 0.00
#> 126 1 0.000 0.988 1.00 0.00
#> 127 1 0.000 0.988 1.00 0.00
#> 128 1 0.000 0.988 1.00 0.00
#> 129 1 0.000 0.988 1.00 0.00
#> 130 1 0.000 0.988 1.00 0.00
#> 131 1 0.000 0.988 1.00 0.00
#> 132 1 0.000 0.988 1.00 0.00
#> 133 1 0.000 0.988 1.00 0.00
#> 134 1 0.000 0.988 1.00 0.00
#> 135 1 0.000 0.988 1.00 0.00
#> 136 1 0.000 0.988 1.00 0.00
#> 137 1 0.000 0.988 1.00 0.00
#> 138 1 0.000 0.988 1.00 0.00
#> 139 1 0.000 0.988 1.00 0.00
#> 140 1 0.000 0.988 1.00 0.00
#> 141 1 0.000 0.988 1.00 0.00
#> 142 1 0.000 0.988 1.00 0.00
#> 143 1 0.000 0.988 1.00 0.00
#> 144 1 0.000 0.988 1.00 0.00
#> 145 1 0.000 0.988 1.00 0.00
#> 146 1 0.000 0.988 1.00 0.00
#> 147 1 0.000 0.988 1.00 0.00
#> 148 1 0.000 0.988 1.00 0.00
#> 149 1 0.141 0.968 0.98 0.02
#> 150 1 0.000 0.988 1.00 0.00
#> 151 1 0.000 0.988 1.00 0.00
#> 152 1 0.000 0.988 1.00 0.00
#> 153 1 0.000 0.988 1.00 0.00
#> 154 1 0.000 0.988 1.00 0.00
#> 155 2 0.722 0.776 0.20 0.80
#> 156 1 0.000 0.988 1.00 0.00
#> 157 1 0.000 0.988 1.00 0.00
#> 158 1 0.000 0.988 1.00 0.00
#> 159 1 0.000 0.988 1.00 0.00
#> 160 1 0.000 0.988 1.00 0.00
#> 161 1 0.000 0.988 1.00 0.00
#> 162 1 0.000 0.988 1.00 0.00
#> 163 1 0.000 0.988 1.00 0.00
#> 164 1 0.000 0.988 1.00 0.00
#> 165 1 0.000 0.988 1.00 0.00
#> 166 1 0.000 0.988 1.00 0.00
#> 167 1 0.000 0.988 1.00 0.00
#> 168 1 0.000 0.988 1.00 0.00
#> 169 1 0.000 0.988 1.00 0.00
#> 170 1 0.000 0.988 1.00 0.00
#> 171 1 0.000 0.988 1.00 0.00
#> 172 1 0.000 0.988 1.00 0.00
#> 173 1 0.000 0.988 1.00 0.00
#> 174 1 0.000 0.988 1.00 0.00
#> 175 1 0.000 0.988 1.00 0.00
#> 176 1 0.000 0.988 1.00 0.00
#> 177 1 0.000 0.988 1.00 0.00
#> 178 1 0.000 0.988 1.00 0.00
#> 179 1 0.000 0.988 1.00 0.00
#> 180 1 0.000 0.988 1.00 0.00
#> 181 1 0.000 0.988 1.00 0.00
#> 182 1 0.000 0.988 1.00 0.00
#> 183 1 0.000 0.988 1.00 0.00
#> 184 1 0.000 0.988 1.00 0.00
#> 185 1 0.000 0.988 1.00 0.00
#> 186 1 0.000 0.988 1.00 0.00
#> 187 1 0.000 0.988 1.00 0.00
#> 188 1 0.000 0.988 1.00 0.00
#> 189 1 0.000 0.988 1.00 0.00
#> 190 1 0.000 0.988 1.00 0.00
#> 191 1 0.000 0.988 1.00 0.00
#> 192 1 0.000 0.988 1.00 0.00
#> 193 1 0.000 0.988 1.00 0.00
#> 194 1 0.000 0.988 1.00 0.00
#> 195 1 0.000 0.988 1.00 0.00
#> 196 1 0.000 0.988 1.00 0.00
#> 197 1 0.000 0.988 1.00 0.00
#> 198 1 0.000 0.988 1.00 0.00
#> 199 1 0.000 0.988 1.00 0.00
#> 200 1 0.000 0.988 1.00 0.00
#> 201 1 0.000 0.988 1.00 0.00
#> 202 1 0.000 0.988 1.00 0.00
#> 203 1 0.000 0.988 1.00 0.00
#> 204 1 0.000 0.988 1.00 0.00
#> 205 1 0.000 0.988 1.00 0.00
#> 206 1 0.000 0.988 1.00 0.00
#> 207 1 0.000 0.988 1.00 0.00
#> 208 1 0.000 0.988 1.00 0.00
#> 209 1 0.000 0.988 1.00 0.00
#> 210 1 0.000 0.988 1.00 0.00
#> 211 1 0.000 0.988 1.00 0.00
#> 212 1 0.000 0.988 1.00 0.00
#> 213 1 0.000 0.988 1.00 0.00
#> 214 1 0.000 0.988 1.00 0.00
#> 215 2 0.469 0.883 0.10 0.90
#> 216 2 0.327 0.917 0.06 0.94
#> 217 1 0.000 0.988 1.00 0.00
#> 218 2 0.000 0.965 0.00 1.00
#> 219 1 0.000 0.988 1.00 0.00
#> 220 2 0.000 0.965 0.00 1.00
#> 221 2 0.000 0.965 0.00 1.00
#> 222 2 0.242 0.934 0.04 0.96
#> 223 2 0.000 0.965 0.00 1.00
#> 224 1 0.000 0.988 1.00 0.00
#> 225 1 0.000 0.988 1.00 0.00
#> 226 1 0.000 0.988 1.00 0.00
#> 227 1 0.000 0.988 1.00 0.00
#> 228 1 0.000 0.988 1.00 0.00
#> 229 1 0.000 0.988 1.00 0.00
#> 230 1 0.000 0.988 1.00 0.00
#> 231 1 0.000 0.988 1.00 0.00
#> 232 1 0.000 0.988 1.00 0.00
#> 233 1 0.000 0.988 1.00 0.00
#> 234 1 0.000 0.988 1.00 0.00
#> 235 1 0.000 0.988 1.00 0.00
#> 236 1 0.000 0.988 1.00 0.00
#> 237 2 0.000 0.965 0.00 1.00
#> 238 2 0.722 0.776 0.20 0.80
#> 239 2 0.881 0.611 0.30 0.70
#> 240 1 0.760 0.704 0.78 0.22
#> 241 1 0.000 0.988 1.00 0.00
#> 242 2 0.141 0.950 0.02 0.98
#> 243 2 0.000 0.965 0.00 1.00
#> 244 2 0.000 0.965 0.00 1.00
#> 245 1 0.000 0.988 1.00 0.00
#> 246 2 0.000 0.965 0.00 1.00
#> 247 2 0.000 0.965 0.00 1.00
#> 248 2 0.000 0.965 0.00 1.00
#> 249 1 0.855 0.598 0.72 0.28
#> 250 2 0.000 0.965 0.00 1.00
#> 251 2 0.000 0.965 0.00 1.00
#> 252 2 0.000 0.965 0.00 1.00
#> 253 2 0.000 0.965 0.00 1.00
#> 254 2 0.000 0.965 0.00 1.00
#> 255 2 0.402 0.898 0.08 0.92
#> 256 2 0.000 0.965 0.00 1.00
#> 257 2 0.000 0.965 0.00 1.00
#> 258 2 0.000 0.965 0.00 1.00
#> 259 2 0.904 0.576 0.32 0.68
#> 260 2 0.000 0.965 0.00 1.00
#> 261 2 0.000 0.965 0.00 1.00
#> 262 2 0.000 0.965 0.00 1.00
#> 263 2 0.000 0.965 0.00 1.00
#> 264 2 0.000 0.965 0.00 1.00
#> 265 2 0.000 0.965 0.00 1.00
#> 266 2 0.242 0.935 0.04 0.96
#> 267 2 0.000 0.965 0.00 1.00
#> 268 2 0.000 0.965 0.00 1.00
#> 269 2 0.000 0.965 0.00 1.00
#> 270 2 0.000 0.965 0.00 1.00
#> 271 2 0.000 0.965 0.00 1.00
#> 272 1 0.000 0.988 1.00 0.00
#> 273 2 0.000 0.965 0.00 1.00
#> 274 2 0.000 0.965 0.00 1.00
#> 275 2 0.000 0.965 0.00 1.00
#> 276 2 0.000 0.965 0.00 1.00
#> 277 2 0.000 0.965 0.00 1.00
#> 278 2 0.000 0.965 0.00 1.00
#> 279 2 0.000 0.965 0.00 1.00
#> 280 2 0.000 0.965 0.00 1.00
#> 281 1 0.000 0.988 1.00 0.00
#> 282 1 0.000 0.988 1.00 0.00
#> 283 2 0.000 0.965 0.00 1.00
#> 284 2 0.000 0.965 0.00 1.00
#> 285 2 0.000 0.965 0.00 1.00
#> 286 2 0.000 0.965 0.00 1.00
#> 287 1 0.584 0.827 0.86 0.14
#> 288 2 0.000 0.965 0.00 1.00
#> 289 2 0.000 0.965 0.00 1.00
#> 290 2 0.000 0.965 0.00 1.00
#> 291 2 0.000 0.965 0.00 1.00
#> 292 2 0.000 0.965 0.00 1.00
#> 293 2 0.000 0.965 0.00 1.00
#> 294 2 0.000 0.965 0.00 1.00
#> 295 2 0.000 0.965 0.00 1.00
#> 296 2 0.000 0.965 0.00 1.00
#> 297 2 0.000 0.965 0.00 1.00
#> 298 2 0.000 0.965 0.00 1.00
#> 299 2 0.000 0.965 0.00 1.00
#> 300 2 0.000 0.965 0.00 1.00
#> 301 2 0.000 0.965 0.00 1.00
#> 302 2 0.000 0.965 0.00 1.00
#> 303 2 0.000 0.965 0.00 1.00
#> 304 2 0.000 0.965 0.00 1.00
#> 305 2 0.000 0.965 0.00 1.00
#> 306 2 0.000 0.965 0.00 1.00
#> 307 2 0.000 0.965 0.00 1.00
#> 308 2 0.000 0.965 0.00 1.00
#> 309 2 0.000 0.965 0.00 1.00
#> 310 2 0.000 0.965 0.00 1.00
#> 311 2 0.000 0.965 0.00 1.00
#> 312 1 0.000 0.988 1.00 0.00
#> 313 2 0.000 0.965 0.00 1.00
#> 314 2 0.000 0.965 0.00 1.00
#> 315 2 0.000 0.965 0.00 1.00
#> 316 2 0.000 0.965 0.00 1.00
#> 317 2 0.000 0.965 0.00 1.00
#> 318 2 0.000 0.965 0.00 1.00
#> 319 2 0.000 0.965 0.00 1.00
#> 320 2 0.000 0.965 0.00 1.00
#> 321 2 0.000 0.965 0.00 1.00
#> 322 2 0.000 0.965 0.00 1.00
#> 323 2 0.000 0.965 0.00 1.00
#> 324 2 0.000 0.965 0.00 1.00
#> 325 2 0.000 0.965 0.00 1.00
#> 326 2 0.000 0.965 0.00 1.00
#> 327 2 0.469 0.877 0.10 0.90
#> 328 2 0.000 0.965 0.00 1.00
#> 329 2 0.000 0.965 0.00 1.00
#> 330 2 0.000 0.965 0.00 1.00
#> 331 2 0.000 0.965 0.00 1.00
#> 332 2 0.000 0.965 0.00 1.00
#> 333 2 0.000 0.965 0.00 1.00
#> 334 2 0.000 0.965 0.00 1.00
#> 335 2 0.000 0.965 0.00 1.00
#> 336 2 0.000 0.965 0.00 1.00
#> 337 2 0.000 0.965 0.00 1.00
#> 338 2 0.000 0.965 0.00 1.00
#> 339 2 0.000 0.965 0.00 1.00
#> 340 2 0.000 0.965 0.00 1.00
#> 341 2 0.000 0.965 0.00 1.00
#> 342 2 0.000 0.965 0.00 1.00
#> 343 2 0.000 0.965 0.00 1.00
#> 344 2 0.000 0.965 0.00 1.00
#> 345 2 0.000 0.965 0.00 1.00
#> 346 2 0.000 0.965 0.00 1.00
#> 347 2 0.000 0.965 0.00 1.00
#> 348 2 0.000 0.965 0.00 1.00
#> 349 2 0.000 0.965 0.00 1.00
#> 350 2 0.000 0.965 0.00 1.00
#> 351 1 0.904 0.530 0.68 0.32
#> 352 2 0.000 0.965 0.00 1.00
#> 353 2 0.000 0.965 0.00 1.00
#> 354 2 0.000 0.965 0.00 1.00
#> 355 2 0.000 0.965 0.00 1.00
#> 356 2 0.000 0.965 0.00 1.00
#> 357 2 0.000 0.965 0.00 1.00
#> 358 2 0.000 0.965 0.00 1.00
#> 359 2 0.000 0.965 0.00 1.00
#> 360 2 0.000 0.965 0.00 1.00
#> 361 2 0.000 0.965 0.00 1.00
#> 362 2 0.000 0.965 0.00 1.00
#> 363 2 0.000 0.965 0.00 1.00
#> 364 2 0.000 0.965 0.00 1.00
#> 365 2 0.000 0.965 0.00 1.00
#> 366 2 0.000 0.965 0.00 1.00
#> 367 2 0.000 0.965 0.00 1.00
#> 368 2 0.000 0.965 0.00 1.00
#> 369 2 0.000 0.965 0.00 1.00
#> 370 2 0.000 0.965 0.00 1.00
#> 371 2 0.000 0.965 0.00 1.00
#> 372 2 0.000 0.965 0.00 1.00
#> 373 2 0.000 0.965 0.00 1.00
#> 374 2 0.000 0.965 0.00 1.00
#> 375 2 0.000 0.965 0.00 1.00
#> 376 2 0.000 0.965 0.00 1.00
#> 377 2 0.000 0.965 0.00 1.00
#> 378 2 0.000 0.965 0.00 1.00
#> 379 2 0.000 0.965 0.00 1.00
#> 380 2 0.000 0.965 0.00 1.00
#> 381 2 0.000 0.965 0.00 1.00
#> 382 2 0.000 0.965 0.00 1.00
#> 383 2 0.000 0.965 0.00 1.00
#> 384 2 0.000 0.965 0.00 1.00
#> 385 2 0.000 0.965 0.00 1.00
#> 386 2 0.000 0.965 0.00 1.00
#> 387 2 0.000 0.965 0.00 1.00
#> 388 2 0.000 0.965 0.00 1.00
#> 389 2 0.000 0.965 0.00 1.00
#> 390 2 0.000 0.965 0.00 1.00
#> 391 2 0.925 0.496 0.34 0.66
#> 392 2 0.000 0.965 0.00 1.00
#> 393 2 0.000 0.965 0.00 1.00
#> 394 1 0.000 0.988 1.00 0.00
#> 395 2 0.000 0.965 0.00 1.00
#> 396 2 0.000 0.965 0.00 1.00
#> 397 2 0.000 0.965 0.00 1.00
#> 398 2 0.000 0.965 0.00 1.00
#> 399 2 0.000 0.965 0.00 1.00
#> 400 2 0.000 0.965 0.00 1.00
#> 401 2 0.000 0.965 0.00 1.00
#> 402 2 0.000 0.965 0.00 1.00
#> 403 2 0.000 0.965 0.00 1.00
#> 404 2 0.000 0.965 0.00 1.00
#> 405 2 0.000 0.965 0.00 1.00
#> 406 2 0.000 0.965 0.00 1.00
#> 407 2 0.000 0.965 0.00 1.00
#> 408 2 0.000 0.965 0.00 1.00
#> 409 2 0.000 0.965 0.00 1.00
#> 410 2 0.000 0.965 0.00 1.00
#> 411 2 0.000 0.965 0.00 1.00
#> 412 2 0.000 0.965 0.00 1.00
#> 413 2 0.000 0.965 0.00 1.00
#> 414 2 0.000 0.965 0.00 1.00
#> 415 2 0.000 0.965 0.00 1.00
#> 416 2 0.000 0.965 0.00 1.00
#> 417 2 0.000 0.965 0.00 1.00
#> 418 2 0.000 0.965 0.00 1.00
#> 419 2 0.000 0.965 0.00 1.00
#> 420 2 0.000 0.965 0.00 1.00
#> 421 2 0.000 0.965 0.00 1.00
#> 422 2 0.000 0.965 0.00 1.00
#> 423 2 0.000 0.965 0.00 1.00
#> 424 2 0.000 0.965 0.00 1.00
#> 425 2 0.000 0.965 0.00 1.00
#> 426 2 0.000 0.965 0.00 1.00
#> 427 2 0.000 0.965 0.00 1.00
#> 428 2 0.000 0.965 0.00 1.00
#> 429 2 0.000 0.965 0.00 1.00
#> 430 2 0.000 0.965 0.00 1.00
#> 431 2 0.000 0.965 0.00 1.00
#> 432 2 0.000 0.965 0.00 1.00
#> 433 2 0.000 0.965 0.00 1.00
#> 434 2 0.000 0.965 0.00 1.00
#> 435 2 0.000 0.965 0.00 1.00
#> 436 2 0.000 0.965 0.00 1.00
#> 437 2 0.000 0.965 0.00 1.00
#> 438 2 0.000 0.965 0.00 1.00
#> 439 2 0.000 0.965 0.00 1.00
#> 440 1 0.141 0.969 0.98 0.02
#> 441 1 0.000 0.988 1.00 0.00
#> 442 2 0.242 0.935 0.04 0.96
#> 443 2 0.000 0.965 0.00 1.00
#> 444 2 0.795 0.718 0.24 0.76
#> 445 2 0.904 0.576 0.32 0.68
#> 446 2 0.990 0.275 0.44 0.56
#> 447 1 0.000 0.988 1.00 0.00
#> 448 1 0.141 0.969 0.98 0.02
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.994 0.00 0.00 1.00
#> 2 3 0.0000 0.994 0.00 0.00 1.00
#> 3 3 0.0000 0.994 0.00 0.00 1.00
#> 4 3 0.0000 0.994 0.00 0.00 1.00
#> 5 3 0.0000 0.994 0.00 0.00 1.00
#> 6 3 0.0000 0.994 0.00 0.00 1.00
#> 7 3 0.0000 0.994 0.00 0.00 1.00
#> 8 3 0.0000 0.994 0.00 0.00 1.00
#> 9 3 0.0000 0.994 0.00 0.00 1.00
#> 10 3 0.0000 0.994 0.00 0.00 1.00
#> 11 3 0.0000 0.994 0.00 0.00 1.00
#> 12 3 0.0000 0.994 0.00 0.00 1.00
#> 13 3 0.0000 0.994 0.00 0.00 1.00
#> 14 3 0.0000 0.994 0.00 0.00 1.00
#> 15 3 0.0000 0.994 0.00 0.00 1.00
#> 16 3 0.0000 0.994 0.00 0.00 1.00
#> 17 3 0.0000 0.994 0.00 0.00 1.00
#> 18 3 0.0000 0.994 0.00 0.00 1.00
#> 19 3 0.0000 0.994 0.00 0.00 1.00
#> 20 3 0.0000 0.994 0.00 0.00 1.00
#> 21 3 0.0000 0.994 0.00 0.00 1.00
#> 22 3 0.0000 0.994 0.00 0.00 1.00
#> 23 3 0.0000 0.994 0.00 0.00 1.00
#> 24 3 0.0000 0.994 0.00 0.00 1.00
#> 25 3 0.0000 0.994 0.00 0.00 1.00
#> 26 3 0.0000 0.994 0.00 0.00 1.00
#> 27 3 0.0000 0.994 0.00 0.00 1.00
#> 28 3 0.0000 0.994 0.00 0.00 1.00
#> 29 3 0.0000 0.994 0.00 0.00 1.00
#> 30 3 0.0000 0.994 0.00 0.00 1.00
#> 31 3 0.0000 0.994 0.00 0.00 1.00
#> 32 3 0.0000 0.994 0.00 0.00 1.00
#> 33 3 0.0000 0.994 0.00 0.00 1.00
#> 34 3 0.0000 0.994 0.00 0.00 1.00
#> 35 3 0.0000 0.994 0.00 0.00 1.00
#> 36 3 0.0000 0.994 0.00 0.00 1.00
#> 37 3 0.0000 0.994 0.00 0.00 1.00
#> 38 1 0.0000 0.996 1.00 0.00 0.00
#> 39 1 0.0000 0.996 1.00 0.00 0.00
#> 40 1 0.0000 0.996 1.00 0.00 0.00
#> 41 1 0.0000 0.996 1.00 0.00 0.00
#> 42 1 0.0000 0.996 1.00 0.00 0.00
#> 43 1 0.0000 0.996 1.00 0.00 0.00
#> 44 1 0.0000 0.996 1.00 0.00 0.00
#> 45 1 0.0000 0.996 1.00 0.00 0.00
#> 46 1 0.0000 0.996 1.00 0.00 0.00
#> 47 1 0.0000 0.996 1.00 0.00 0.00
#> 48 1 0.0000 0.996 1.00 0.00 0.00
#> 49 1 0.0000 0.996 1.00 0.00 0.00
#> 50 1 0.0000 0.996 1.00 0.00 0.00
#> 51 1 0.0000 0.996 1.00 0.00 0.00
#> 52 1 0.0000 0.996 1.00 0.00 0.00
#> 53 1 0.0000 0.996 1.00 0.00 0.00
#> 54 1 0.0000 0.996 1.00 0.00 0.00
#> 55 1 0.0000 0.996 1.00 0.00 0.00
#> 56 1 0.0000 0.996 1.00 0.00 0.00
#> 57 1 0.0000 0.996 1.00 0.00 0.00
#> 58 1 0.0000 0.996 1.00 0.00 0.00
#> 59 1 0.0000 0.996 1.00 0.00 0.00
#> 60 1 0.0000 0.996 1.00 0.00 0.00
#> 61 1 0.0000 0.996 1.00 0.00 0.00
#> 62 1 0.0000 0.996 1.00 0.00 0.00
#> 63 1 0.0000 0.996 1.00 0.00 0.00
#> 64 1 0.0000 0.996 1.00 0.00 0.00
#> 65 1 0.0000 0.996 1.00 0.00 0.00
#> 66 1 0.0000 0.996 1.00 0.00 0.00
#> 67 1 0.0000 0.996 1.00 0.00 0.00
#> 68 1 0.0000 0.996 1.00 0.00 0.00
#> 69 1 0.0000 0.996 1.00 0.00 0.00
#> 70 1 0.0000 0.996 1.00 0.00 0.00
#> 71 1 0.0000 0.996 1.00 0.00 0.00
#> 72 1 0.0000 0.996 1.00 0.00 0.00
#> 73 1 0.0000 0.996 1.00 0.00 0.00
#> 74 1 0.0000 0.996 1.00 0.00 0.00
#> 75 1 0.0000 0.996 1.00 0.00 0.00
#> 76 1 0.0000 0.996 1.00 0.00 0.00
#> 77 1 0.0000 0.996 1.00 0.00 0.00
#> 78 1 0.0000 0.996 1.00 0.00 0.00
#> 79 1 0.0000 0.996 1.00 0.00 0.00
#> 80 1 0.0000 0.996 1.00 0.00 0.00
#> 81 1 0.0000 0.996 1.00 0.00 0.00
#> 82 1 0.0000 0.996 1.00 0.00 0.00
#> 83 1 0.0000 0.996 1.00 0.00 0.00
#> 84 1 0.0000 0.996 1.00 0.00 0.00
#> 85 1 0.0000 0.996 1.00 0.00 0.00
#> 86 1 0.0000 0.996 1.00 0.00 0.00
#> 87 1 0.0000 0.996 1.00 0.00 0.00
#> 88 1 0.0000 0.996 1.00 0.00 0.00
#> 89 1 0.0000 0.996 1.00 0.00 0.00
#> 90 1 0.0000 0.996 1.00 0.00 0.00
#> 91 1 0.0000 0.996 1.00 0.00 0.00
#> 92 1 0.0000 0.996 1.00 0.00 0.00
#> 93 1 0.0000 0.996 1.00 0.00 0.00
#> 94 1 0.0000 0.996 1.00 0.00 0.00
#> 95 1 0.0000 0.996 1.00 0.00 0.00
#> 96 1 0.0000 0.996 1.00 0.00 0.00
#> 97 1 0.0000 0.996 1.00 0.00 0.00
#> 98 1 0.0000 0.996 1.00 0.00 0.00
#> 99 1 0.0000 0.996 1.00 0.00 0.00
#> 100 1 0.0000 0.996 1.00 0.00 0.00
#> 101 1 0.0000 0.996 1.00 0.00 0.00
#> 102 1 0.0000 0.996 1.00 0.00 0.00
#> 103 1 0.0000 0.996 1.00 0.00 0.00
#> 104 1 0.0000 0.996 1.00 0.00 0.00
#> 105 1 0.0000 0.996 1.00 0.00 0.00
#> 106 1 0.0000 0.996 1.00 0.00 0.00
#> 107 1 0.0000 0.996 1.00 0.00 0.00
#> 108 1 0.0000 0.996 1.00 0.00 0.00
#> 109 1 0.0000 0.996 1.00 0.00 0.00
#> 110 1 0.0000 0.996 1.00 0.00 0.00
#> 111 1 0.0000 0.996 1.00 0.00 0.00
#> 112 1 0.0000 0.996 1.00 0.00 0.00
#> 113 1 0.0000 0.996 1.00 0.00 0.00
#> 114 1 0.0000 0.996 1.00 0.00 0.00
#> 115 1 0.0000 0.996 1.00 0.00 0.00
#> 116 1 0.0000 0.996 1.00 0.00 0.00
#> 117 1 0.0000 0.996 1.00 0.00 0.00
#> 118 1 0.0000 0.996 1.00 0.00 0.00
#> 119 1 0.0000 0.996 1.00 0.00 0.00
#> 120 1 0.0000 0.996 1.00 0.00 0.00
#> 121 1 0.0000 0.996 1.00 0.00 0.00
#> 122 1 0.0000 0.996 1.00 0.00 0.00
#> 123 1 0.0000 0.996 1.00 0.00 0.00
#> 124 1 0.0000 0.996 1.00 0.00 0.00
#> 125 1 0.0000 0.996 1.00 0.00 0.00
#> 126 1 0.0000 0.996 1.00 0.00 0.00
#> 127 1 0.0000 0.996 1.00 0.00 0.00
#> 128 1 0.0000 0.996 1.00 0.00 0.00
#> 129 1 0.0000 0.996 1.00 0.00 0.00
#> 130 1 0.0000 0.996 1.00 0.00 0.00
#> 131 1 0.0000 0.996 1.00 0.00 0.00
#> 132 1 0.0000 0.996 1.00 0.00 0.00
#> 133 1 0.0000 0.996 1.00 0.00 0.00
#> 134 1 0.0000 0.996 1.00 0.00 0.00
#> 135 1 0.0000 0.996 1.00 0.00 0.00
#> 136 1 0.0000 0.996 1.00 0.00 0.00
#> 137 1 0.0000 0.996 1.00 0.00 0.00
#> 138 1 0.0000 0.996 1.00 0.00 0.00
#> 139 1 0.0000 0.996 1.00 0.00 0.00
#> 140 1 0.0000 0.996 1.00 0.00 0.00
#> 141 1 0.0000 0.996 1.00 0.00 0.00
#> 142 1 0.0000 0.996 1.00 0.00 0.00
#> 143 1 0.0000 0.996 1.00 0.00 0.00
#> 144 1 0.0000 0.996 1.00 0.00 0.00
#> 145 1 0.0000 0.996 1.00 0.00 0.00
#> 146 1 0.0000 0.996 1.00 0.00 0.00
#> 147 1 0.0000 0.996 1.00 0.00 0.00
#> 148 1 0.0000 0.996 1.00 0.00 0.00
#> 149 3 0.0000 0.994 0.00 0.00 1.00
#> 150 1 0.0000 0.996 1.00 0.00 0.00
#> 151 1 0.0000 0.996 1.00 0.00 0.00
#> 152 1 0.0000 0.996 1.00 0.00 0.00
#> 153 1 0.0000 0.996 1.00 0.00 0.00
#> 154 3 0.0000 0.994 0.00 0.00 1.00
#> 155 3 0.0000 0.994 0.00 0.00 1.00
#> 156 1 0.0000 0.996 1.00 0.00 0.00
#> 157 1 0.0000 0.996 1.00 0.00 0.00
#> 158 1 0.0000 0.996 1.00 0.00 0.00
#> 159 1 0.0000 0.996 1.00 0.00 0.00
#> 160 1 0.0000 0.996 1.00 0.00 0.00
#> 161 1 0.0000 0.996 1.00 0.00 0.00
#> 162 3 0.0000 0.994 0.00 0.00 1.00
#> 163 1 0.0000 0.996 1.00 0.00 0.00
#> 164 1 0.0000 0.996 1.00 0.00 0.00
#> 165 1 0.0000 0.996 1.00 0.00 0.00
#> 166 1 0.0000 0.996 1.00 0.00 0.00
#> 167 3 0.0000 0.994 0.00 0.00 1.00
#> 168 1 0.0000 0.996 1.00 0.00 0.00
#> 169 1 0.0000 0.996 1.00 0.00 0.00
#> 170 1 0.0000 0.996 1.00 0.00 0.00
#> 171 1 0.0000 0.996 1.00 0.00 0.00
#> 172 1 0.0000 0.996 1.00 0.00 0.00
#> 173 1 0.0000 0.996 1.00 0.00 0.00
#> 174 1 0.0000 0.996 1.00 0.00 0.00
#> 175 1 0.0000 0.996 1.00 0.00 0.00
#> 176 1 0.0000 0.996 1.00 0.00 0.00
#> 177 1 0.0000 0.996 1.00 0.00 0.00
#> 178 1 0.0000 0.996 1.00 0.00 0.00
#> 179 1 0.0000 0.996 1.00 0.00 0.00
#> 180 1 0.0000 0.996 1.00 0.00 0.00
#> 181 1 0.0000 0.996 1.00 0.00 0.00
#> 182 1 0.0000 0.996 1.00 0.00 0.00
#> 183 1 0.0000 0.996 1.00 0.00 0.00
#> 184 3 0.0000 0.994 0.00 0.00 1.00
#> 185 1 0.0000 0.996 1.00 0.00 0.00
#> 186 1 0.0000 0.996 1.00 0.00 0.00
#> 187 1 0.0000 0.996 1.00 0.00 0.00
#> 188 1 0.0000 0.996 1.00 0.00 0.00
#> 189 1 0.3340 0.863 0.88 0.00 0.12
#> 190 1 0.0000 0.996 1.00 0.00 0.00
#> 191 1 0.0000 0.996 1.00 0.00 0.00
#> 192 1 0.0000 0.996 1.00 0.00 0.00
#> 193 1 0.0000 0.996 1.00 0.00 0.00
#> 194 1 0.0000 0.996 1.00 0.00 0.00
#> 195 3 0.0000 0.994 0.00 0.00 1.00
#> 196 1 0.0000 0.996 1.00 0.00 0.00
#> 197 1 0.0000 0.996 1.00 0.00 0.00
#> 198 1 0.0000 0.996 1.00 0.00 0.00
#> 199 1 0.0000 0.996 1.00 0.00 0.00
#> 200 1 0.0892 0.976 0.98 0.00 0.02
#> 201 1 0.0000 0.996 1.00 0.00 0.00
#> 202 3 0.0000 0.994 0.00 0.00 1.00
#> 203 1 0.0000 0.996 1.00 0.00 0.00
#> 204 1 0.0000 0.996 1.00 0.00 0.00
#> 205 1 0.0000 0.996 1.00 0.00 0.00
#> 206 1 0.0000 0.996 1.00 0.00 0.00
#> 207 1 0.0000 0.996 1.00 0.00 0.00
#> 208 1 0.0000 0.996 1.00 0.00 0.00
#> 209 1 0.0000 0.996 1.00 0.00 0.00
#> 210 1 0.0000 0.996 1.00 0.00 0.00
#> 211 1 0.2537 0.911 0.92 0.00 0.08
#> 212 1 0.0000 0.996 1.00 0.00 0.00
#> 213 1 0.0000 0.996 1.00 0.00 0.00
#> 214 1 0.0000 0.996 1.00 0.00 0.00
#> 215 3 0.0000 0.994 0.00 0.00 1.00
#> 216 2 0.4291 0.780 0.18 0.82 0.00
#> 217 1 0.0000 0.996 1.00 0.00 0.00
#> 218 2 0.0000 0.992 0.00 1.00 0.00
#> 219 1 0.0000 0.996 1.00 0.00 0.00
#> 220 3 0.0000 0.994 0.00 0.00 1.00
#> 221 2 0.5397 0.613 0.00 0.72 0.28
#> 222 3 0.0000 0.994 0.00 0.00 1.00
#> 223 2 0.0000 0.992 0.00 1.00 0.00
#> 224 3 0.0000 0.994 0.00 0.00 1.00
#> 225 3 0.0892 0.974 0.02 0.00 0.98
#> 226 3 0.0000 0.994 0.00 0.00 1.00
#> 227 3 0.0000 0.994 0.00 0.00 1.00
#> 228 3 0.0000 0.994 0.00 0.00 1.00
#> 229 3 0.0000 0.994 0.00 0.00 1.00
#> 230 3 0.0000 0.994 0.00 0.00 1.00
#> 231 3 0.0000 0.994 0.00 0.00 1.00
#> 232 3 0.0000 0.994 0.00 0.00 1.00
#> 233 3 0.0000 0.994 0.00 0.00 1.00
#> 234 3 0.0000 0.994 0.00 0.00 1.00
#> 235 3 0.0000 0.994 0.00 0.00 1.00
#> 236 3 0.0000 0.994 0.00 0.00 1.00
#> 237 2 0.0000 0.992 0.00 1.00 0.00
#> 238 3 0.0000 0.994 0.00 0.00 1.00
#> 239 3 0.0000 0.994 0.00 0.00 1.00
#> 240 3 0.0000 0.994 0.00 0.00 1.00
#> 241 3 0.0000 0.994 0.00 0.00 1.00
#> 242 3 0.0000 0.994 0.00 0.00 1.00
#> 243 3 0.0000 0.994 0.00 0.00 1.00
#> 244 3 0.0000 0.994 0.00 0.00 1.00
#> 245 3 0.0000 0.994 0.00 0.00 1.00
#> 246 3 0.0000 0.994 0.00 0.00 1.00
#> 247 3 0.0000 0.994 0.00 0.00 1.00
#> 248 3 0.0000 0.994 0.00 0.00 1.00
#> 249 3 0.0000 0.994 0.00 0.00 1.00
#> 250 3 0.0000 0.994 0.00 0.00 1.00
#> 251 3 0.0000 0.994 0.00 0.00 1.00
#> 252 3 0.0000 0.994 0.00 0.00 1.00
#> 253 3 0.0000 0.994 0.00 0.00 1.00
#> 254 3 0.0000 0.994 0.00 0.00 1.00
#> 255 3 0.1529 0.952 0.04 0.00 0.96
#> 256 3 0.0000 0.994 0.00 0.00 1.00
#> 257 3 0.0000 0.994 0.00 0.00 1.00
#> 258 3 0.0000 0.994 0.00 0.00 1.00
#> 259 3 0.0000 0.994 0.00 0.00 1.00
#> 260 3 0.0000 0.994 0.00 0.00 1.00
#> 261 3 0.0000 0.994 0.00 0.00 1.00
#> 262 3 0.0000 0.994 0.00 0.00 1.00
#> 263 3 0.0000 0.994 0.00 0.00 1.00
#> 264 3 0.0000 0.994 0.00 0.00 1.00
#> 265 3 0.0000 0.994 0.00 0.00 1.00
#> 266 3 0.0000 0.994 0.00 0.00 1.00
#> 267 3 0.0000 0.994 0.00 0.00 1.00
#> 268 3 0.0000 0.994 0.00 0.00 1.00
#> 269 3 0.5948 0.435 0.00 0.36 0.64
#> 270 3 0.0000 0.994 0.00 0.00 1.00
#> 271 3 0.0000 0.994 0.00 0.00 1.00
#> 272 3 0.0000 0.994 0.00 0.00 1.00
#> 273 3 0.0000 0.994 0.00 0.00 1.00
#> 274 3 0.0000 0.994 0.00 0.00 1.00
#> 275 3 0.0000 0.994 0.00 0.00 1.00
#> 276 3 0.0000 0.994 0.00 0.00 1.00
#> 277 3 0.0000 0.994 0.00 0.00 1.00
#> 278 3 0.0000 0.994 0.00 0.00 1.00
#> 279 2 0.2959 0.888 0.00 0.90 0.10
#> 280 3 0.0000 0.994 0.00 0.00 1.00
#> 281 1 0.0000 0.996 1.00 0.00 0.00
#> 282 1 0.0000 0.996 1.00 0.00 0.00
#> 283 3 0.0000 0.994 0.00 0.00 1.00
#> 284 2 0.0000 0.992 0.00 1.00 0.00
#> 285 3 0.0000 0.994 0.00 0.00 1.00
#> 286 3 0.0000 0.994 0.00 0.00 1.00
#> 287 1 0.0000 0.996 1.00 0.00 0.00
#> 288 3 0.0000 0.994 0.00 0.00 1.00
#> 289 3 0.0000 0.994 0.00 0.00 1.00
#> 290 3 0.0000 0.994 0.00 0.00 1.00
#> 291 3 0.0000 0.994 0.00 0.00 1.00
#> 292 3 0.0000 0.994 0.00 0.00 1.00
#> 293 3 0.0000 0.994 0.00 0.00 1.00
#> 294 3 0.0000 0.994 0.00 0.00 1.00
#> 295 3 0.0000 0.994 0.00 0.00 1.00
#> 296 2 0.0892 0.974 0.00 0.98 0.02
#> 297 2 0.0000 0.992 0.00 1.00 0.00
#> 298 3 0.0000 0.994 0.00 0.00 1.00
#> 299 3 0.0000 0.994 0.00 0.00 1.00
#> 300 3 0.0000 0.994 0.00 0.00 1.00
#> 301 3 0.0000 0.994 0.00 0.00 1.00
#> 302 2 0.0000 0.992 0.00 1.00 0.00
#> 303 3 0.0000 0.994 0.00 0.00 1.00
#> 304 3 0.0892 0.976 0.00 0.02 0.98
#> 305 3 0.1529 0.956 0.00 0.04 0.96
#> 306 3 0.0000 0.994 0.00 0.00 1.00
#> 307 3 0.0000 0.994 0.00 0.00 1.00
#> 308 3 0.0000 0.994 0.00 0.00 1.00
#> 309 3 0.0000 0.994 0.00 0.00 1.00
#> 310 3 0.0000 0.994 0.00 0.00 1.00
#> 311 3 0.0000 0.994 0.00 0.00 1.00
#> 312 1 0.6244 0.214 0.56 0.00 0.44
#> 313 3 0.0000 0.994 0.00 0.00 1.00
#> 314 2 0.0000 0.992 0.00 1.00 0.00
#> 315 2 0.0000 0.992 0.00 1.00 0.00
#> 316 2 0.0000 0.992 0.00 1.00 0.00
#> 317 2 0.2066 0.933 0.00 0.94 0.06
#> 318 2 0.0000 0.992 0.00 1.00 0.00
#> 319 2 0.0000 0.992 0.00 1.00 0.00
#> 320 2 0.0000 0.992 0.00 1.00 0.00
#> 321 2 0.0000 0.992 0.00 1.00 0.00
#> 322 2 0.0000 0.992 0.00 1.00 0.00
#> 323 2 0.0000 0.992 0.00 1.00 0.00
#> 324 2 0.0000 0.992 0.00 1.00 0.00
#> 325 2 0.0000 0.992 0.00 1.00 0.00
#> 326 2 0.0000 0.992 0.00 1.00 0.00
#> 327 2 0.0000 0.992 0.00 1.00 0.00
#> 328 2 0.0000 0.992 0.00 1.00 0.00
#> 329 2 0.0000 0.992 0.00 1.00 0.00
#> 330 2 0.0000 0.992 0.00 1.00 0.00
#> 331 2 0.0000 0.992 0.00 1.00 0.00
#> 332 2 0.0000 0.992 0.00 1.00 0.00
#> 333 2 0.0000 0.992 0.00 1.00 0.00
#> 334 2 0.0000 0.992 0.00 1.00 0.00
#> 335 2 0.0000 0.992 0.00 1.00 0.00
#> 336 2 0.0000 0.992 0.00 1.00 0.00
#> 337 2 0.0000 0.992 0.00 1.00 0.00
#> 338 2 0.0000 0.992 0.00 1.00 0.00
#> 339 2 0.0000 0.992 0.00 1.00 0.00
#> 340 2 0.0000 0.992 0.00 1.00 0.00
#> 341 2 0.0000 0.992 0.00 1.00 0.00
#> 342 2 0.0000 0.992 0.00 1.00 0.00
#> 343 2 0.0000 0.992 0.00 1.00 0.00
#> 344 2 0.0000 0.992 0.00 1.00 0.00
#> 345 2 0.0000 0.992 0.00 1.00 0.00
#> 346 2 0.0000 0.992 0.00 1.00 0.00
#> 347 2 0.0000 0.992 0.00 1.00 0.00
#> 348 2 0.0000 0.992 0.00 1.00 0.00
#> 349 2 0.0000 0.992 0.00 1.00 0.00
#> 350 2 0.0000 0.992 0.00 1.00 0.00
#> 351 2 0.0892 0.973 0.02 0.98 0.00
#> 352 2 0.0000 0.992 0.00 1.00 0.00
#> 353 2 0.0000 0.992 0.00 1.00 0.00
#> 354 2 0.0000 0.992 0.00 1.00 0.00
#> 355 2 0.0000 0.992 0.00 1.00 0.00
#> 356 2 0.0000 0.992 0.00 1.00 0.00
#> 357 2 0.0000 0.992 0.00 1.00 0.00
#> 358 2 0.0000 0.992 0.00 1.00 0.00
#> 359 2 0.0000 0.992 0.00 1.00 0.00
#> 360 2 0.0000 0.992 0.00 1.00 0.00
#> 361 2 0.0000 0.992 0.00 1.00 0.00
#> 362 2 0.0000 0.992 0.00 1.00 0.00
#> 363 2 0.0000 0.992 0.00 1.00 0.00
#> 364 2 0.0000 0.992 0.00 1.00 0.00
#> 365 2 0.0000 0.992 0.00 1.00 0.00
#> 366 2 0.0000 0.992 0.00 1.00 0.00
#> 367 2 0.0000 0.992 0.00 1.00 0.00
#> 368 2 0.0000 0.992 0.00 1.00 0.00
#> 369 2 0.0000 0.992 0.00 1.00 0.00
#> 370 2 0.0000 0.992 0.00 1.00 0.00
#> 371 2 0.0000 0.992 0.00 1.00 0.00
#> 372 2 0.0000 0.992 0.00 1.00 0.00
#> 373 2 0.0000 0.992 0.00 1.00 0.00
#> 374 2 0.0000 0.992 0.00 1.00 0.00
#> 375 2 0.0000 0.992 0.00 1.00 0.00
#> 376 2 0.0000 0.992 0.00 1.00 0.00
#> 377 2 0.0000 0.992 0.00 1.00 0.00
#> 378 2 0.0000 0.992 0.00 1.00 0.00
#> 379 2 0.0000 0.992 0.00 1.00 0.00
#> 380 2 0.0000 0.992 0.00 1.00 0.00
#> 381 3 0.1529 0.955 0.00 0.04 0.96
#> 382 3 0.3686 0.837 0.00 0.14 0.86
#> 383 2 0.0000 0.992 0.00 1.00 0.00
#> 384 3 0.0000 0.994 0.00 0.00 1.00
#> 385 3 0.0000 0.994 0.00 0.00 1.00
#> 386 3 0.2959 0.888 0.00 0.10 0.90
#> 387 2 0.0000 0.992 0.00 1.00 0.00
#> 388 2 0.0000 0.992 0.00 1.00 0.00
#> 389 2 0.0000 0.992 0.00 1.00 0.00
#> 390 2 0.0000 0.992 0.00 1.00 0.00
#> 391 2 0.0000 0.992 0.00 1.00 0.00
#> 392 2 0.0892 0.974 0.00 0.98 0.02
#> 393 2 0.0000 0.992 0.00 1.00 0.00
#> 394 1 0.0000 0.996 1.00 0.00 0.00
#> 395 2 0.0000 0.992 0.00 1.00 0.00
#> 396 2 0.4555 0.750 0.00 0.80 0.20
#> 397 2 0.0000 0.992 0.00 1.00 0.00
#> 398 2 0.0000 0.992 0.00 1.00 0.00
#> 399 2 0.0000 0.992 0.00 1.00 0.00
#> 400 2 0.0000 0.992 0.00 1.00 0.00
#> 401 2 0.0000 0.992 0.00 1.00 0.00
#> 402 2 0.0000 0.992 0.00 1.00 0.00
#> 403 2 0.0000 0.992 0.00 1.00 0.00
#> 404 2 0.0000 0.992 0.00 1.00 0.00
#> 405 2 0.0000 0.992 0.00 1.00 0.00
#> 406 2 0.0000 0.992 0.00 1.00 0.00
#> 407 2 0.0000 0.992 0.00 1.00 0.00
#> 408 2 0.0000 0.992 0.00 1.00 0.00
#> 409 2 0.0000 0.992 0.00 1.00 0.00
#> 410 2 0.2537 0.911 0.00 0.92 0.08
#> 411 2 0.0000 0.992 0.00 1.00 0.00
#> 412 2 0.0000 0.992 0.00 1.00 0.00
#> 413 2 0.0000 0.992 0.00 1.00 0.00
#> 414 2 0.0000 0.992 0.00 1.00 0.00
#> 415 2 0.0000 0.992 0.00 1.00 0.00
#> 416 2 0.0000 0.992 0.00 1.00 0.00
#> 417 2 0.0000 0.992 0.00 1.00 0.00
#> 418 2 0.0000 0.992 0.00 1.00 0.00
#> 419 2 0.0000 0.992 0.00 1.00 0.00
#> 420 2 0.0000 0.992 0.00 1.00 0.00
#> 421 2 0.0000 0.992 0.00 1.00 0.00
#> 422 2 0.0000 0.992 0.00 1.00 0.00
#> 423 2 0.0000 0.992 0.00 1.00 0.00
#> 424 2 0.0000 0.992 0.00 1.00 0.00
#> 425 2 0.0000 0.992 0.00 1.00 0.00
#> 426 2 0.0000 0.992 0.00 1.00 0.00
#> 427 2 0.0000 0.992 0.00 1.00 0.00
#> 428 2 0.0000 0.992 0.00 1.00 0.00
#> 429 2 0.0000 0.992 0.00 1.00 0.00
#> 430 2 0.0000 0.992 0.00 1.00 0.00
#> 431 2 0.0000 0.992 0.00 1.00 0.00
#> 432 2 0.0000 0.992 0.00 1.00 0.00
#> 433 2 0.0000 0.992 0.00 1.00 0.00
#> 434 2 0.0000 0.992 0.00 1.00 0.00
#> 435 2 0.0000 0.992 0.00 1.00 0.00
#> 436 2 0.0000 0.992 0.00 1.00 0.00
#> 437 2 0.0000 0.992 0.00 1.00 0.00
#> 438 2 0.0000 0.992 0.00 1.00 0.00
#> 439 2 0.0000 0.992 0.00 1.00 0.00
#> 440 3 0.0000 0.994 0.00 0.00 1.00
#> 441 3 0.0000 0.994 0.00 0.00 1.00
#> 442 3 0.0000 0.994 0.00 0.00 1.00
#> 443 2 0.0000 0.992 0.00 1.00 0.00
#> 444 3 0.0000 0.994 0.00 0.00 1.00
#> 445 3 0.0000 0.994 0.00 0.00 1.00
#> 446 3 0.0000 0.994 0.00 0.00 1.00
#> 447 1 0.0000 0.996 1.00 0.00 0.00
#> 448 3 0.0000 0.994 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 2 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 3 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 4 4 0.2345 0.5788 0.00 0.00 0.10 0.90
#> 5 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 6 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 7 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 8 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 9 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 10 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 11 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 12 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 13 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 14 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 15 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 16 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 17 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 18 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 19 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 20 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 21 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 22 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 23 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 24 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 25 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 26 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 27 3 0.4855 -0.2361 0.00 0.00 0.60 0.40
#> 28 3 0.3975 -0.0477 0.00 0.00 0.76 0.24
#> 29 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 30 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 31 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 32 4 0.4855 0.3934 0.00 0.00 0.40 0.60
#> 33 3 0.4994 -0.3112 0.00 0.00 0.52 0.48
#> 34 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 35 3 0.4994 -0.3112 0.00 0.00 0.52 0.48
#> 36 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 37 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 38 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 39 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 40 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 41 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 42 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 43 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 44 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 45 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 46 1 0.3172 0.7295 0.84 0.00 0.16 0.00
#> 47 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 48 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 49 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 50 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 51 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 52 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 53 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 54 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 55 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 56 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 57 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 58 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 59 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 60 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 61 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 62 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 63 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 64 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 65 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 66 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 67 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 68 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 69 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 70 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 71 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 72 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 73 1 0.2921 0.7360 0.86 0.00 0.14 0.00
#> 74 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 75 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 76 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 77 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 78 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 79 1 0.3172 0.7295 0.84 0.00 0.16 0.00
#> 80 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 81 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 82 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 83 1 0.3172 0.7303 0.84 0.00 0.16 0.00
#> 84 1 0.3400 0.7225 0.82 0.00 0.18 0.00
#> 85 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 86 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 87 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 88 1 0.3975 0.6962 0.76 0.00 0.24 0.00
#> 89 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 90 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 91 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 92 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 93 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 94 1 0.3975 0.6961 0.76 0.00 0.24 0.00
#> 95 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 96 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 97 1 0.4907 0.5915 0.58 0.00 0.42 0.00
#> 98 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 99 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 100 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 101 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 102 1 0.4790 0.6179 0.62 0.00 0.38 0.00
#> 103 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 104 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 105 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 106 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 107 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 108 1 0.2011 0.7520 0.92 0.00 0.08 0.00
#> 109 1 0.2921 0.7360 0.86 0.00 0.14 0.00
#> 110 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 111 1 0.4790 0.6177 0.62 0.00 0.38 0.00
#> 112 1 0.3400 0.7224 0.82 0.00 0.18 0.00
#> 113 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 114 1 0.0707 0.7611 0.98 0.00 0.02 0.00
#> 115 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 116 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 117 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 118 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 119 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 120 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 121 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 122 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 123 1 0.4994 0.5495 0.52 0.00 0.48 0.00
#> 124 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 125 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 126 1 0.1211 0.7588 0.96 0.00 0.04 0.00
#> 127 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 128 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 129 1 0.3801 0.7061 0.78 0.00 0.22 0.00
#> 130 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 131 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 132 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 133 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 134 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 135 1 0.4790 0.6180 0.62 0.00 0.38 0.00
#> 136 1 0.1637 0.7560 0.94 0.00 0.06 0.00
#> 137 1 0.4977 0.5639 0.54 0.00 0.46 0.00
#> 138 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 139 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 140 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 141 1 0.3400 0.7224 0.82 0.00 0.18 0.00
#> 142 1 0.4277 0.6712 0.72 0.00 0.28 0.00
#> 143 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 144 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 145 1 0.2921 0.7365 0.86 0.00 0.14 0.00
#> 146 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 147 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 148 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 149 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 150 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 151 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 152 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 153 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 154 3 0.1637 0.1092 0.00 0.00 0.94 0.06
#> 155 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 156 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 157 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 158 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 159 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 160 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 161 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 162 4 0.2647 0.5329 0.12 0.00 0.00 0.88
#> 163 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 164 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 165 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 166 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 167 3 0.0707 0.1267 0.00 0.00 0.98 0.02
#> 168 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 169 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 170 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 171 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 172 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 173 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 174 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 175 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 176 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 177 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 178 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 179 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 180 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 181 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 182 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 183 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 184 3 0.2921 0.0614 0.00 0.00 0.86 0.14
#> 185 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 186 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 187 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 188 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 189 3 0.0707 0.1428 0.02 0.00 0.98 0.00
#> 190 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 191 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 192 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 193 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 194 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 195 4 0.4624 0.3915 0.00 0.00 0.34 0.66
#> 196 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 197 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 198 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 199 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 200 3 0.2647 0.1436 0.12 0.00 0.88 0.00
#> 201 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 202 4 0.4948 0.0939 0.00 0.00 0.44 0.56
#> 203 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 204 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 205 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 206 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 207 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 208 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 209 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 210 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 211 3 0.1637 0.1580 0.06 0.00 0.94 0.00
#> 212 3 0.4624 -0.3252 0.34 0.00 0.66 0.00
#> 213 3 0.5000 -0.5417 0.50 0.00 0.50 0.00
#> 214 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 215 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 216 1 0.5606 0.0367 0.50 0.02 0.00 0.48
#> 217 1 0.0707 0.7611 0.98 0.00 0.02 0.00
#> 218 1 0.6323 0.0519 0.50 0.06 0.00 0.44
#> 219 1 0.5000 0.5345 0.50 0.00 0.50 0.00
#> 220 4 0.1913 0.6084 0.02 0.00 0.04 0.94
#> 221 4 0.6011 -0.0220 0.48 0.04 0.00 0.48
#> 222 3 0.5606 -0.3231 0.02 0.00 0.50 0.48
#> 223 1 0.6005 0.0442 0.50 0.04 0.00 0.46
#> 224 3 0.4977 -0.2937 0.00 0.00 0.54 0.46
#> 225 3 0.3975 -0.0482 0.00 0.00 0.76 0.24
#> 226 3 0.3172 0.0467 0.00 0.00 0.84 0.16
#> 227 3 0.4855 -0.2370 0.00 0.00 0.60 0.40
#> 228 3 0.3801 -0.0227 0.00 0.00 0.78 0.22
#> 229 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 230 3 0.4277 -0.0986 0.00 0.00 0.72 0.28
#> 231 4 0.4948 0.3634 0.00 0.00 0.44 0.56
#> 232 3 0.2921 0.0616 0.00 0.00 0.86 0.14
#> 233 3 0.3172 0.0467 0.00 0.00 0.84 0.16
#> 234 3 0.3172 0.0467 0.00 0.00 0.84 0.16
#> 235 3 0.3172 0.0467 0.00 0.00 0.84 0.16
#> 236 3 0.2345 0.0872 0.00 0.00 0.90 0.10
#> 237 2 0.2345 0.8634 0.00 0.90 0.00 0.10
#> 238 4 0.0000 0.6150 0.00 0.00 0.00 1.00
#> 239 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 240 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 241 3 0.3172 0.0467 0.00 0.00 0.84 0.16
#> 242 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 243 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 244 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 245 3 0.3172 0.0370 0.00 0.00 0.84 0.16
#> 246 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 247 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 248 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 249 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 250 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 251 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 252 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 253 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 254 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 255 3 0.6005 -0.3146 0.04 0.00 0.50 0.46
#> 256 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 257 4 0.4994 0.3314 0.00 0.00 0.48 0.52
#> 258 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 259 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 260 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 261 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 262 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 263 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 264 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 265 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 266 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 267 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 268 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 269 4 0.7748 0.3138 0.00 0.28 0.28 0.44
#> 270 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 271 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 272 3 0.3172 0.0467 0.00 0.00 0.84 0.16
#> 273 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 274 3 0.6586 -0.2935 0.00 0.08 0.50 0.42
#> 275 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 276 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 277 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 278 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 279 3 0.6323 -0.1350 0.00 0.44 0.50 0.06
#> 280 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 281 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 282 1 0.0707 0.7611 0.98 0.00 0.02 0.00
#> 283 4 0.1637 0.5964 0.06 0.00 0.00 0.94
#> 284 2 0.1211 0.9325 0.00 0.96 0.00 0.04
#> 285 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 286 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 287 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 288 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 289 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 290 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 291 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 292 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 293 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 294 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 295 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 296 3 0.6988 -0.1634 0.00 0.38 0.50 0.12
#> 297 2 0.2411 0.8932 0.00 0.92 0.04 0.04
#> 298 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 299 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 300 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 301 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 302 2 0.0707 0.9525 0.00 0.98 0.00 0.02
#> 303 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 304 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 305 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 306 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 307 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 308 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 309 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 310 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 311 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 312 3 0.0000 0.1346 0.00 0.00 1.00 0.00
#> 313 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 314 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 315 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 316 2 0.1637 0.9114 0.00 0.94 0.00 0.06
#> 317 2 0.5957 0.1791 0.00 0.54 0.42 0.04
#> 318 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 319 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 320 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 321 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 322 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 323 2 0.0707 0.9526 0.00 0.98 0.02 0.00
#> 324 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 325 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 326 2 0.3172 0.7884 0.00 0.84 0.16 0.00
#> 327 1 0.5000 -0.2564 0.50 0.50 0.00 0.00
#> 328 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 329 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 330 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 331 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 332 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 333 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 334 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 335 2 0.5713 0.3856 0.00 0.62 0.34 0.04
#> 336 2 0.3975 0.6653 0.00 0.76 0.24 0.00
#> 337 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 338 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 339 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 340 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 341 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 342 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 343 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 344 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 345 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 346 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 347 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 348 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 349 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 350 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 351 2 0.5000 0.2321 0.50 0.50 0.00 0.00
#> 352 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 353 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 354 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 355 2 0.1637 0.9121 0.00 0.94 0.06 0.00
#> 356 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 357 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 358 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 359 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 360 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 361 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 362 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 363 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 364 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 365 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 366 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 367 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 368 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 369 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 370 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 371 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 372 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 373 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 374 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 375 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 376 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 377 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 378 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 379 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 380 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 381 4 0.6336 0.3135 0.00 0.06 0.46 0.48
#> 382 3 0.6005 -0.3097 0.00 0.04 0.50 0.46
#> 383 2 0.3172 0.8081 0.16 0.84 0.00 0.00
#> 384 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 385 3 0.5606 -0.3138 0.00 0.02 0.50 0.48
#> 386 3 0.6005 -0.3101 0.00 0.04 0.50 0.46
#> 387 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 388 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 389 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 390 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 391 2 0.5000 0.2450 0.50 0.50 0.00 0.00
#> 392 2 0.4797 0.5945 0.00 0.72 0.26 0.02
#> 393 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 394 1 0.2345 0.7473 0.90 0.00 0.10 0.00
#> 395 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 396 3 0.7583 -0.2138 0.00 0.28 0.48 0.24
#> 397 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 398 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 399 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 400 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 401 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 402 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 403 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 404 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 405 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 406 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 407 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 408 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 409 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 410 2 0.3853 0.7529 0.00 0.82 0.02 0.16
#> 411 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 412 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 413 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 414 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 415 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 416 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 417 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 418 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 419 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 420 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 421 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 422 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 423 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 424 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 425 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 426 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 427 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 428 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 429 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 430 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 431 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 432 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 433 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 434 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 435 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 436 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 437 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 438 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 439 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 440 3 0.4994 -0.3112 0.00 0.00 0.52 0.48
#> 441 3 0.3172 0.0467 0.00 0.00 0.84 0.16
#> 442 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 443 2 0.0000 0.9711 0.00 1.00 0.00 0.00
#> 444 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 445 4 0.5000 0.3171 0.00 0.00 0.50 0.50
#> 446 3 0.5000 -0.3333 0.00 0.00 0.50 0.50
#> 447 1 0.0000 0.7625 1.00 0.00 0.00 0.00
#> 448 3 0.4994 -0.3112 0.00 0.00 0.52 0.48
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 444 1.62e-69 2
#> ATC:skmeans 446 1.88e-143 3
#> ATC:skmeans 289 3.24e-102 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node012. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0121"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7298 rows and 177 columns.
#> Top rows (730) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.972 0.989 0.491 0.509 0.509
#> 3 3 0.755 0.794 0.906 0.210 0.895 0.796
#> 4 4 0.708 0.738 0.882 0.103 0.936 0.849
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.000 0.9919 1.00 0.00
#> 2 2 0.000 0.9850 0.00 1.00
#> 3 2 0.000 0.9850 0.00 1.00
#> 4 2 0.000 0.9850 0.00 1.00
#> 5 2 0.000 0.9850 0.00 1.00
#> 6 2 0.000 0.9850 0.00 1.00
#> 7 2 0.000 0.9850 0.00 1.00
#> 8 2 0.000 0.9850 0.00 1.00
#> 9 2 0.958 0.3950 0.38 0.62
#> 10 2 0.000 0.9850 0.00 1.00
#> 11 2 0.000 0.9850 0.00 1.00
#> 12 2 0.000 0.9850 0.00 1.00
#> 13 2 0.000 0.9850 0.00 1.00
#> 14 2 0.000 0.9850 0.00 1.00
#> 15 2 0.000 0.9850 0.00 1.00
#> 16 2 0.000 0.9850 0.00 1.00
#> 17 2 0.000 0.9850 0.00 1.00
#> 18 2 0.000 0.9850 0.00 1.00
#> 19 2 0.000 0.9850 0.00 1.00
#> 20 2 0.000 0.9850 0.00 1.00
#> 21 2 0.000 0.9850 0.00 1.00
#> 22 2 0.000 0.9850 0.00 1.00
#> 23 2 0.000 0.9850 0.00 1.00
#> 24 2 0.000 0.9850 0.00 1.00
#> 25 2 0.000 0.9850 0.00 1.00
#> 26 2 0.000 0.9850 0.00 1.00
#> 27 2 0.000 0.9850 0.00 1.00
#> 28 2 0.000 0.9850 0.00 1.00
#> 29 2 0.000 0.9850 0.00 1.00
#> 30 2 0.000 0.9850 0.00 1.00
#> 31 2 0.000 0.9850 0.00 1.00
#> 32 2 0.000 0.9850 0.00 1.00
#> 33 2 0.000 0.9850 0.00 1.00
#> 34 2 0.000 0.9850 0.00 1.00
#> 35 2 0.000 0.9850 0.00 1.00
#> 36 1 0.000 0.9919 1.00 0.00
#> 37 1 0.000 0.9919 1.00 0.00
#> 38 2 0.000 0.9850 0.00 1.00
#> 39 2 0.000 0.9850 0.00 1.00
#> 40 2 0.000 0.9850 0.00 1.00
#> 41 1 0.000 0.9919 1.00 0.00
#> 42 1 0.000 0.9919 1.00 0.00
#> 43 1 0.000 0.9919 1.00 0.00
#> 44 2 0.000 0.9850 0.00 1.00
#> 45 2 0.000 0.9850 0.00 1.00
#> 46 1 0.141 0.9718 0.98 0.02
#> 47 1 0.000 0.9919 1.00 0.00
#> 48 2 0.141 0.9668 0.02 0.98
#> 49 2 0.000 0.9850 0.00 1.00
#> 50 1 0.000 0.9919 1.00 0.00
#> 51 1 0.000 0.9919 1.00 0.00
#> 52 2 0.000 0.9850 0.00 1.00
#> 53 2 0.000 0.9850 0.00 1.00
#> 54 2 0.000 0.9850 0.00 1.00
#> 55 2 0.000 0.9850 0.00 1.00
#> 56 1 0.000 0.9919 1.00 0.00
#> 57 1 0.000 0.9919 1.00 0.00
#> 58 2 0.000 0.9850 0.00 1.00
#> 59 2 0.000 0.9850 0.00 1.00
#> 60 1 0.000 0.9919 1.00 0.00
#> 61 1 0.000 0.9919 1.00 0.00
#> 62 1 0.000 0.9919 1.00 0.00
#> 63 1 0.000 0.9919 1.00 0.00
#> 64 2 0.000 0.9850 0.00 1.00
#> 65 1 0.000 0.9919 1.00 0.00
#> 66 1 0.000 0.9919 1.00 0.00
#> 67 1 0.000 0.9919 1.00 0.00
#> 68 1 0.000 0.9919 1.00 0.00
#> 69 1 0.000 0.9919 1.00 0.00
#> 70 1 0.000 0.9919 1.00 0.00
#> 71 2 0.943 0.4458 0.36 0.64
#> 72 1 0.855 0.6031 0.72 0.28
#> 73 2 0.000 0.9850 0.00 1.00
#> 74 1 0.000 0.9919 1.00 0.00
#> 75 1 0.000 0.9919 1.00 0.00
#> 76 1 0.000 0.9919 1.00 0.00
#> 77 2 0.000 0.9850 0.00 1.00
#> 78 1 0.000 0.9919 1.00 0.00
#> 79 2 0.000 0.9850 0.00 1.00
#> 80 2 0.000 0.9850 0.00 1.00
#> 81 2 0.000 0.9850 0.00 1.00
#> 82 2 0.000 0.9850 0.00 1.00
#> 83 2 0.000 0.9850 0.00 1.00
#> 84 2 0.000 0.9850 0.00 1.00
#> 85 1 0.000 0.9919 1.00 0.00
#> 86 1 0.000 0.9919 1.00 0.00
#> 87 2 0.000 0.9850 0.00 1.00
#> 88 2 0.000 0.9850 0.00 1.00
#> 89 2 0.000 0.9850 0.00 1.00
#> 90 2 0.141 0.9668 0.02 0.98
#> 91 2 0.000 0.9850 0.00 1.00
#> 92 2 0.827 0.6507 0.26 0.74
#> 93 2 0.000 0.9850 0.00 1.00
#> 94 2 0.000 0.9850 0.00 1.00
#> 95 2 0.000 0.9850 0.00 1.00
#> 96 2 0.000 0.9850 0.00 1.00
#> 97 2 0.000 0.9850 0.00 1.00
#> 98 1 0.000 0.9919 1.00 0.00
#> 99 2 0.000 0.9850 0.00 1.00
#> 100 1 0.000 0.9919 1.00 0.00
#> 101 2 0.000 0.9850 0.00 1.00
#> 102 1 0.000 0.9919 1.00 0.00
#> 103 1 0.000 0.9919 1.00 0.00
#> 104 1 0.000 0.9919 1.00 0.00
#> 105 1 0.000 0.9919 1.00 0.00
#> 106 2 0.000 0.9850 0.00 1.00
#> 107 1 0.000 0.9919 1.00 0.00
#> 108 1 0.000 0.9919 1.00 0.00
#> 109 1 0.000 0.9919 1.00 0.00
#> 110 1 0.000 0.9919 1.00 0.00
#> 111 1 0.000 0.9919 1.00 0.00
#> 112 1 0.000 0.9919 1.00 0.00
#> 113 1 0.000 0.9919 1.00 0.00
#> 114 1 0.000 0.9919 1.00 0.00
#> 115 1 0.000 0.9919 1.00 0.00
#> 116 1 0.000 0.9919 1.00 0.00
#> 117 1 0.000 0.9919 1.00 0.00
#> 118 1 0.000 0.9919 1.00 0.00
#> 119 1 0.000 0.9919 1.00 0.00
#> 120 1 0.000 0.9919 1.00 0.00
#> 121 1 0.000 0.9919 1.00 0.00
#> 122 1 0.000 0.9919 1.00 0.00
#> 123 1 0.000 0.9919 1.00 0.00
#> 124 1 0.000 0.9919 1.00 0.00
#> 125 1 0.000 0.9919 1.00 0.00
#> 126 1 0.000 0.9919 1.00 0.00
#> 127 1 0.000 0.9919 1.00 0.00
#> 128 1 0.000 0.9919 1.00 0.00
#> 129 1 0.000 0.9919 1.00 0.00
#> 130 1 0.000 0.9919 1.00 0.00
#> 131 1 0.000 0.9919 1.00 0.00
#> 132 1 0.000 0.9919 1.00 0.00
#> 133 1 0.000 0.9919 1.00 0.00
#> 134 1 0.000 0.9919 1.00 0.00
#> 135 1 0.000 0.9919 1.00 0.00
#> 136 1 0.000 0.9919 1.00 0.00
#> 137 1 0.000 0.9919 1.00 0.00
#> 138 1 0.000 0.9919 1.00 0.00
#> 139 1 0.000 0.9919 1.00 0.00
#> 140 1 0.000 0.9919 1.00 0.00
#> 141 1 0.000 0.9919 1.00 0.00
#> 142 1 0.000 0.9919 1.00 0.00
#> 143 1 0.000 0.9919 1.00 0.00
#> 144 1 0.000 0.9919 1.00 0.00
#> 145 1 0.000 0.9919 1.00 0.00
#> 146 1 0.000 0.9919 1.00 0.00
#> 147 1 0.000 0.9919 1.00 0.00
#> 148 1 0.000 0.9919 1.00 0.00
#> 149 1 0.000 0.9919 1.00 0.00
#> 150 1 0.000 0.9919 1.00 0.00
#> 151 1 0.000 0.9919 1.00 0.00
#> 152 1 0.000 0.9919 1.00 0.00
#> 153 1 0.000 0.9919 1.00 0.00
#> 154 1 0.000 0.9919 1.00 0.00
#> 155 1 0.000 0.9919 1.00 0.00
#> 156 1 0.000 0.9919 1.00 0.00
#> 157 1 0.000 0.9919 1.00 0.00
#> 158 1 0.000 0.9919 1.00 0.00
#> 159 1 0.000 0.9919 1.00 0.00
#> 160 1 0.000 0.9919 1.00 0.00
#> 161 1 0.000 0.9919 1.00 0.00
#> 162 1 0.000 0.9919 1.00 0.00
#> 163 1 0.000 0.9919 1.00 0.00
#> 164 1 0.000 0.9919 1.00 0.00
#> 165 1 0.000 0.9919 1.00 0.00
#> 166 1 0.000 0.9919 1.00 0.00
#> 167 1 0.000 0.9919 1.00 0.00
#> 168 1 0.000 0.9919 1.00 0.00
#> 169 1 0.000 0.9919 1.00 0.00
#> 170 2 0.327 0.9263 0.06 0.94
#> 171 1 0.000 0.9919 1.00 0.00
#> 172 2 0.000 0.9850 0.00 1.00
#> 173 1 1.000 -0.0219 0.50 0.50
#> 174 2 0.000 0.9850 0.00 1.00
#> 175 1 0.000 0.9919 1.00 0.00
#> 176 1 0.000 0.9919 1.00 0.00
#> 177 2 0.000 0.9850 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 1 0.0892 0.9261 0.98 0.00 0.02
#> 2 2 0.0000 0.8959 0.00 1.00 0.00
#> 3 2 0.0000 0.8959 0.00 1.00 0.00
#> 4 2 0.0000 0.8959 0.00 1.00 0.00
#> 5 2 0.0000 0.8959 0.00 1.00 0.00
#> 6 2 0.6309 -0.0244 0.00 0.50 0.50
#> 7 2 0.0000 0.8959 0.00 1.00 0.00
#> 8 2 0.0000 0.8959 0.00 1.00 0.00
#> 9 3 0.8859 0.2933 0.12 0.40 0.48
#> 10 2 0.0000 0.8959 0.00 1.00 0.00
#> 11 2 0.0000 0.8959 0.00 1.00 0.00
#> 12 2 0.0000 0.8959 0.00 1.00 0.00
#> 13 2 0.0000 0.8959 0.00 1.00 0.00
#> 14 2 0.0000 0.8959 0.00 1.00 0.00
#> 15 2 0.4796 0.6716 0.00 0.78 0.22
#> 16 2 0.0000 0.8959 0.00 1.00 0.00
#> 17 2 0.0000 0.8959 0.00 1.00 0.00
#> 18 2 0.0000 0.8959 0.00 1.00 0.00
#> 19 2 0.0000 0.8959 0.00 1.00 0.00
#> 20 2 0.0000 0.8959 0.00 1.00 0.00
#> 21 2 0.0000 0.8959 0.00 1.00 0.00
#> 22 2 0.0000 0.8959 0.00 1.00 0.00
#> 23 2 0.0000 0.8959 0.00 1.00 0.00
#> 24 2 0.0000 0.8959 0.00 1.00 0.00
#> 25 2 0.0000 0.8959 0.00 1.00 0.00
#> 26 2 0.0000 0.8959 0.00 1.00 0.00
#> 27 2 0.0000 0.8959 0.00 1.00 0.00
#> 28 2 0.0000 0.8959 0.00 1.00 0.00
#> 29 2 0.0000 0.8959 0.00 1.00 0.00
#> 30 2 0.0000 0.8959 0.00 1.00 0.00
#> 31 2 0.0000 0.8959 0.00 1.00 0.00
#> 32 2 0.0000 0.8959 0.00 1.00 0.00
#> 33 2 0.0000 0.8959 0.00 1.00 0.00
#> 34 2 0.0000 0.8959 0.00 1.00 0.00
#> 35 2 0.0000 0.8959 0.00 1.00 0.00
#> 36 3 0.6045 0.4570 0.38 0.00 0.62
#> 37 1 0.1529 0.9095 0.96 0.00 0.04
#> 38 2 0.4796 0.6664 0.00 0.78 0.22
#> 39 2 0.0000 0.8959 0.00 1.00 0.00
#> 40 2 0.6045 0.3691 0.00 0.62 0.38
#> 41 1 0.0892 0.9261 0.98 0.00 0.02
#> 42 3 0.6302 0.2159 0.48 0.00 0.52
#> 43 1 0.2066 0.8860 0.94 0.00 0.06
#> 44 2 0.4796 0.6677 0.00 0.78 0.22
#> 45 2 0.6192 0.2346 0.00 0.58 0.42
#> 46 3 0.4796 0.6312 0.22 0.00 0.78
#> 47 3 0.5948 0.5063 0.36 0.00 0.64
#> 48 3 0.5706 0.4458 0.00 0.32 0.68
#> 49 2 0.5835 0.4503 0.00 0.66 0.34
#> 50 1 0.3686 0.7895 0.86 0.00 0.14
#> 51 1 0.6192 0.0909 0.58 0.00 0.42
#> 52 2 0.5706 0.4997 0.00 0.68 0.32
#> 53 2 0.0892 0.8797 0.00 0.98 0.02
#> 54 2 0.0000 0.8959 0.00 1.00 0.00
#> 55 3 0.5397 0.4831 0.00 0.28 0.72
#> 56 1 0.2066 0.8896 0.94 0.00 0.06
#> 57 3 0.5216 0.6160 0.26 0.00 0.74
#> 58 2 0.5948 0.4216 0.00 0.64 0.36
#> 59 3 0.5835 0.3802 0.00 0.34 0.66
#> 60 1 0.3340 0.8287 0.88 0.00 0.12
#> 61 1 0.0000 0.9409 1.00 0.00 0.00
#> 62 1 0.0000 0.9409 1.00 0.00 0.00
#> 63 1 0.0000 0.9409 1.00 0.00 0.00
#> 64 2 0.0000 0.8959 0.00 1.00 0.00
#> 65 1 0.5706 0.5188 0.68 0.00 0.32
#> 66 1 0.0000 0.9409 1.00 0.00 0.00
#> 67 1 0.0892 0.9261 0.98 0.00 0.02
#> 68 1 0.0000 0.9409 1.00 0.00 0.00
#> 69 1 0.3340 0.8275 0.88 0.00 0.12
#> 70 1 0.0000 0.9409 1.00 0.00 0.00
#> 71 3 0.6245 0.5998 0.06 0.18 0.76
#> 72 3 0.9020 0.5117 0.22 0.22 0.56
#> 73 2 0.6302 0.0241 0.00 0.52 0.48
#> 74 1 0.6192 0.2870 0.58 0.00 0.42
#> 75 1 0.5948 0.4342 0.64 0.00 0.36
#> 76 1 0.0000 0.9409 1.00 0.00 0.00
#> 77 2 0.5016 0.6025 0.00 0.76 0.24
#> 78 1 0.4555 0.7210 0.80 0.00 0.20
#> 79 2 0.2959 0.8115 0.00 0.90 0.10
#> 80 2 0.0000 0.8959 0.00 1.00 0.00
#> 81 2 0.0000 0.8959 0.00 1.00 0.00
#> 82 2 0.0000 0.8959 0.00 1.00 0.00
#> 83 2 0.5397 0.5241 0.00 0.72 0.28
#> 84 2 0.2066 0.8420 0.00 0.94 0.06
#> 85 1 0.6984 0.2275 0.56 0.02 0.42
#> 86 1 0.5835 0.4770 0.66 0.00 0.34
#> 87 2 0.0000 0.8959 0.00 1.00 0.00
#> 88 2 0.6126 0.3013 0.00 0.60 0.40
#> 89 3 0.4796 0.5408 0.00 0.22 0.78
#> 90 3 0.4796 0.5432 0.00 0.22 0.78
#> 91 2 0.2959 0.8128 0.00 0.90 0.10
#> 92 3 0.6677 0.5905 0.08 0.18 0.74
#> 93 2 0.6126 0.3179 0.00 0.60 0.40
#> 94 2 0.0000 0.8959 0.00 1.00 0.00
#> 95 2 0.0000 0.8959 0.00 1.00 0.00
#> 96 2 0.0000 0.8959 0.00 1.00 0.00
#> 97 2 0.0000 0.8959 0.00 1.00 0.00
#> 98 1 0.5560 0.5581 0.70 0.00 0.30
#> 99 3 0.5397 0.4665 0.00 0.28 0.72
#> 100 1 0.5706 0.5254 0.68 0.00 0.32
#> 101 2 0.0000 0.8959 0.00 1.00 0.00
#> 102 1 0.2066 0.8906 0.94 0.00 0.06
#> 103 1 0.5397 0.5946 0.72 0.00 0.28
#> 104 3 0.5835 0.5355 0.34 0.00 0.66
#> 105 1 0.6045 0.2380 0.62 0.00 0.38
#> 106 3 0.6280 0.0975 0.00 0.46 0.54
#> 107 1 0.0000 0.9409 1.00 0.00 0.00
#> 108 3 0.6045 0.4751 0.38 0.00 0.62
#> 109 1 0.0000 0.9409 1.00 0.00 0.00
#> 110 1 0.0000 0.9409 1.00 0.00 0.00
#> 111 1 0.0000 0.9409 1.00 0.00 0.00
#> 112 1 0.0000 0.9409 1.00 0.00 0.00
#> 113 1 0.0000 0.9409 1.00 0.00 0.00
#> 114 1 0.0000 0.9409 1.00 0.00 0.00
#> 115 1 0.0000 0.9409 1.00 0.00 0.00
#> 116 1 0.0000 0.9409 1.00 0.00 0.00
#> 117 1 0.0000 0.9409 1.00 0.00 0.00
#> 118 1 0.0000 0.9409 1.00 0.00 0.00
#> 119 1 0.0000 0.9409 1.00 0.00 0.00
#> 120 1 0.0000 0.9409 1.00 0.00 0.00
#> 121 1 0.0000 0.9409 1.00 0.00 0.00
#> 122 1 0.0000 0.9409 1.00 0.00 0.00
#> 123 1 0.0000 0.9409 1.00 0.00 0.00
#> 124 1 0.0000 0.9409 1.00 0.00 0.00
#> 125 1 0.0000 0.9409 1.00 0.00 0.00
#> 126 1 0.0000 0.9409 1.00 0.00 0.00
#> 127 1 0.0000 0.9409 1.00 0.00 0.00
#> 128 1 0.0000 0.9409 1.00 0.00 0.00
#> 129 1 0.0000 0.9409 1.00 0.00 0.00
#> 130 1 0.0892 0.9247 0.98 0.00 0.02
#> 131 1 0.0000 0.9409 1.00 0.00 0.00
#> 132 1 0.0000 0.9409 1.00 0.00 0.00
#> 133 1 0.0000 0.9409 1.00 0.00 0.00
#> 134 1 0.0000 0.9409 1.00 0.00 0.00
#> 135 1 0.0000 0.9409 1.00 0.00 0.00
#> 136 1 0.0000 0.9409 1.00 0.00 0.00
#> 137 1 0.0000 0.9409 1.00 0.00 0.00
#> 138 1 0.0000 0.9409 1.00 0.00 0.00
#> 139 1 0.1529 0.9060 0.96 0.00 0.04
#> 140 1 0.0000 0.9409 1.00 0.00 0.00
#> 141 1 0.0892 0.9247 0.98 0.00 0.02
#> 142 1 0.0000 0.9409 1.00 0.00 0.00
#> 143 1 0.0000 0.9409 1.00 0.00 0.00
#> 144 1 0.0000 0.9409 1.00 0.00 0.00
#> 145 1 0.0000 0.9409 1.00 0.00 0.00
#> 146 1 0.0000 0.9409 1.00 0.00 0.00
#> 147 1 0.0000 0.9409 1.00 0.00 0.00
#> 148 1 0.0000 0.9409 1.00 0.00 0.00
#> 149 1 0.0000 0.9409 1.00 0.00 0.00
#> 150 1 0.0000 0.9409 1.00 0.00 0.00
#> 151 1 0.0000 0.9409 1.00 0.00 0.00
#> 152 1 0.0000 0.9409 1.00 0.00 0.00
#> 153 1 0.0000 0.9409 1.00 0.00 0.00
#> 154 1 0.0000 0.9409 1.00 0.00 0.00
#> 155 1 0.0000 0.9409 1.00 0.00 0.00
#> 156 1 0.0000 0.9409 1.00 0.00 0.00
#> 157 1 0.0000 0.9409 1.00 0.00 0.00
#> 158 1 0.0000 0.9409 1.00 0.00 0.00
#> 159 1 0.0000 0.9409 1.00 0.00 0.00
#> 160 1 0.0892 0.9257 0.98 0.00 0.02
#> 161 1 0.0000 0.9409 1.00 0.00 0.00
#> 162 1 0.0000 0.9409 1.00 0.00 0.00
#> 163 1 0.0000 0.9409 1.00 0.00 0.00
#> 164 1 0.0000 0.9409 1.00 0.00 0.00
#> 165 1 0.0000 0.9409 1.00 0.00 0.00
#> 166 1 0.0000 0.9409 1.00 0.00 0.00
#> 167 1 0.0000 0.9409 1.00 0.00 0.00
#> 168 1 0.0000 0.9409 1.00 0.00 0.00
#> 169 1 0.0000 0.9409 1.00 0.00 0.00
#> 170 3 0.7310 0.4170 0.04 0.36 0.60
#> 171 1 0.0000 0.9409 1.00 0.00 0.00
#> 172 2 0.0000 0.8959 0.00 1.00 0.00
#> 173 3 0.8631 0.5933 0.18 0.22 0.60
#> 174 2 0.5397 0.5781 0.00 0.72 0.28
#> 175 1 0.0000 0.9409 1.00 0.00 0.00
#> 176 1 0.0892 0.9253 0.98 0.00 0.02
#> 177 2 0.0000 0.8959 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 1 0.3610 0.7559 0.80 0.00 0.00 0.20
#> 2 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 3 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 4 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 5 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 6 2 0.6766 0.2470 0.00 0.52 0.38 0.10
#> 7 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 8 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 9 4 0.3821 0.4772 0.00 0.04 0.12 0.84
#> 10 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 11 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 12 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 13 2 0.1637 0.8457 0.00 0.94 0.06 0.00
#> 14 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 15 2 0.6074 0.4262 0.00 0.60 0.34 0.06
#> 16 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 17 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 18 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 19 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 20 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 21 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 22 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 23 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 24 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 25 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 26 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 27 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 28 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 29 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 30 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 31 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 32 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 33 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 34 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 35 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 36 3 0.7382 0.2676 0.26 0.00 0.52 0.22
#> 37 1 0.3400 0.7777 0.82 0.00 0.00 0.18
#> 38 2 0.5173 0.5371 0.00 0.66 0.32 0.02
#> 39 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 40 2 0.5271 0.4979 0.00 0.64 0.34 0.02
#> 41 1 0.2647 0.8515 0.88 0.00 0.00 0.12
#> 42 3 0.6976 0.2835 0.24 0.00 0.58 0.18
#> 43 1 0.2706 0.8721 0.90 0.00 0.02 0.08
#> 44 2 0.4948 0.3124 0.00 0.56 0.44 0.00
#> 45 3 0.4713 0.2929 0.00 0.36 0.64 0.00
#> 46 3 0.6574 0.1009 0.04 0.02 0.52 0.42
#> 47 3 0.3037 0.5013 0.10 0.00 0.88 0.02
#> 48 4 0.6366 0.3079 0.00 0.12 0.24 0.64
#> 49 2 0.4277 0.6262 0.00 0.72 0.28 0.00
#> 50 1 0.5594 0.6201 0.72 0.00 0.18 0.10
#> 51 1 0.5487 0.2112 0.58 0.00 0.40 0.02
#> 52 2 0.7310 0.1119 0.00 0.48 0.36 0.16
#> 53 2 0.2830 0.8216 0.00 0.90 0.06 0.04
#> 54 2 0.3525 0.7847 0.00 0.86 0.04 0.10
#> 55 3 0.5428 0.4090 0.00 0.14 0.74 0.12
#> 56 1 0.3853 0.7872 0.82 0.00 0.02 0.16
#> 57 3 0.5657 0.4303 0.16 0.00 0.72 0.12
#> 58 2 0.6805 0.1743 0.00 0.50 0.40 0.10
#> 59 4 0.7135 0.2244 0.00 0.20 0.24 0.56
#> 60 1 0.7121 0.1784 0.54 0.00 0.16 0.30
#> 61 1 0.2647 0.8481 0.88 0.00 0.00 0.12
#> 62 1 0.2921 0.8284 0.86 0.00 0.00 0.14
#> 63 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 64 2 0.2011 0.8345 0.00 0.92 0.08 0.00
#> 65 4 0.5487 0.2343 0.40 0.00 0.02 0.58
#> 66 1 0.0707 0.9225 0.98 0.00 0.00 0.02
#> 67 1 0.2647 0.8488 0.88 0.00 0.00 0.12
#> 68 1 0.1211 0.9121 0.96 0.00 0.00 0.04
#> 69 1 0.4936 0.5832 0.70 0.00 0.02 0.28
#> 70 1 0.1211 0.9109 0.96 0.00 0.00 0.04
#> 71 4 0.7075 0.1351 0.02 0.08 0.36 0.54
#> 72 4 0.5255 0.5206 0.08 0.02 0.12 0.78
#> 73 4 0.4292 0.4900 0.00 0.10 0.08 0.82
#> 74 4 0.3611 0.5480 0.08 0.00 0.06 0.86
#> 75 4 0.3335 0.5167 0.12 0.00 0.02 0.86
#> 76 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 77 2 0.6611 -0.0145 0.00 0.46 0.08 0.46
#> 78 1 0.4624 0.4719 0.66 0.00 0.00 0.34
#> 79 2 0.4277 0.6290 0.00 0.72 0.28 0.00
#> 80 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 81 2 0.1637 0.8452 0.00 0.94 0.00 0.06
#> 82 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 83 2 0.6011 0.0369 0.00 0.48 0.04 0.48
#> 84 2 0.4797 0.6270 0.00 0.72 0.02 0.26
#> 85 4 0.2335 0.5417 0.06 0.00 0.02 0.92
#> 86 4 0.4134 0.4200 0.26 0.00 0.00 0.74
#> 87 2 0.0707 0.8663 0.00 0.98 0.00 0.02
#> 88 2 0.6323 0.1758 0.00 0.50 0.44 0.06
#> 89 3 0.1637 0.4792 0.00 0.06 0.94 0.00
#> 90 3 0.2335 0.4472 0.00 0.02 0.92 0.06
#> 91 2 0.3606 0.7670 0.00 0.84 0.14 0.02
#> 92 4 0.6782 0.3203 0.04 0.04 0.34 0.58
#> 93 2 0.7653 0.1092 0.00 0.46 0.30 0.24
#> 94 2 0.0707 0.8679 0.00 0.98 0.02 0.00
#> 95 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 96 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 97 2 0.0000 0.8783 0.00 1.00 0.00 0.00
#> 98 4 0.5767 0.3546 0.28 0.00 0.06 0.66
#> 99 4 0.6104 0.3830 0.00 0.18 0.14 0.68
#> 100 4 0.6881 0.2543 0.34 0.00 0.12 0.54
#> 101 2 0.0707 0.8676 0.00 0.98 0.02 0.00
#> 102 1 0.4936 0.5885 0.70 0.00 0.02 0.28
#> 103 1 0.4907 0.2603 0.58 0.00 0.00 0.42
#> 104 3 0.5383 0.4191 0.10 0.00 0.74 0.16
#> 105 3 0.4977 0.1562 0.46 0.00 0.54 0.00
#> 106 3 0.3975 0.4111 0.00 0.24 0.76 0.00
#> 107 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 108 3 0.5820 0.3948 0.24 0.00 0.68 0.08
#> 109 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 110 1 0.0707 0.9221 0.98 0.00 0.02 0.00
#> 111 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 112 1 0.0707 0.9221 0.98 0.00 0.02 0.00
#> 113 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 114 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 115 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 116 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 117 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 118 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 119 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 120 1 0.2011 0.8817 0.92 0.00 0.00 0.08
#> 121 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 122 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 123 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 124 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 125 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 126 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 127 1 0.0707 0.9221 0.98 0.00 0.00 0.02
#> 128 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 129 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 130 1 0.2345 0.8518 0.90 0.00 0.10 0.00
#> 131 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 132 1 0.0707 0.9221 0.98 0.00 0.02 0.00
#> 133 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 134 1 0.1913 0.9015 0.94 0.00 0.02 0.04
#> 135 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 136 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 137 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 138 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 139 1 0.2706 0.8656 0.90 0.00 0.08 0.02
#> 140 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 141 1 0.0707 0.9217 0.98 0.00 0.02 0.00
#> 142 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 143 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 144 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 145 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 146 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 147 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 148 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 149 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 150 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 151 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 152 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 153 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 154 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 155 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 156 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 157 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 158 1 0.2011 0.8844 0.92 0.00 0.00 0.08
#> 159 1 0.0707 0.9225 0.98 0.00 0.00 0.02
#> 160 1 0.5291 0.6655 0.74 0.00 0.08 0.18
#> 161 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 162 1 0.1211 0.9108 0.96 0.00 0.00 0.04
#> 163 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 164 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 165 1 0.3247 0.8406 0.88 0.00 0.06 0.06
#> 166 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 167 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 168 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 169 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 170 3 0.6110 0.3661 0.00 0.24 0.66 0.10
#> 171 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 172 2 0.1211 0.8570 0.00 0.96 0.04 0.00
#> 173 3 0.7568 0.3929 0.14 0.06 0.62 0.18
#> 174 2 0.5256 0.5952 0.00 0.70 0.26 0.04
#> 175 1 0.0000 0.9334 1.00 0.00 0.00 0.00
#> 176 1 0.5661 0.5466 0.70 0.00 0.08 0.22
#> 177 2 0.0000 0.8783 0.00 1.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 174 0.411 2
#> ATC:skmeans 152 0.471 3
#> ATC:skmeans 138 0.941 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node012. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0122"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7335 rows and 131 columns.
#> Top rows (734) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.998 0.489 0.512 0.512
#> 3 3 0.975 0.961 0.984 0.286 0.773 0.591
#> 4 4 0.856 0.858 0.940 0.152 0.860 0.645
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.996 0.00 1.00
#> 2 2 0.000 0.996 0.00 1.00
#> 3 2 0.000 0.996 0.00 1.00
#> 4 2 0.000 0.996 0.00 1.00
#> 5 2 0.000 0.996 0.00 1.00
#> 6 1 0.000 0.998 1.00 0.00
#> 7 1 0.000 0.998 1.00 0.00
#> 8 1 0.000 0.998 1.00 0.00
#> 9 1 0.000 0.998 1.00 0.00
#> 10 1 0.242 0.959 0.96 0.04
#> 11 2 0.000 0.996 0.00 1.00
#> 12 1 0.000 0.998 1.00 0.00
#> 13 1 0.000 0.998 1.00 0.00
#> 14 1 0.000 0.998 1.00 0.00
#> 15 1 0.000 0.998 1.00 0.00
#> 16 2 0.000 0.996 0.00 1.00
#> 17 2 0.000 0.996 0.00 1.00
#> 18 2 0.242 0.959 0.04 0.96
#> 19 2 0.000 0.996 0.00 1.00
#> 20 1 0.000 0.998 1.00 0.00
#> 21 1 0.000 0.998 1.00 0.00
#> 22 1 0.000 0.998 1.00 0.00
#> 23 1 0.000 0.998 1.00 0.00
#> 24 2 0.000 0.996 0.00 1.00
#> 25 1 0.000 0.998 1.00 0.00
#> 26 1 0.000 0.998 1.00 0.00
#> 27 1 0.000 0.998 1.00 0.00
#> 28 1 0.000 0.998 1.00 0.00
#> 29 1 0.000 0.998 1.00 0.00
#> 30 1 0.000 0.998 1.00 0.00
#> 31 1 0.000 0.998 1.00 0.00
#> 32 1 0.000 0.998 1.00 0.00
#> 33 1 0.000 0.998 1.00 0.00
#> 34 1 0.000 0.998 1.00 0.00
#> 35 1 0.000 0.998 1.00 0.00
#> 36 1 0.000 0.998 1.00 0.00
#> 37 1 0.000 0.998 1.00 0.00
#> 38 2 0.000 0.996 0.00 1.00
#> 39 1 0.000 0.998 1.00 0.00
#> 40 1 0.000 0.998 1.00 0.00
#> 41 1 0.000 0.998 1.00 0.00
#> 42 1 0.000 0.998 1.00 0.00
#> 43 1 0.000 0.998 1.00 0.00
#> 44 1 0.000 0.998 1.00 0.00
#> 45 1 0.000 0.998 1.00 0.00
#> 46 1 0.000 0.998 1.00 0.00
#> 47 1 0.000 0.998 1.00 0.00
#> 48 2 0.000 0.996 0.00 1.00
#> 49 1 0.000 0.998 1.00 0.00
#> 50 1 0.000 0.998 1.00 0.00
#> 51 1 0.000 0.998 1.00 0.00
#> 52 1 0.000 0.998 1.00 0.00
#> 53 1 0.000 0.998 1.00 0.00
#> 54 1 0.000 0.998 1.00 0.00
#> 55 1 0.000 0.998 1.00 0.00
#> 56 1 0.000 0.998 1.00 0.00
#> 57 1 0.000 0.998 1.00 0.00
#> 58 1 0.000 0.998 1.00 0.00
#> 59 1 0.000 0.998 1.00 0.00
#> 60 1 0.000 0.998 1.00 0.00
#> 61 1 0.000 0.998 1.00 0.00
#> 62 1 0.000 0.998 1.00 0.00
#> 63 1 0.000 0.998 1.00 0.00
#> 64 1 0.000 0.998 1.00 0.00
#> 65 2 0.000 0.996 0.00 1.00
#> 66 1 0.000 0.998 1.00 0.00
#> 67 1 0.000 0.998 1.00 0.00
#> 68 1 0.000 0.998 1.00 0.00
#> 69 1 0.141 0.979 0.98 0.02
#> 70 1 0.000 0.998 1.00 0.00
#> 71 1 0.000 0.998 1.00 0.00
#> 72 1 0.000 0.998 1.00 0.00
#> 73 1 0.000 0.998 1.00 0.00
#> 74 2 0.000 0.996 0.00 1.00
#> 75 1 0.000 0.998 1.00 0.00
#> 76 1 0.000 0.998 1.00 0.00
#> 77 2 0.000 0.996 0.00 1.00
#> 78 2 0.000 0.996 0.00 1.00
#> 79 1 0.000 0.998 1.00 0.00
#> 80 1 0.000 0.998 1.00 0.00
#> 81 1 0.000 0.998 1.00 0.00
#> 82 2 0.000 0.996 0.00 1.00
#> 83 2 0.000 0.996 0.00 1.00
#> 84 1 0.000 0.998 1.00 0.00
#> 85 2 0.000 0.996 0.00 1.00
#> 86 2 0.000 0.996 0.00 1.00
#> 87 1 0.000 0.998 1.00 0.00
#> 88 2 0.000 0.996 0.00 1.00
#> 89 2 0.000 0.996 0.00 1.00
#> 90 1 0.000 0.998 1.00 0.00
#> 91 2 0.000 0.996 0.00 1.00
#> 92 2 0.000 0.996 0.00 1.00
#> 93 1 0.000 0.998 1.00 0.00
#> 94 1 0.000 0.998 1.00 0.00
#> 95 1 0.000 0.998 1.00 0.00
#> 96 1 0.000 0.998 1.00 0.00
#> 97 2 0.000 0.996 0.00 1.00
#> 98 1 0.000 0.998 1.00 0.00
#> 99 2 0.000 0.996 0.00 1.00
#> 100 2 0.000 0.996 0.00 1.00
#> 101 2 0.242 0.959 0.04 0.96
#> 102 2 0.000 0.996 0.00 1.00
#> 103 2 0.000 0.996 0.00 1.00
#> 104 1 0.327 0.936 0.94 0.06
#> 105 2 0.000 0.996 0.00 1.00
#> 106 2 0.000 0.996 0.00 1.00
#> 107 2 0.000 0.996 0.00 1.00
#> 108 1 0.000 0.998 1.00 0.00
#> 109 2 0.000 0.996 0.00 1.00
#> 110 1 0.000 0.998 1.00 0.00
#> 111 1 0.000 0.998 1.00 0.00
#> 112 2 0.469 0.891 0.10 0.90
#> 113 2 0.000 0.996 0.00 1.00
#> 114 2 0.000 0.996 0.00 1.00
#> 115 2 0.000 0.996 0.00 1.00
#> 116 2 0.000 0.996 0.00 1.00
#> 117 2 0.000 0.996 0.00 1.00
#> 118 2 0.000 0.996 0.00 1.00
#> 119 2 0.000 0.996 0.00 1.00
#> 120 2 0.000 0.996 0.00 1.00
#> 121 2 0.000 0.996 0.00 1.00
#> 122 2 0.000 0.996 0.00 1.00
#> 123 2 0.000 0.996 0.00 1.00
#> 124 2 0.000 0.996 0.00 1.00
#> 125 2 0.141 0.978 0.02 0.98
#> 126 2 0.000 0.996 0.00 1.00
#> 127 2 0.000 0.996 0.00 1.00
#> 128 2 0.000 0.996 0.00 1.00
#> 129 2 0.000 0.996 0.00 1.00
#> 130 2 0.000 0.996 0.00 1.00
#> 131 1 0.000 0.998 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.984 0.00 0.00 1.00
#> 2 3 0.0000 0.984 0.00 0.00 1.00
#> 3 3 0.0000 0.984 0.00 0.00 1.00
#> 4 3 0.0000 0.984 0.00 0.00 1.00
#> 5 3 0.0000 0.984 0.00 0.00 1.00
#> 6 1 0.0000 0.985 1.00 0.00 0.00
#> 7 3 0.0000 0.984 0.00 0.00 1.00
#> 8 1 0.5835 0.480 0.66 0.00 0.34
#> 9 3 0.5835 0.473 0.34 0.00 0.66
#> 10 3 0.0000 0.984 0.00 0.00 1.00
#> 11 3 0.0000 0.984 0.00 0.00 1.00
#> 12 1 0.0000 0.985 1.00 0.00 0.00
#> 13 3 0.0000 0.984 0.00 0.00 1.00
#> 14 1 0.0000 0.985 1.00 0.00 0.00
#> 15 1 0.0000 0.985 1.00 0.00 0.00
#> 16 2 0.0000 0.973 0.00 1.00 0.00
#> 17 3 0.0000 0.984 0.00 0.00 1.00
#> 18 2 0.0000 0.973 0.00 1.00 0.00
#> 19 3 0.0000 0.984 0.00 0.00 1.00
#> 20 1 0.0000 0.985 1.00 0.00 0.00
#> 21 1 0.0000 0.985 1.00 0.00 0.00
#> 22 1 0.0000 0.985 1.00 0.00 0.00
#> 23 1 0.0000 0.985 1.00 0.00 0.00
#> 24 3 0.0000 0.984 0.00 0.00 1.00
#> 25 1 0.0892 0.967 0.98 0.02 0.00
#> 26 1 0.0000 0.985 1.00 0.00 0.00
#> 27 1 0.0000 0.985 1.00 0.00 0.00
#> 28 1 0.0000 0.985 1.00 0.00 0.00
#> 29 1 0.0000 0.985 1.00 0.00 0.00
#> 30 1 0.0000 0.985 1.00 0.00 0.00
#> 31 1 0.0000 0.985 1.00 0.00 0.00
#> 32 1 0.0000 0.985 1.00 0.00 0.00
#> 33 1 0.0000 0.985 1.00 0.00 0.00
#> 34 1 0.0000 0.985 1.00 0.00 0.00
#> 35 1 0.0000 0.985 1.00 0.00 0.00
#> 36 1 0.1529 0.949 0.96 0.00 0.04
#> 37 1 0.0000 0.985 1.00 0.00 0.00
#> 38 2 0.0000 0.973 0.00 1.00 0.00
#> 39 1 0.0000 0.985 1.00 0.00 0.00
#> 40 1 0.0000 0.985 1.00 0.00 0.00
#> 41 1 0.0000 0.985 1.00 0.00 0.00
#> 42 1 0.0000 0.985 1.00 0.00 0.00
#> 43 2 0.5560 0.585 0.30 0.70 0.00
#> 44 1 0.2959 0.886 0.90 0.00 0.10
#> 45 1 0.0000 0.985 1.00 0.00 0.00
#> 46 1 0.0000 0.985 1.00 0.00 0.00
#> 47 1 0.0000 0.985 1.00 0.00 0.00
#> 48 3 0.0000 0.984 0.00 0.00 1.00
#> 49 1 0.0000 0.985 1.00 0.00 0.00
#> 50 1 0.0000 0.985 1.00 0.00 0.00
#> 51 1 0.0000 0.985 1.00 0.00 0.00
#> 52 1 0.0000 0.985 1.00 0.00 0.00
#> 53 1 0.0000 0.985 1.00 0.00 0.00
#> 54 1 0.4209 0.844 0.86 0.12 0.02
#> 55 1 0.0892 0.967 0.98 0.02 0.00
#> 56 1 0.0000 0.985 1.00 0.00 0.00
#> 57 1 0.0000 0.985 1.00 0.00 0.00
#> 58 1 0.0000 0.985 1.00 0.00 0.00
#> 59 1 0.0000 0.985 1.00 0.00 0.00
#> 60 1 0.0000 0.985 1.00 0.00 0.00
#> 61 2 0.2066 0.918 0.06 0.94 0.00
#> 62 1 0.0000 0.985 1.00 0.00 0.00
#> 63 1 0.0000 0.985 1.00 0.00 0.00
#> 64 1 0.2959 0.883 0.90 0.10 0.00
#> 65 2 0.0000 0.973 0.00 1.00 0.00
#> 66 1 0.0000 0.985 1.00 0.00 0.00
#> 67 1 0.0000 0.985 1.00 0.00 0.00
#> 68 2 0.4291 0.771 0.18 0.82 0.00
#> 69 2 0.1529 0.938 0.04 0.96 0.00
#> 70 1 0.0000 0.985 1.00 0.00 0.00
#> 71 1 0.0000 0.985 1.00 0.00 0.00
#> 72 1 0.0000 0.985 1.00 0.00 0.00
#> 73 1 0.0000 0.985 1.00 0.00 0.00
#> 74 2 0.0000 0.973 0.00 1.00 0.00
#> 75 1 0.0000 0.985 1.00 0.00 0.00
#> 76 2 0.2066 0.919 0.06 0.94 0.00
#> 77 3 0.0000 0.984 0.00 0.00 1.00
#> 78 3 0.0000 0.984 0.00 0.00 1.00
#> 79 1 0.0000 0.985 1.00 0.00 0.00
#> 80 1 0.0000 0.985 1.00 0.00 0.00
#> 81 1 0.0000 0.985 1.00 0.00 0.00
#> 82 2 0.0000 0.973 0.00 1.00 0.00
#> 83 3 0.0000 0.984 0.00 0.00 1.00
#> 84 1 0.0000 0.985 1.00 0.00 0.00
#> 85 3 0.0000 0.984 0.00 0.00 1.00
#> 86 3 0.0000 0.984 0.00 0.00 1.00
#> 87 3 0.0000 0.984 0.00 0.00 1.00
#> 88 3 0.0000 0.984 0.00 0.00 1.00
#> 89 3 0.0000 0.984 0.00 0.00 1.00
#> 90 2 0.3686 0.825 0.14 0.86 0.00
#> 91 2 0.0000 0.973 0.00 1.00 0.00
#> 92 2 0.0000 0.973 0.00 1.00 0.00
#> 93 1 0.0000 0.985 1.00 0.00 0.00
#> 94 1 0.0000 0.985 1.00 0.00 0.00
#> 95 2 0.2537 0.897 0.08 0.92 0.00
#> 96 1 0.0000 0.985 1.00 0.00 0.00
#> 97 2 0.0000 0.973 0.00 1.00 0.00
#> 98 1 0.0000 0.985 1.00 0.00 0.00
#> 99 3 0.0000 0.984 0.00 0.00 1.00
#> 100 3 0.0000 0.984 0.00 0.00 1.00
#> 101 3 0.0000 0.984 0.00 0.00 1.00
#> 102 3 0.0000 0.984 0.00 0.00 1.00
#> 103 2 0.0000 0.973 0.00 1.00 0.00
#> 104 2 0.0000 0.973 0.00 1.00 0.00
#> 105 2 0.0000 0.973 0.00 1.00 0.00
#> 106 2 0.0000 0.973 0.00 1.00 0.00
#> 107 2 0.0000 0.973 0.00 1.00 0.00
#> 108 1 0.0000 0.985 1.00 0.00 0.00
#> 109 2 0.0000 0.973 0.00 1.00 0.00
#> 110 1 0.0892 0.967 0.98 0.02 0.00
#> 111 1 0.3686 0.837 0.86 0.14 0.00
#> 112 2 0.0000 0.973 0.00 1.00 0.00
#> 113 2 0.0000 0.973 0.00 1.00 0.00
#> 114 2 0.0000 0.973 0.00 1.00 0.00
#> 115 2 0.0000 0.973 0.00 1.00 0.00
#> 116 2 0.0000 0.973 0.00 1.00 0.00
#> 117 2 0.0000 0.973 0.00 1.00 0.00
#> 118 2 0.0000 0.973 0.00 1.00 0.00
#> 119 2 0.0000 0.973 0.00 1.00 0.00
#> 120 2 0.0000 0.973 0.00 1.00 0.00
#> 121 2 0.0000 0.973 0.00 1.00 0.00
#> 122 2 0.0000 0.973 0.00 1.00 0.00
#> 123 2 0.0000 0.973 0.00 1.00 0.00
#> 124 2 0.0000 0.973 0.00 1.00 0.00
#> 125 2 0.0892 0.956 0.02 0.98 0.00
#> 126 2 0.0000 0.973 0.00 1.00 0.00
#> 127 2 0.0000 0.973 0.00 1.00 0.00
#> 128 2 0.0000 0.973 0.00 1.00 0.00
#> 129 2 0.0000 0.973 0.00 1.00 0.00
#> 130 2 0.0000 0.973 0.00 1.00 0.00
#> 131 1 0.0000 0.985 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 2 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 3 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 4 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 5 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 6 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 7 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 8 1 0.1211 0.9002 0.96 0.00 0.04 0.00
#> 9 1 0.4277 0.5905 0.72 0.00 0.28 0.00
#> 10 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 11 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 12 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 13 3 0.0707 0.9711 0.02 0.00 0.98 0.00
#> 14 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 15 1 0.1637 0.8936 0.94 0.00 0.00 0.06
#> 16 4 0.0707 0.8088 0.00 0.02 0.00 0.98
#> 17 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 18 4 0.1637 0.7842 0.00 0.06 0.00 0.94
#> 19 3 0.2830 0.8973 0.00 0.06 0.90 0.04
#> 20 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 21 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 22 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 23 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 24 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 25 4 0.1211 0.8030 0.04 0.00 0.00 0.96
#> 26 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 27 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 28 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 29 1 0.4948 0.1907 0.56 0.00 0.00 0.44
#> 30 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 31 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 32 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 33 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 34 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 35 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 36 1 0.2335 0.8783 0.92 0.02 0.00 0.06
#> 37 1 0.4134 0.6494 0.74 0.00 0.00 0.26
#> 38 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 39 4 0.5000 0.0243 0.50 0.00 0.00 0.50
#> 40 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 41 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 42 1 0.4790 0.3450 0.62 0.00 0.00 0.38
#> 43 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 44 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 45 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 46 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 47 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 48 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 49 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 50 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 51 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 52 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 53 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 54 4 0.7378 0.3262 0.38 0.10 0.02 0.50
#> 55 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 56 4 0.5000 0.0397 0.50 0.00 0.00 0.50
#> 57 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 58 1 0.1637 0.8901 0.94 0.00 0.00 0.06
#> 59 1 0.0707 0.9189 0.98 0.00 0.00 0.02
#> 60 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 61 4 0.5535 0.2226 0.02 0.42 0.00 0.56
#> 62 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 63 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 64 1 0.5902 0.5757 0.70 0.14 0.00 0.16
#> 65 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 66 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 67 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 68 4 0.4284 0.6493 0.02 0.20 0.00 0.78
#> 69 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 70 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 71 1 0.3610 0.7426 0.80 0.00 0.00 0.20
#> 72 1 0.2647 0.8374 0.88 0.00 0.00 0.12
#> 73 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 74 2 0.2011 0.9036 0.00 0.92 0.00 0.08
#> 75 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 76 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 77 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 78 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 79 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 80 1 0.1211 0.9045 0.96 0.00 0.00 0.04
#> 81 1 0.5173 0.4879 0.66 0.02 0.00 0.32
#> 82 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 83 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 84 1 0.0000 0.9317 1.00 0.00 0.00 0.00
#> 85 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 86 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 87 3 0.1211 0.9470 0.04 0.00 0.96 0.00
#> 88 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 89 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 90 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 91 2 0.3801 0.7352 0.00 0.78 0.00 0.22
#> 92 4 0.4855 0.2741 0.00 0.40 0.00 0.60
#> 93 1 0.2921 0.8155 0.86 0.00 0.00 0.14
#> 94 4 0.4994 0.0707 0.48 0.00 0.00 0.52
#> 95 4 0.1637 0.7864 0.00 0.06 0.00 0.94
#> 96 4 0.2345 0.7720 0.10 0.00 0.00 0.90
#> 97 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 98 4 0.4277 0.5765 0.28 0.00 0.00 0.72
#> 99 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 100 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 101 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 102 3 0.0000 0.9928 0.00 0.00 1.00 0.00
#> 103 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 104 4 0.1211 0.7998 0.00 0.04 0.00 0.96
#> 105 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 106 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 107 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 108 1 0.3172 0.7930 0.84 0.00 0.00 0.16
#> 109 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 110 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 111 4 0.0000 0.8158 0.00 0.00 0.00 1.00
#> 112 4 0.0707 0.8088 0.00 0.02 0.00 0.98
#> 113 2 0.2345 0.8855 0.00 0.90 0.00 0.10
#> 114 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 115 2 0.2921 0.8396 0.00 0.86 0.00 0.14
#> 116 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 117 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 118 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 119 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 120 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 121 2 0.3172 0.8061 0.00 0.84 0.00 0.16
#> 122 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 123 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 124 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 125 2 0.0707 0.9468 0.00 0.98 0.00 0.02
#> 126 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 127 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 128 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 129 2 0.0000 0.9633 0.00 1.00 0.00 0.00
#> 130 2 0.3975 0.7001 0.00 0.76 0.00 0.24
#> 131 1 0.3801 0.7123 0.78 0.00 0.00 0.22
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 131 3.20e-02 2
#> ATC:skmeans 129 5.06e-05 3
#> ATC:skmeans 122 2.90e-03 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node012. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0123"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7111 rows and 140 columns.
#> Top rows (711) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.00 0.973 0.989 0.400 0.602 0.602
#> 3 3 0.77 0.957 0.966 0.582 0.741 0.579
#> 4 4 0.99 0.966 0.984 0.193 0.860 0.629
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.981 0.00 1.00
#> 2 2 0.000 0.981 0.00 1.00
#> 3 2 0.000 0.981 0.00 1.00
#> 4 2 0.000 0.981 0.00 1.00
#> 5 2 0.000 0.981 0.00 1.00
#> 6 2 0.000 0.981 0.00 1.00
#> 7 2 0.000 0.981 0.00 1.00
#> 8 2 0.000 0.981 0.00 1.00
#> 9 2 0.000 0.981 0.00 1.00
#> 10 2 0.000 0.981 0.00 1.00
#> 11 2 0.000 0.981 0.00 1.00
#> 12 2 0.000 0.981 0.00 1.00
#> 13 2 0.000 0.981 0.00 1.00
#> 14 2 0.000 0.981 0.00 1.00
#> 15 2 0.000 0.981 0.00 1.00
#> 16 2 0.000 0.981 0.00 1.00
#> 17 2 0.000 0.981 0.00 1.00
#> 18 2 0.000 0.981 0.00 1.00
#> 19 2 0.000 0.981 0.00 1.00
#> 20 2 0.000 0.981 0.00 1.00
#> 21 2 0.000 0.981 0.00 1.00
#> 22 2 0.000 0.981 0.00 1.00
#> 23 2 0.000 0.981 0.00 1.00
#> 24 2 0.000 0.981 0.00 1.00
#> 25 2 0.000 0.981 0.00 1.00
#> 26 2 0.000 0.981 0.00 1.00
#> 27 1 0.469 0.889 0.90 0.10
#> 28 1 0.000 0.991 1.00 0.00
#> 29 2 0.971 0.330 0.40 0.60
#> 30 2 0.000 0.981 0.00 1.00
#> 31 1 0.971 0.328 0.60 0.40
#> 32 2 0.000 0.981 0.00 1.00
#> 33 1 0.000 0.991 1.00 0.00
#> 34 1 0.327 0.935 0.94 0.06
#> 35 1 0.000 0.991 1.00 0.00
#> 36 1 0.242 0.955 0.96 0.04
#> 37 1 0.402 0.913 0.92 0.08
#> 38 2 0.000 0.981 0.00 1.00
#> 39 1 0.000 0.991 1.00 0.00
#> 40 2 0.000 0.981 0.00 1.00
#> 41 2 0.000 0.981 0.00 1.00
#> 42 1 0.000 0.991 1.00 0.00
#> 43 1 0.000 0.991 1.00 0.00
#> 44 2 0.000 0.981 0.00 1.00
#> 45 2 0.000 0.981 0.00 1.00
#> 46 1 0.000 0.991 1.00 0.00
#> 47 1 0.000 0.991 1.00 0.00
#> 48 1 0.000 0.991 1.00 0.00
#> 49 1 0.242 0.955 0.96 0.04
#> 50 1 0.000 0.991 1.00 0.00
#> 51 1 0.000 0.991 1.00 0.00
#> 52 2 0.855 0.610 0.28 0.72
#> 53 1 0.000 0.991 1.00 0.00
#> 54 1 0.327 0.936 0.94 0.06
#> 55 1 0.000 0.991 1.00 0.00
#> 56 2 0.000 0.981 0.00 1.00
#> 57 1 0.000 0.991 1.00 0.00
#> 58 1 0.000 0.991 1.00 0.00
#> 59 1 0.000 0.991 1.00 0.00
#> 60 1 0.000 0.991 1.00 0.00
#> 61 1 0.000 0.991 1.00 0.00
#> 62 2 0.000 0.981 0.00 1.00
#> 63 1 0.000 0.991 1.00 0.00
#> 64 1 0.000 0.991 1.00 0.00
#> 65 1 0.000 0.991 1.00 0.00
#> 66 1 0.000 0.991 1.00 0.00
#> 67 1 0.000 0.991 1.00 0.00
#> 68 1 0.000 0.991 1.00 0.00
#> 69 1 0.000 0.991 1.00 0.00
#> 70 1 0.000 0.991 1.00 0.00
#> 71 1 0.000 0.991 1.00 0.00
#> 72 1 0.000 0.991 1.00 0.00
#> 73 1 0.000 0.991 1.00 0.00
#> 74 1 0.000 0.991 1.00 0.00
#> 75 1 0.000 0.991 1.00 0.00
#> 76 1 0.000 0.991 1.00 0.00
#> 77 1 0.000 0.991 1.00 0.00
#> 78 1 0.000 0.991 1.00 0.00
#> 79 1 0.000 0.991 1.00 0.00
#> 80 1 0.000 0.991 1.00 0.00
#> 81 1 0.327 0.934 0.94 0.06
#> 82 1 0.000 0.991 1.00 0.00
#> 83 1 0.141 0.974 0.98 0.02
#> 84 1 0.000 0.991 1.00 0.00
#> 85 1 0.000 0.991 1.00 0.00
#> 86 1 0.000 0.991 1.00 0.00
#> 87 1 0.000 0.991 1.00 0.00
#> 88 1 0.000 0.991 1.00 0.00
#> 89 1 0.000 0.991 1.00 0.00
#> 90 1 0.000 0.991 1.00 0.00
#> 91 1 0.000 0.991 1.00 0.00
#> 92 1 0.000 0.991 1.00 0.00
#> 93 1 0.000 0.991 1.00 0.00
#> 94 1 0.000 0.991 1.00 0.00
#> 95 1 0.000 0.991 1.00 0.00
#> 96 1 0.000 0.991 1.00 0.00
#> 97 1 0.000 0.991 1.00 0.00
#> 98 1 0.000 0.991 1.00 0.00
#> 99 1 0.000 0.991 1.00 0.00
#> 100 1 0.000 0.991 1.00 0.00
#> 101 1 0.000 0.991 1.00 0.00
#> 102 1 0.000 0.991 1.00 0.00
#> 103 1 0.000 0.991 1.00 0.00
#> 104 2 0.000 0.981 0.00 1.00
#> 105 1 0.000 0.991 1.00 0.00
#> 106 1 0.000 0.991 1.00 0.00
#> 107 1 0.000 0.991 1.00 0.00
#> 108 1 0.000 0.991 1.00 0.00
#> 109 1 0.000 0.991 1.00 0.00
#> 110 1 0.000 0.991 1.00 0.00
#> 111 1 0.000 0.991 1.00 0.00
#> 112 1 0.000 0.991 1.00 0.00
#> 113 1 0.000 0.991 1.00 0.00
#> 114 1 0.000 0.991 1.00 0.00
#> 115 1 0.000 0.991 1.00 0.00
#> 116 1 0.000 0.991 1.00 0.00
#> 117 1 0.000 0.991 1.00 0.00
#> 118 1 0.000 0.991 1.00 0.00
#> 119 1 0.000 0.991 1.00 0.00
#> 120 1 0.000 0.991 1.00 0.00
#> 121 1 0.000 0.991 1.00 0.00
#> 122 1 0.000 0.991 1.00 0.00
#> 123 1 0.000 0.991 1.00 0.00
#> 124 1 0.000 0.991 1.00 0.00
#> 125 1 0.000 0.991 1.00 0.00
#> 126 1 0.000 0.991 1.00 0.00
#> 127 1 0.000 0.991 1.00 0.00
#> 128 1 0.141 0.974 0.98 0.02
#> 129 1 0.000 0.991 1.00 0.00
#> 130 1 0.000 0.991 1.00 0.00
#> 131 1 0.000 0.991 1.00 0.00
#> 132 1 0.000 0.991 1.00 0.00
#> 133 1 0.000 0.991 1.00 0.00
#> 134 1 0.000 0.991 1.00 0.00
#> 135 1 0.000 0.991 1.00 0.00
#> 136 1 0.000 0.991 1.00 0.00
#> 137 1 0.000 0.991 1.00 0.00
#> 138 1 0.000 0.991 1.00 0.00
#> 139 1 0.000 0.991 1.00 0.00
#> 140 1 0.000 0.991 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 2 0.0000 0.999 0.00 1.00 0.00
#> 2 2 0.0000 0.999 0.00 1.00 0.00
#> 3 2 0.0000 0.999 0.00 1.00 0.00
#> 4 2 0.0000 0.999 0.00 1.00 0.00
#> 5 2 0.0000 0.999 0.00 1.00 0.00
#> 6 2 0.0000 0.999 0.00 1.00 0.00
#> 7 2 0.0000 0.999 0.00 1.00 0.00
#> 8 2 0.0000 0.999 0.00 1.00 0.00
#> 9 2 0.0000 0.999 0.00 1.00 0.00
#> 10 2 0.0000 0.999 0.00 1.00 0.00
#> 11 2 0.0000 0.999 0.00 1.00 0.00
#> 12 2 0.0000 0.999 0.00 1.00 0.00
#> 13 2 0.0000 0.999 0.00 1.00 0.00
#> 14 2 0.0000 0.999 0.00 1.00 0.00
#> 15 2 0.0000 0.999 0.00 1.00 0.00
#> 16 2 0.0000 0.999 0.00 1.00 0.00
#> 17 2 0.0000 0.999 0.00 1.00 0.00
#> 18 2 0.0000 0.999 0.00 1.00 0.00
#> 19 2 0.0000 0.999 0.00 1.00 0.00
#> 20 2 0.0000 0.999 0.00 1.00 0.00
#> 21 2 0.0000 0.999 0.00 1.00 0.00
#> 22 2 0.0000 0.999 0.00 1.00 0.00
#> 23 2 0.0000 0.999 0.00 1.00 0.00
#> 24 2 0.0000 0.999 0.00 1.00 0.00
#> 25 2 0.0000 0.999 0.00 1.00 0.00
#> 26 2 0.0000 0.999 0.00 1.00 0.00
#> 27 1 0.3686 0.912 0.86 0.00 0.14
#> 28 1 0.3686 0.912 0.86 0.00 0.14
#> 29 1 0.5159 0.885 0.82 0.04 0.14
#> 30 2 0.0000 0.999 0.00 1.00 0.00
#> 31 1 0.4209 0.912 0.86 0.02 0.12
#> 32 2 0.0892 0.975 0.02 0.98 0.00
#> 33 1 0.3686 0.912 0.86 0.00 0.14
#> 34 1 0.3686 0.912 0.86 0.00 0.14
#> 35 1 0.3686 0.912 0.86 0.00 0.14
#> 36 1 0.3686 0.912 0.86 0.00 0.14
#> 37 1 0.3686 0.912 0.86 0.00 0.14
#> 38 2 0.0000 0.999 0.00 1.00 0.00
#> 39 1 0.3686 0.912 0.86 0.00 0.14
#> 40 2 0.0000 0.999 0.00 1.00 0.00
#> 41 2 0.0000 0.999 0.00 1.00 0.00
#> 42 1 0.3686 0.912 0.86 0.00 0.14
#> 43 1 0.3340 0.918 0.88 0.00 0.12
#> 44 2 0.0000 0.999 0.00 1.00 0.00
#> 45 2 0.0000 0.999 0.00 1.00 0.00
#> 46 1 0.0000 0.930 1.00 0.00 0.00
#> 47 1 0.0892 0.922 0.98 0.00 0.02
#> 48 1 0.0000 0.930 1.00 0.00 0.00
#> 49 1 0.3340 0.918 0.88 0.00 0.12
#> 50 1 0.0000 0.930 1.00 0.00 0.00
#> 51 1 0.3686 0.912 0.86 0.00 0.14
#> 52 1 0.2066 0.908 0.94 0.06 0.00
#> 53 1 0.3686 0.912 0.86 0.00 0.14
#> 54 1 0.3686 0.912 0.86 0.00 0.14
#> 55 1 0.3686 0.912 0.86 0.00 0.14
#> 56 2 0.0000 0.999 0.00 1.00 0.00
#> 57 1 0.3686 0.912 0.86 0.00 0.14
#> 58 1 0.3686 0.912 0.86 0.00 0.14
#> 59 1 0.3686 0.912 0.86 0.00 0.14
#> 60 1 0.3686 0.912 0.86 0.00 0.14
#> 61 1 0.3686 0.912 0.86 0.00 0.14
#> 62 2 0.0000 0.999 0.00 1.00 0.00
#> 63 1 0.2537 0.925 0.92 0.00 0.08
#> 64 1 0.2959 0.922 0.90 0.00 0.10
#> 65 1 0.3686 0.912 0.86 0.00 0.14
#> 66 1 0.0000 0.930 1.00 0.00 0.00
#> 67 1 0.0000 0.930 1.00 0.00 0.00
#> 68 1 0.0000 0.930 1.00 0.00 0.00
#> 69 1 0.0000 0.930 1.00 0.00 0.00
#> 70 1 0.0000 0.930 1.00 0.00 0.00
#> 71 1 0.0000 0.930 1.00 0.00 0.00
#> 72 1 0.0000 0.930 1.00 0.00 0.00
#> 73 1 0.0000 0.930 1.00 0.00 0.00
#> 74 1 0.0000 0.930 1.00 0.00 0.00
#> 75 1 0.0000 0.930 1.00 0.00 0.00
#> 76 1 0.0000 0.930 1.00 0.00 0.00
#> 77 1 0.0000 0.930 1.00 0.00 0.00
#> 78 1 0.0000 0.930 1.00 0.00 0.00
#> 79 1 0.0000 0.930 1.00 0.00 0.00
#> 80 1 0.0000 0.930 1.00 0.00 0.00
#> 81 1 0.0000 0.930 1.00 0.00 0.00
#> 82 1 0.0000 0.930 1.00 0.00 0.00
#> 83 1 0.2959 0.922 0.90 0.00 0.10
#> 84 1 0.0000 0.930 1.00 0.00 0.00
#> 85 1 0.0000 0.930 1.00 0.00 0.00
#> 86 1 0.0000 0.930 1.00 0.00 0.00
#> 87 1 0.0000 0.930 1.00 0.00 0.00
#> 88 1 0.0000 0.930 1.00 0.00 0.00
#> 89 1 0.0000 0.930 1.00 0.00 0.00
#> 90 1 0.0000 0.930 1.00 0.00 0.00
#> 91 1 0.0000 0.930 1.00 0.00 0.00
#> 92 1 0.0000 0.930 1.00 0.00 0.00
#> 93 1 0.0000 0.930 1.00 0.00 0.00
#> 94 1 0.0000 0.930 1.00 0.00 0.00
#> 95 1 0.0000 0.930 1.00 0.00 0.00
#> 96 1 0.3686 0.912 0.86 0.00 0.14
#> 97 3 0.0000 0.995 0.00 0.00 1.00
#> 98 3 0.0000 0.995 0.00 0.00 1.00
#> 99 3 0.0000 0.995 0.00 0.00 1.00
#> 100 3 0.0000 0.995 0.00 0.00 1.00
#> 101 3 0.0000 0.995 0.00 0.00 1.00
#> 102 3 0.0000 0.995 0.00 0.00 1.00
#> 103 3 0.0000 0.995 0.00 0.00 1.00
#> 104 2 0.0000 0.999 0.00 1.00 0.00
#> 105 3 0.0000 0.995 0.00 0.00 1.00
#> 106 3 0.0000 0.995 0.00 0.00 1.00
#> 107 3 0.0000 0.995 0.00 0.00 1.00
#> 108 3 0.0000 0.995 0.00 0.00 1.00
#> 109 3 0.0000 0.995 0.00 0.00 1.00
#> 110 3 0.0000 0.995 0.00 0.00 1.00
#> 111 3 0.0000 0.995 0.00 0.00 1.00
#> 112 3 0.0000 0.995 0.00 0.00 1.00
#> 113 3 0.0000 0.995 0.00 0.00 1.00
#> 114 3 0.0000 0.995 0.00 0.00 1.00
#> 115 3 0.0000 0.995 0.00 0.00 1.00
#> 116 3 0.0000 0.995 0.00 0.00 1.00
#> 117 3 0.0000 0.995 0.00 0.00 1.00
#> 118 3 0.0000 0.995 0.00 0.00 1.00
#> 119 3 0.0000 0.995 0.00 0.00 1.00
#> 120 3 0.0000 0.995 0.00 0.00 1.00
#> 121 3 0.0000 0.995 0.00 0.00 1.00
#> 122 3 0.0000 0.995 0.00 0.00 1.00
#> 123 3 0.0000 0.995 0.00 0.00 1.00
#> 124 3 0.0000 0.995 0.00 0.00 1.00
#> 125 3 0.0000 0.995 0.00 0.00 1.00
#> 126 3 0.0000 0.995 0.00 0.00 1.00
#> 127 3 0.0000 0.995 0.00 0.00 1.00
#> 128 1 0.2959 0.910 0.90 0.00 0.10
#> 129 3 0.4002 0.812 0.16 0.00 0.84
#> 130 1 0.1529 0.911 0.96 0.00 0.04
#> 131 3 0.0000 0.995 0.00 0.00 1.00
#> 132 3 0.0000 0.995 0.00 0.00 1.00
#> 133 3 0.0000 0.995 0.00 0.00 1.00
#> 134 1 0.3686 0.912 0.86 0.00 0.14
#> 135 1 0.3686 0.912 0.86 0.00 0.14
#> 136 1 0.0000 0.930 1.00 0.00 0.00
#> 137 1 0.3340 0.918 0.88 0.00 0.12
#> 138 1 0.3340 0.918 0.88 0.00 0.12
#> 139 1 0.3340 0.918 0.88 0.00 0.12
#> 140 1 0.3686 0.912 0.86 0.00 0.14
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 2 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 3 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 4 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 5 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 6 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 7 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 8 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 9 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 10 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 11 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 12 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 13 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 14 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 15 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 16 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 17 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 18 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 19 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 20 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 21 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 22 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 23 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 24 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 25 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 26 2 0.0707 0.973 0.02 0.98 0.00 0.00
#> 27 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 28 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 29 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 30 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 31 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 32 1 0.4907 0.261 0.58 0.42 0.00 0.00
#> 33 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 34 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 35 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 36 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 37 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 38 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 39 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 40 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 41 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 42 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 43 1 0.1211 0.947 0.96 0.00 0.00 0.04
#> 44 2 0.2647 0.860 0.12 0.88 0.00 0.00
#> 45 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 46 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 47 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 48 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 49 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 50 4 0.2011 0.904 0.08 0.00 0.00 0.92
#> 51 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 52 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 53 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 54 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 55 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 56 2 0.3975 0.683 0.24 0.76 0.00 0.00
#> 57 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 58 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 59 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 60 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 61 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 62 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 63 1 0.4790 0.412 0.62 0.00 0.00 0.38
#> 64 1 0.2647 0.868 0.88 0.00 0.00 0.12
#> 65 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 66 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 67 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 68 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 69 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 70 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 71 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 72 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 73 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 74 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 75 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 76 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 77 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 78 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 79 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 80 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 81 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 82 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 83 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 84 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 85 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 86 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 87 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 88 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 89 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 90 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 91 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 92 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 93 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 94 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 95 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 96 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 97 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 98 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 99 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 100 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 101 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 102 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 103 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 104 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> 105 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 106 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 107 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 108 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 109 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 110 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 111 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 112 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 113 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 114 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 115 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 116 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 117 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 118 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 119 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 120 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 121 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 122 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 123 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 124 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 125 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 126 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 127 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 128 1 0.3335 0.838 0.86 0.00 0.02 0.12
#> 129 4 0.2345 0.884 0.00 0.00 0.10 0.90
#> 130 4 0.0000 0.987 0.00 0.00 0.00 1.00
#> 131 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 132 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 133 3 0.0000 1.000 0.00 0.00 1.00 0.00
#> 134 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 135 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 136 4 0.4134 0.635 0.26 0.00 0.00 0.74
#> 137 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 138 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 139 1 0.0707 0.962 0.98 0.00 0.00 0.02
#> 140 1 0.0707 0.962 0.98 0.00 0.00 0.02
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 138 5.63e-15 2
#> ATC:skmeans 140 1.82e-35 3
#> ATC:skmeans 138 2.22e-45 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["013"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 8520 rows and 201 columns.
#> Top rows (852) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.946 0.951 0.979 0.495 0.507 0.507
#> 3 3 0.780 0.869 0.936 0.303 0.797 0.619
#> 4 4 0.742 0.763 0.894 0.105 0.889 0.706
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.980 0.00 1.00
#> 2 2 0.000 0.980 0.00 1.00
#> 3 2 0.000 0.980 0.00 1.00
#> 4 2 0.000 0.980 0.00 1.00
#> 5 2 0.000 0.980 0.00 1.00
#> 6 1 0.000 0.977 1.00 0.00
#> 7 1 0.141 0.961 0.98 0.02
#> 8 2 0.000 0.980 0.00 1.00
#> 9 2 0.000 0.980 0.00 1.00
#> 10 2 0.000 0.980 0.00 1.00
#> 11 1 0.000 0.977 1.00 0.00
#> 12 1 0.327 0.925 0.94 0.06
#> 13 1 0.402 0.904 0.92 0.08
#> 14 2 0.000 0.980 0.00 1.00
#> 15 2 0.000 0.980 0.00 1.00
#> 16 2 0.000 0.980 0.00 1.00
#> 17 2 0.000 0.980 0.00 1.00
#> 18 2 0.000 0.980 0.00 1.00
#> 19 2 0.000 0.980 0.00 1.00
#> 20 2 0.000 0.980 0.00 1.00
#> 21 2 0.000 0.980 0.00 1.00
#> 22 2 0.000 0.980 0.00 1.00
#> 23 2 0.000 0.980 0.00 1.00
#> 24 2 0.000 0.980 0.00 1.00
#> 25 2 0.000 0.980 0.00 1.00
#> 26 2 0.000 0.980 0.00 1.00
#> 27 1 0.925 0.491 0.66 0.34
#> 28 2 0.000 0.980 0.00 1.00
#> 29 1 0.000 0.977 1.00 0.00
#> 30 1 0.000 0.977 1.00 0.00
#> 31 2 0.000 0.980 0.00 1.00
#> 32 1 0.242 0.944 0.96 0.04
#> 33 1 0.971 0.342 0.60 0.40
#> 34 1 0.000 0.977 1.00 0.00
#> 35 2 0.000 0.980 0.00 1.00
#> 36 2 0.000 0.980 0.00 1.00
#> 37 2 0.000 0.980 0.00 1.00
#> 38 2 0.000 0.980 0.00 1.00
#> 39 2 0.000 0.980 0.00 1.00
#> 40 1 0.000 0.977 1.00 0.00
#> 41 2 0.000 0.980 0.00 1.00
#> 42 1 0.000 0.977 1.00 0.00
#> 43 1 0.000 0.977 1.00 0.00
#> 44 2 0.000 0.980 0.00 1.00
#> 45 2 0.141 0.963 0.02 0.98
#> 46 2 0.469 0.886 0.10 0.90
#> 47 1 0.722 0.752 0.80 0.20
#> 48 2 0.000 0.980 0.00 1.00
#> 49 1 0.000 0.977 1.00 0.00
#> 50 1 0.925 0.493 0.66 0.34
#> 51 2 0.000 0.980 0.00 1.00
#> 52 2 0.529 0.862 0.12 0.88
#> 53 2 0.000 0.980 0.00 1.00
#> 54 2 0.000 0.980 0.00 1.00
#> 55 2 0.000 0.980 0.00 1.00
#> 56 2 0.000 0.980 0.00 1.00
#> 57 1 0.634 0.809 0.84 0.16
#> 58 2 0.000 0.980 0.00 1.00
#> 59 2 0.000 0.980 0.00 1.00
#> 60 2 0.000 0.980 0.00 1.00
#> 61 1 0.000 0.977 1.00 0.00
#> 62 1 0.000 0.977 1.00 0.00
#> 63 2 0.000 0.980 0.00 1.00
#> 64 1 0.000 0.977 1.00 0.00
#> 65 1 0.000 0.977 1.00 0.00
#> 66 2 0.000 0.980 0.00 1.00
#> 67 1 0.000 0.977 1.00 0.00
#> 68 1 0.000 0.977 1.00 0.00
#> 69 2 0.000 0.980 0.00 1.00
#> 70 1 0.469 0.882 0.90 0.10
#> 71 1 0.634 0.809 0.84 0.16
#> 72 1 0.000 0.977 1.00 0.00
#> 73 2 0.680 0.783 0.18 0.82
#> 74 1 0.000 0.977 1.00 0.00
#> 75 2 0.000 0.980 0.00 1.00
#> 76 2 0.000 0.980 0.00 1.00
#> 77 2 0.000 0.980 0.00 1.00
#> 78 2 0.000 0.980 0.00 1.00
#> 79 1 0.141 0.961 0.98 0.02
#> 80 1 0.000 0.977 1.00 0.00
#> 81 2 0.000 0.980 0.00 1.00
#> 82 1 0.000 0.977 1.00 0.00
#> 83 2 0.000 0.980 0.00 1.00
#> 84 2 0.000 0.980 0.00 1.00
#> 85 2 0.141 0.963 0.02 0.98
#> 86 2 0.000 0.980 0.00 1.00
#> 87 2 0.000 0.980 0.00 1.00
#> 88 2 0.141 0.963 0.02 0.98
#> 89 1 0.000 0.977 1.00 0.00
#> 90 1 0.000 0.977 1.00 0.00
#> 91 2 0.795 0.688 0.24 0.76
#> 92 2 0.000 0.980 0.00 1.00
#> 93 1 0.000 0.977 1.00 0.00
#> 94 1 0.000 0.977 1.00 0.00
#> 95 1 0.000 0.977 1.00 0.00
#> 96 1 0.000 0.977 1.00 0.00
#> 97 1 0.000 0.977 1.00 0.00
#> 98 1 0.000 0.977 1.00 0.00
#> 99 1 0.000 0.977 1.00 0.00
#> 100 1 0.000 0.977 1.00 0.00
#> 101 1 0.000 0.977 1.00 0.00
#> 102 1 0.000 0.977 1.00 0.00
#> 103 1 0.000 0.977 1.00 0.00
#> 104 1 0.000 0.977 1.00 0.00
#> 105 1 0.000 0.977 1.00 0.00
#> 106 1 0.000 0.977 1.00 0.00
#> 107 1 0.000 0.977 1.00 0.00
#> 108 1 0.000 0.977 1.00 0.00
#> 109 1 0.000 0.977 1.00 0.00
#> 110 2 0.000 0.980 0.00 1.00
#> 111 1 0.000 0.977 1.00 0.00
#> 112 1 0.000 0.977 1.00 0.00
#> 113 1 0.000 0.977 1.00 0.00
#> 114 1 0.000 0.977 1.00 0.00
#> 115 1 0.000 0.977 1.00 0.00
#> 116 1 0.000 0.977 1.00 0.00
#> 117 1 0.000 0.977 1.00 0.00
#> 118 1 0.000 0.977 1.00 0.00
#> 119 1 0.000 0.977 1.00 0.00
#> 120 1 0.000 0.977 1.00 0.00
#> 121 1 0.000 0.977 1.00 0.00
#> 122 1 0.000 0.977 1.00 0.00
#> 123 1 0.000 0.977 1.00 0.00
#> 124 1 0.000 0.977 1.00 0.00
#> 125 1 0.000 0.977 1.00 0.00
#> 126 1 0.000 0.977 1.00 0.00
#> 127 1 0.242 0.944 0.96 0.04
#> 128 2 0.722 0.754 0.20 0.80
#> 129 1 0.141 0.961 0.98 0.02
#> 130 1 0.000 0.977 1.00 0.00
#> 131 1 0.000 0.977 1.00 0.00
#> 132 1 0.000 0.977 1.00 0.00
#> 133 1 0.000 0.977 1.00 0.00
#> 134 1 0.000 0.977 1.00 0.00
#> 135 1 0.000 0.977 1.00 0.00
#> 136 2 0.680 0.786 0.18 0.82
#> 137 2 0.000 0.980 0.00 1.00
#> 138 1 0.000 0.977 1.00 0.00
#> 139 2 0.000 0.980 0.00 1.00
#> 140 1 0.000 0.977 1.00 0.00
#> 141 1 0.000 0.977 1.00 0.00
#> 142 1 0.000 0.977 1.00 0.00
#> 143 2 0.995 0.139 0.46 0.54
#> 144 1 0.000 0.977 1.00 0.00
#> 145 1 0.000 0.977 1.00 0.00
#> 146 1 0.000 0.977 1.00 0.00
#> 147 1 0.000 0.977 1.00 0.00
#> 148 1 0.000 0.977 1.00 0.00
#> 149 1 0.000 0.977 1.00 0.00
#> 150 1 0.000 0.977 1.00 0.00
#> 151 1 0.000 0.977 1.00 0.00
#> 152 1 0.000 0.977 1.00 0.00
#> 153 2 0.000 0.980 0.00 1.00
#> 154 1 0.000 0.977 1.00 0.00
#> 155 1 0.000 0.977 1.00 0.00
#> 156 2 0.402 0.907 0.08 0.92
#> 157 1 0.000 0.977 1.00 0.00
#> 158 1 0.000 0.977 1.00 0.00
#> 159 2 0.000 0.980 0.00 1.00
#> 160 2 0.000 0.980 0.00 1.00
#> 161 2 0.000 0.980 0.00 1.00
#> 162 1 0.000 0.977 1.00 0.00
#> 163 1 0.000 0.977 1.00 0.00
#> 164 2 0.000 0.980 0.00 1.00
#> 165 2 0.402 0.907 0.08 0.92
#> 166 1 0.000 0.977 1.00 0.00
#> 167 1 0.000 0.977 1.00 0.00
#> 168 1 0.000 0.977 1.00 0.00
#> 169 1 0.995 0.158 0.54 0.46
#> 170 1 0.000 0.977 1.00 0.00
#> 171 1 0.000 0.977 1.00 0.00
#> 172 1 0.141 0.961 0.98 0.02
#> 173 1 0.000 0.977 1.00 0.00
#> 174 1 0.000 0.977 1.00 0.00
#> 175 1 0.000 0.977 1.00 0.00
#> 176 1 0.000 0.977 1.00 0.00
#> 177 1 0.000 0.977 1.00 0.00
#> 178 1 0.000 0.977 1.00 0.00
#> 179 1 0.000 0.977 1.00 0.00
#> 180 1 0.000 0.977 1.00 0.00
#> 181 2 0.000 0.980 0.00 1.00
#> 182 1 0.000 0.977 1.00 0.00
#> 183 1 0.000 0.977 1.00 0.00
#> 184 2 0.000 0.980 0.00 1.00
#> 185 2 0.000 0.980 0.00 1.00
#> 186 2 0.000 0.980 0.00 1.00
#> 187 2 0.000 0.980 0.00 1.00
#> 188 2 0.000 0.980 0.00 1.00
#> 189 2 0.000 0.980 0.00 1.00
#> 190 2 0.000 0.980 0.00 1.00
#> 191 2 0.000 0.980 0.00 1.00
#> 192 2 0.000 0.980 0.00 1.00
#> 193 2 0.000 0.980 0.00 1.00
#> 194 2 0.000 0.980 0.00 1.00
#> 195 1 0.000 0.977 1.00 0.00
#> 196 2 0.000 0.980 0.00 1.00
#> 197 1 0.000 0.977 1.00 0.00
#> 198 2 0.000 0.980 0.00 1.00
#> 199 1 0.242 0.944 0.96 0.04
#> 200 1 0.000 0.977 1.00 0.00
#> 201 2 0.000 0.980 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 2 0.0892 0.9255 0.00 0.98 0.02
#> 2 2 0.0000 0.9299 0.00 1.00 0.00
#> 3 2 0.0000 0.9299 0.00 1.00 0.00
#> 4 2 0.0000 0.9299 0.00 1.00 0.00
#> 5 2 0.0000 0.9299 0.00 1.00 0.00
#> 6 1 0.3686 0.8321 0.86 0.00 0.14
#> 7 1 0.1781 0.9311 0.96 0.02 0.02
#> 8 2 0.0000 0.9299 0.00 1.00 0.00
#> 9 2 0.0000 0.9299 0.00 1.00 0.00
#> 10 2 0.2959 0.8670 0.00 0.90 0.10
#> 11 3 0.2537 0.8500 0.08 0.00 0.92
#> 12 3 0.1529 0.8637 0.04 0.00 0.96
#> 13 3 0.1529 0.8657 0.04 0.00 0.96
#> 14 2 0.0892 0.9255 0.00 0.98 0.02
#> 15 2 0.0000 0.9299 0.00 1.00 0.00
#> 16 2 0.0000 0.9299 0.00 1.00 0.00
#> 17 2 0.2066 0.8994 0.00 0.94 0.06
#> 18 3 0.2959 0.8293 0.00 0.10 0.90
#> 19 2 0.0000 0.9299 0.00 1.00 0.00
#> 20 2 0.0000 0.9299 0.00 1.00 0.00
#> 21 2 0.1529 0.9111 0.00 0.96 0.04
#> 22 2 0.0000 0.9299 0.00 1.00 0.00
#> 23 2 0.2537 0.8839 0.00 0.92 0.08
#> 24 2 0.2959 0.8673 0.00 0.90 0.10
#> 25 2 0.0892 0.9255 0.00 0.98 0.02
#> 26 2 0.0892 0.9255 0.00 0.98 0.02
#> 27 2 0.9930 -0.0765 0.34 0.38 0.28
#> 28 2 0.1529 0.9111 0.00 0.96 0.04
#> 29 1 0.3686 0.8288 0.86 0.00 0.14
#> 30 3 0.0892 0.8645 0.00 0.02 0.98
#> 31 3 0.6126 0.3822 0.00 0.40 0.60
#> 32 3 0.0892 0.8645 0.00 0.02 0.98
#> 33 3 0.6176 0.7822 0.12 0.10 0.78
#> 34 3 0.3340 0.8252 0.12 0.00 0.88
#> 35 2 0.0000 0.9299 0.00 1.00 0.00
#> 36 2 0.0000 0.9299 0.00 1.00 0.00
#> 37 2 0.1529 0.9222 0.00 0.96 0.04
#> 38 2 0.0892 0.9255 0.00 0.98 0.02
#> 39 3 0.5216 0.6600 0.00 0.26 0.74
#> 40 3 0.3340 0.8252 0.12 0.00 0.88
#> 41 2 0.0892 0.9219 0.00 0.98 0.02
#> 42 3 0.2537 0.8497 0.08 0.00 0.92
#> 43 3 0.0892 0.8670 0.02 0.00 0.98
#> 44 2 0.0000 0.9299 0.00 1.00 0.00
#> 45 3 0.1529 0.8609 0.00 0.04 0.96
#> 46 3 0.0892 0.8645 0.00 0.02 0.98
#> 47 1 0.7424 0.4775 0.64 0.30 0.06
#> 48 3 0.6244 0.2605 0.00 0.44 0.56
#> 49 1 0.0000 0.9628 1.00 0.00 0.00
#> 50 3 0.6793 0.7514 0.16 0.10 0.74
#> 51 2 0.6126 0.3047 0.00 0.60 0.40
#> 52 3 0.0892 0.8645 0.00 0.02 0.98
#> 53 3 0.5560 0.6021 0.00 0.30 0.70
#> 54 2 0.0892 0.9255 0.00 0.98 0.02
#> 55 2 0.0892 0.9255 0.00 0.98 0.02
#> 56 2 0.2537 0.8727 0.00 0.92 0.08
#> 57 3 0.0892 0.8645 0.00 0.02 0.98
#> 58 2 0.0000 0.9299 0.00 1.00 0.00
#> 59 2 0.2959 0.8665 0.00 0.90 0.10
#> 60 2 0.2066 0.8983 0.00 0.94 0.06
#> 61 1 0.0892 0.9479 0.98 0.00 0.02
#> 62 1 0.4002 0.8063 0.84 0.00 0.16
#> 63 2 0.0000 0.9299 0.00 1.00 0.00
#> 64 3 0.0892 0.8670 0.02 0.00 0.98
#> 65 3 0.0892 0.8670 0.02 0.00 0.98
#> 66 2 0.3340 0.8458 0.00 0.88 0.12
#> 67 3 0.3686 0.8077 0.14 0.00 0.86
#> 68 3 0.0892 0.8670 0.02 0.00 0.98
#> 69 2 0.4796 0.7113 0.00 0.78 0.22
#> 70 3 0.0892 0.8645 0.00 0.02 0.98
#> 71 3 0.0892 0.8645 0.00 0.02 0.98
#> 72 1 0.2066 0.9159 0.94 0.00 0.06
#> 73 3 0.3686 0.7992 0.00 0.14 0.86
#> 74 3 0.3340 0.8284 0.12 0.00 0.88
#> 75 2 0.2537 0.8839 0.00 0.92 0.08
#> 76 2 0.0000 0.9299 0.00 1.00 0.00
#> 77 3 0.5835 0.5222 0.00 0.34 0.66
#> 78 2 0.0892 0.9255 0.00 0.98 0.02
#> 79 3 0.4002 0.7961 0.16 0.00 0.84
#> 80 3 0.0892 0.8670 0.02 0.00 0.98
#> 81 2 0.2537 0.8846 0.00 0.92 0.08
#> 82 1 0.5016 0.6818 0.76 0.00 0.24
#> 83 2 0.5835 0.4678 0.00 0.66 0.34
#> 84 2 0.5016 0.6787 0.00 0.76 0.24
#> 85 3 0.2959 0.8295 0.00 0.10 0.90
#> 86 2 0.0000 0.9299 0.00 1.00 0.00
#> 87 3 0.2066 0.8521 0.00 0.06 0.94
#> 88 3 0.2959 0.8294 0.00 0.10 0.90
#> 89 1 0.0000 0.9628 1.00 0.00 0.00
#> 90 1 0.0000 0.9628 1.00 0.00 0.00
#> 91 2 0.7074 0.0527 0.48 0.50 0.02
#> 92 2 0.0892 0.9255 0.00 0.98 0.02
#> 93 1 0.0000 0.9628 1.00 0.00 0.00
#> 94 1 0.0000 0.9628 1.00 0.00 0.00
#> 95 3 0.1529 0.8656 0.04 0.00 0.96
#> 96 1 0.5948 0.4337 0.64 0.00 0.36
#> 97 1 0.0000 0.9628 1.00 0.00 0.00
#> 98 1 0.0000 0.9628 1.00 0.00 0.00
#> 99 1 0.0000 0.9628 1.00 0.00 0.00
#> 100 1 0.0000 0.9628 1.00 0.00 0.00
#> 101 1 0.0000 0.9628 1.00 0.00 0.00
#> 102 1 0.0000 0.9628 1.00 0.00 0.00
#> 103 1 0.0000 0.9628 1.00 0.00 0.00
#> 104 1 0.0000 0.9628 1.00 0.00 0.00
#> 105 1 0.0000 0.9628 1.00 0.00 0.00
#> 106 1 0.0000 0.9628 1.00 0.00 0.00
#> 107 1 0.0000 0.9628 1.00 0.00 0.00
#> 108 1 0.0000 0.9628 1.00 0.00 0.00
#> 109 1 0.0000 0.9628 1.00 0.00 0.00
#> 110 2 0.0892 0.9255 0.00 0.98 0.02
#> 111 1 0.0000 0.9628 1.00 0.00 0.00
#> 112 1 0.0000 0.9628 1.00 0.00 0.00
#> 113 1 0.0000 0.9628 1.00 0.00 0.00
#> 114 1 0.0000 0.9628 1.00 0.00 0.00
#> 115 1 0.0000 0.9628 1.00 0.00 0.00
#> 116 3 0.5016 0.6816 0.24 0.00 0.76
#> 117 1 0.0000 0.9628 1.00 0.00 0.00
#> 118 1 0.0000 0.9628 1.00 0.00 0.00
#> 119 1 0.0000 0.9628 1.00 0.00 0.00
#> 120 1 0.0000 0.9628 1.00 0.00 0.00
#> 121 1 0.0000 0.9628 1.00 0.00 0.00
#> 122 3 0.2959 0.8393 0.10 0.00 0.90
#> 123 1 0.1529 0.9331 0.96 0.00 0.04
#> 124 1 0.0892 0.9484 0.98 0.00 0.02
#> 125 1 0.0000 0.9628 1.00 0.00 0.00
#> 126 1 0.0000 0.9628 1.00 0.00 0.00
#> 127 1 0.3415 0.8661 0.90 0.08 0.02
#> 128 3 0.7208 0.5136 0.04 0.34 0.62
#> 129 3 0.0892 0.8670 0.02 0.00 0.98
#> 130 1 0.0000 0.9628 1.00 0.00 0.00
#> 131 1 0.0000 0.9628 1.00 0.00 0.00
#> 132 1 0.0892 0.9485 0.98 0.00 0.02
#> 133 1 0.0000 0.9628 1.00 0.00 0.00
#> 134 1 0.0000 0.9628 1.00 0.00 0.00
#> 135 1 0.0000 0.9628 1.00 0.00 0.00
#> 136 2 0.5147 0.7128 0.18 0.80 0.02
#> 137 2 0.4035 0.8392 0.08 0.88 0.04
#> 138 1 0.0000 0.9628 1.00 0.00 0.00
#> 139 2 0.0000 0.9299 0.00 1.00 0.00
#> 140 1 0.5835 0.4817 0.66 0.00 0.34
#> 141 1 0.0000 0.9628 1.00 0.00 0.00
#> 142 1 0.0000 0.9628 1.00 0.00 0.00
#> 143 3 0.0892 0.8645 0.00 0.02 0.98
#> 144 1 0.0000 0.9628 1.00 0.00 0.00
#> 145 1 0.0000 0.9628 1.00 0.00 0.00
#> 146 1 0.0000 0.9628 1.00 0.00 0.00
#> 147 1 0.0000 0.9628 1.00 0.00 0.00
#> 148 1 0.0000 0.9628 1.00 0.00 0.00
#> 149 1 0.0000 0.9628 1.00 0.00 0.00
#> 150 1 0.4862 0.7820 0.82 0.02 0.16
#> 151 1 0.0000 0.9628 1.00 0.00 0.00
#> 152 1 0.0000 0.9628 1.00 0.00 0.00
#> 153 2 0.0892 0.9255 0.00 0.98 0.02
#> 154 1 0.2066 0.9155 0.94 0.00 0.06
#> 155 1 0.0000 0.9628 1.00 0.00 0.00
#> 156 2 0.4035 0.8397 0.08 0.88 0.04
#> 157 1 0.0892 0.9467 0.98 0.00 0.02
#> 158 1 0.0000 0.9628 1.00 0.00 0.00
#> 159 2 0.0892 0.9255 0.00 0.98 0.02
#> 160 2 0.0892 0.9255 0.00 0.98 0.02
#> 161 2 0.0892 0.9255 0.00 0.98 0.02
#> 162 1 0.3340 0.8518 0.88 0.00 0.12
#> 163 1 0.0000 0.9628 1.00 0.00 0.00
#> 164 2 0.0000 0.9299 0.00 1.00 0.00
#> 165 2 0.3832 0.8283 0.10 0.88 0.02
#> 166 1 0.0000 0.9628 1.00 0.00 0.00
#> 167 1 0.0000 0.9628 1.00 0.00 0.00
#> 168 1 0.0000 0.9628 1.00 0.00 0.00
#> 169 3 0.1781 0.8674 0.02 0.02 0.96
#> 170 1 0.0000 0.9628 1.00 0.00 0.00
#> 171 1 0.0000 0.9628 1.00 0.00 0.00
#> 172 1 0.5948 0.4301 0.64 0.00 0.36
#> 173 1 0.0000 0.9628 1.00 0.00 0.00
#> 174 1 0.0000 0.9628 1.00 0.00 0.00
#> 175 1 0.0000 0.9628 1.00 0.00 0.00
#> 176 1 0.0000 0.9628 1.00 0.00 0.00
#> 177 1 0.0892 0.9467 0.98 0.00 0.02
#> 178 3 0.6045 0.4066 0.38 0.00 0.62
#> 179 1 0.0000 0.9628 1.00 0.00 0.00
#> 180 1 0.0000 0.9628 1.00 0.00 0.00
#> 181 2 0.0892 0.9255 0.00 0.98 0.02
#> 182 1 0.0000 0.9628 1.00 0.00 0.00
#> 183 1 0.0000 0.9628 1.00 0.00 0.00
#> 184 2 0.0000 0.9299 0.00 1.00 0.00
#> 185 2 0.0000 0.9299 0.00 1.00 0.00
#> 186 2 0.0000 0.9299 0.00 1.00 0.00
#> 187 2 0.0000 0.9299 0.00 1.00 0.00
#> 188 2 0.2066 0.8989 0.00 0.94 0.06
#> 189 2 0.2537 0.8839 0.00 0.92 0.08
#> 190 2 0.0000 0.9299 0.00 1.00 0.00
#> 191 2 0.0892 0.9255 0.00 0.98 0.02
#> 192 2 0.0892 0.9221 0.00 0.98 0.02
#> 193 2 0.0000 0.9299 0.00 1.00 0.00
#> 194 3 0.6280 0.2171 0.00 0.46 0.54
#> 195 1 0.0000 0.9628 1.00 0.00 0.00
#> 196 2 0.0892 0.9255 0.00 0.98 0.02
#> 197 1 0.0000 0.9628 1.00 0.00 0.00
#> 198 2 0.0892 0.9255 0.00 0.98 0.02
#> 199 1 0.3832 0.8428 0.88 0.10 0.02
#> 200 1 0.0000 0.9628 1.00 0.00 0.00
#> 201 2 0.0000 0.9299 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 4 0.4277 0.6649 0.00 0.28 0.00 0.72
#> 2 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 3 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 4 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 5 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 6 1 0.3400 0.7793 0.82 0.00 0.18 0.00
#> 7 1 0.4994 0.2100 0.52 0.00 0.00 0.48
#> 8 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 9 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 10 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 11 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 12 3 0.1637 0.8088 0.00 0.00 0.94 0.06
#> 13 3 0.4522 0.5157 0.00 0.00 0.68 0.32
#> 14 2 0.4977 -0.1199 0.00 0.54 0.00 0.46
#> 15 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 16 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 17 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 18 2 0.4907 0.2530 0.00 0.58 0.42 0.00
#> 19 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 20 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 21 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 22 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 23 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 24 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 25 4 0.4277 0.6793 0.00 0.28 0.00 0.72
#> 26 4 0.4977 0.3484 0.00 0.46 0.00 0.54
#> 27 4 0.1211 0.7349 0.00 0.00 0.04 0.96
#> 28 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 29 1 0.3172 0.8047 0.84 0.00 0.16 0.00
#> 30 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 31 2 0.3975 0.6507 0.00 0.76 0.24 0.00
#> 32 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 33 3 0.6731 0.5575 0.02 0.28 0.62 0.08
#> 34 3 0.3172 0.7326 0.16 0.00 0.84 0.00
#> 35 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 36 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 37 2 0.5173 0.3918 0.00 0.66 0.02 0.32
#> 38 4 0.4994 0.2940 0.00 0.48 0.00 0.52
#> 39 2 0.4948 0.2071 0.00 0.56 0.44 0.00
#> 40 3 0.0707 0.8301 0.02 0.00 0.98 0.00
#> 41 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 42 3 0.0707 0.8298 0.02 0.00 0.98 0.00
#> 43 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 44 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 45 3 0.4406 0.5616 0.00 0.30 0.70 0.00
#> 46 3 0.0707 0.8279 0.00 0.02 0.98 0.00
#> 47 4 0.0000 0.7488 0.00 0.00 0.00 1.00
#> 48 2 0.7206 0.0338 0.00 0.46 0.40 0.14
#> 49 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 50 3 0.8406 0.4939 0.18 0.20 0.54 0.08
#> 51 2 0.2647 0.7734 0.00 0.88 0.12 0.00
#> 52 3 0.0707 0.8274 0.00 0.02 0.98 0.00
#> 53 2 0.4977 0.1311 0.00 0.54 0.46 0.00
#> 54 2 0.4855 0.1293 0.00 0.60 0.00 0.40
#> 55 2 0.4522 0.3972 0.00 0.68 0.00 0.32
#> 56 2 0.7581 -0.0665 0.00 0.44 0.20 0.36
#> 57 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 58 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 59 2 0.2335 0.8109 0.00 0.92 0.06 0.02
#> 60 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 61 1 0.1211 0.9109 0.96 0.00 0.00 0.04
#> 62 1 0.4522 0.5567 0.68 0.00 0.32 0.00
#> 63 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 64 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 65 3 0.0707 0.8301 0.02 0.00 0.98 0.00
#> 66 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 67 3 0.3610 0.6881 0.20 0.00 0.80 0.00
#> 68 3 0.0707 0.8299 0.02 0.00 0.98 0.00
#> 69 2 0.1211 0.8362 0.00 0.96 0.04 0.00
#> 70 3 0.0707 0.8262 0.00 0.02 0.98 0.00
#> 71 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 72 1 0.2647 0.8465 0.88 0.00 0.12 0.00
#> 73 3 0.3975 0.6558 0.00 0.24 0.76 0.00
#> 74 3 0.4227 0.7366 0.12 0.00 0.82 0.06
#> 75 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 76 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 77 2 0.4855 0.3218 0.00 0.60 0.40 0.00
#> 78 4 0.4713 0.5757 0.00 0.36 0.00 0.64
#> 79 3 0.4939 0.6487 0.22 0.04 0.74 0.00
#> 80 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 81 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 82 1 0.4624 0.5153 0.66 0.00 0.34 0.00
#> 83 2 0.3821 0.7440 0.00 0.84 0.12 0.04
#> 84 2 0.2011 0.8076 0.00 0.92 0.08 0.00
#> 85 3 0.4855 0.3433 0.00 0.40 0.60 0.00
#> 86 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 87 3 0.4277 0.5888 0.00 0.28 0.72 0.00
#> 88 2 0.5606 -0.0165 0.00 0.50 0.48 0.02
#> 89 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 90 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 91 4 0.0000 0.7488 0.00 0.00 0.00 1.00
#> 92 4 0.3400 0.7510 0.00 0.18 0.00 0.82
#> 93 1 0.0707 0.9218 0.98 0.00 0.00 0.02
#> 94 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 95 3 0.1637 0.8104 0.06 0.00 0.94 0.00
#> 96 1 0.4977 0.1685 0.54 0.00 0.46 0.00
#> 97 1 0.0707 0.9205 0.98 0.00 0.02 0.00
#> 98 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 99 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 100 1 0.0707 0.9210 0.98 0.00 0.00 0.02
#> 101 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 102 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 103 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 104 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 105 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 106 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 107 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 108 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 109 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 110 2 0.4134 0.5585 0.00 0.74 0.00 0.26
#> 111 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 112 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 113 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 114 1 0.3400 0.7882 0.82 0.00 0.00 0.18
#> 115 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 116 3 0.2011 0.7989 0.08 0.00 0.92 0.00
#> 117 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 118 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 119 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 120 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 121 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 122 3 0.0707 0.8299 0.02 0.00 0.98 0.00
#> 123 1 0.2011 0.8803 0.92 0.00 0.08 0.00
#> 124 1 0.2011 0.8811 0.92 0.00 0.08 0.00
#> 125 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 126 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 127 4 0.3610 0.5782 0.20 0.00 0.00 0.80
#> 128 3 0.8581 0.2648 0.10 0.36 0.44 0.10
#> 129 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 130 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 131 1 0.1211 0.9095 0.96 0.00 0.00 0.04
#> 132 1 0.2345 0.8669 0.90 0.00 0.10 0.00
#> 133 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 134 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 135 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 136 4 0.0000 0.7488 0.00 0.00 0.00 1.00
#> 137 4 0.2011 0.7677 0.00 0.08 0.00 0.92
#> 138 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 139 2 0.1211 0.8357 0.00 0.96 0.00 0.04
#> 140 1 0.6005 0.0949 0.50 0.00 0.46 0.04
#> 141 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 142 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 143 3 0.0000 0.8321 0.00 0.00 1.00 0.00
#> 144 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 145 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 146 1 0.4277 0.6601 0.72 0.00 0.00 0.28
#> 147 1 0.1637 0.8976 0.94 0.00 0.00 0.06
#> 148 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 149 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 150 1 0.5422 0.6750 0.74 0.04 0.20 0.02
#> 151 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 152 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 153 4 0.1637 0.7652 0.00 0.06 0.00 0.94
#> 154 1 0.2647 0.8456 0.88 0.00 0.12 0.00
#> 155 1 0.2011 0.8801 0.92 0.00 0.08 0.00
#> 156 4 0.0000 0.7488 0.00 0.00 0.00 1.00
#> 157 4 0.2921 0.6352 0.14 0.00 0.00 0.86
#> 158 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 159 4 0.0707 0.7552 0.00 0.02 0.00 0.98
#> 160 4 0.4522 0.6347 0.00 0.32 0.00 0.68
#> 161 4 0.2921 0.7647 0.00 0.14 0.00 0.86
#> 162 1 0.5291 0.6986 0.74 0.00 0.18 0.08
#> 163 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 164 2 0.4134 0.5327 0.00 0.74 0.00 0.26
#> 165 4 0.0000 0.7488 0.00 0.00 0.00 1.00
#> 166 1 0.3335 0.8406 0.86 0.00 0.02 0.12
#> 167 1 0.3610 0.7659 0.80 0.00 0.00 0.20
#> 168 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 169 3 0.6831 0.2084 0.10 0.42 0.48 0.00
#> 170 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 171 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 172 1 0.5883 0.5137 0.64 0.00 0.30 0.06
#> 173 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 174 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 175 1 0.2335 0.8884 0.92 0.00 0.06 0.02
#> 176 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 177 4 0.4624 0.3388 0.34 0.00 0.00 0.66
#> 178 3 0.3610 0.6753 0.20 0.00 0.80 0.00
#> 179 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 180 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 181 4 0.4522 0.6346 0.00 0.32 0.00 0.68
#> 182 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 183 1 0.0000 0.9315 1.00 0.00 0.00 0.00
#> 184 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 185 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 186 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 187 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 188 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 189 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 190 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 191 4 0.4713 0.5757 0.00 0.36 0.00 0.64
#> 192 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 193 2 0.0000 0.8629 0.00 1.00 0.00 0.00
#> 194 2 0.3172 0.7420 0.00 0.84 0.16 0.00
#> 195 1 0.0707 0.9215 0.98 0.00 0.00 0.02
#> 196 4 0.3975 0.7148 0.00 0.24 0.00 0.76
#> 197 1 0.4624 0.5518 0.66 0.00 0.00 0.34
#> 198 4 0.3975 0.7132 0.00 0.24 0.00 0.76
#> 199 4 0.0000 0.7488 0.00 0.00 0.00 1.00
#> 200 1 0.0707 0.9205 0.98 0.00 0.02 0.00
#> 201 2 0.3975 0.5772 0.00 0.76 0.00 0.24
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 196 0.1663 2
#> ATC:skmeans 189 0.2321 3
#> ATC:skmeans 180 0.0307 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["014"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7603 rows and 139 columns.
#> Top rows (760) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.973 0.988 0.504 0.496 0.496
#> 3 3 1.000 0.963 0.986 0.246 0.814 0.648
#> 4 4 0.841 0.888 0.946 0.178 0.827 0.569
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.982 0.00 1.00
#> 2 1 0.000 0.994 1.00 0.00
#> 3 2 0.000 0.982 0.00 1.00
#> 4 2 0.000 0.982 0.00 1.00
#> 5 2 0.000 0.982 0.00 1.00
#> 6 1 0.000 0.994 1.00 0.00
#> 7 2 0.000 0.982 0.00 1.00
#> 8 2 0.000 0.982 0.00 1.00
#> 9 2 0.000 0.982 0.00 1.00
#> 10 2 0.000 0.982 0.00 1.00
#> 11 2 0.000 0.982 0.00 1.00
#> 12 2 0.000 0.982 0.00 1.00
#> 13 2 0.000 0.982 0.00 1.00
#> 14 2 0.000 0.982 0.00 1.00
#> 15 2 0.000 0.982 0.00 1.00
#> 16 2 0.000 0.982 0.00 1.00
#> 17 2 0.000 0.982 0.00 1.00
#> 18 2 0.141 0.967 0.02 0.98
#> 19 2 0.141 0.967 0.02 0.98
#> 20 2 0.000 0.982 0.00 1.00
#> 21 2 0.000 0.982 0.00 1.00
#> 22 2 0.000 0.982 0.00 1.00
#> 23 2 0.000 0.982 0.00 1.00
#> 24 1 0.000 0.994 1.00 0.00
#> 25 1 0.141 0.975 0.98 0.02
#> 26 1 0.000 0.994 1.00 0.00
#> 27 1 0.000 0.994 1.00 0.00
#> 28 1 0.000 0.994 1.00 0.00
#> 29 1 0.000 0.994 1.00 0.00
#> 30 1 0.000 0.994 1.00 0.00
#> 31 1 0.000 0.994 1.00 0.00
#> 32 1 0.000 0.994 1.00 0.00
#> 33 1 0.000 0.994 1.00 0.00
#> 34 1 0.000 0.994 1.00 0.00
#> 35 1 0.000 0.994 1.00 0.00
#> 36 1 0.000 0.994 1.00 0.00
#> 37 1 0.000 0.994 1.00 0.00
#> 38 2 0.141 0.967 0.02 0.98
#> 39 2 0.000 0.982 0.00 1.00
#> 40 2 0.000 0.982 0.00 1.00
#> 41 2 0.971 0.342 0.40 0.60
#> 42 2 0.000 0.982 0.00 1.00
#> 43 2 0.141 0.967 0.02 0.98
#> 44 1 0.000 0.994 1.00 0.00
#> 45 1 0.141 0.975 0.98 0.02
#> 46 1 0.000 0.994 1.00 0.00
#> 47 2 0.000 0.982 0.00 1.00
#> 48 1 0.000 0.994 1.00 0.00
#> 49 1 0.000 0.994 1.00 0.00
#> 50 2 0.000 0.982 0.00 1.00
#> 51 2 0.000 0.982 0.00 1.00
#> 52 2 0.000 0.982 0.00 1.00
#> 53 1 0.000 0.994 1.00 0.00
#> 54 2 0.000 0.982 0.00 1.00
#> 55 2 0.000 0.982 0.00 1.00
#> 56 2 0.881 0.582 0.30 0.70
#> 57 2 0.000 0.982 0.00 1.00
#> 58 1 0.000 0.994 1.00 0.00
#> 59 1 0.000 0.994 1.00 0.00
#> 60 2 0.000 0.982 0.00 1.00
#> 61 1 0.000 0.994 1.00 0.00
#> 62 2 0.000 0.982 0.00 1.00
#> 63 2 0.000 0.982 0.00 1.00
#> 64 1 0.000 0.994 1.00 0.00
#> 65 2 0.827 0.655 0.26 0.74
#> 66 2 0.000 0.982 0.00 1.00
#> 67 1 0.000 0.994 1.00 0.00
#> 68 1 0.529 0.862 0.88 0.12
#> 69 2 0.242 0.949 0.04 0.96
#> 70 2 0.000 0.982 0.00 1.00
#> 71 2 0.242 0.949 0.04 0.96
#> 72 1 0.000 0.994 1.00 0.00
#> 73 2 0.000 0.982 0.00 1.00
#> 74 1 0.000 0.994 1.00 0.00
#> 75 1 0.000 0.994 1.00 0.00
#> 76 2 0.000 0.982 0.00 1.00
#> 77 2 0.000 0.982 0.00 1.00
#> 78 1 0.000 0.994 1.00 0.00
#> 79 1 0.000 0.994 1.00 0.00
#> 80 1 0.000 0.994 1.00 0.00
#> 81 1 0.000 0.994 1.00 0.00
#> 82 1 0.000 0.994 1.00 0.00
#> 83 2 0.000 0.982 0.00 1.00
#> 84 1 0.000 0.994 1.00 0.00
#> 85 1 0.000 0.994 1.00 0.00
#> 86 1 0.000 0.994 1.00 0.00
#> 87 1 0.000 0.994 1.00 0.00
#> 88 1 0.000 0.994 1.00 0.00
#> 89 2 0.000 0.982 0.00 1.00
#> 90 2 0.327 0.929 0.06 0.94
#> 91 1 0.000 0.994 1.00 0.00
#> 92 1 0.000 0.994 1.00 0.00
#> 93 2 0.000 0.982 0.00 1.00
#> 94 1 0.000 0.994 1.00 0.00
#> 95 1 0.000 0.994 1.00 0.00
#> 96 2 0.000 0.982 0.00 1.00
#> 97 2 0.000 0.982 0.00 1.00
#> 98 2 0.000 0.982 0.00 1.00
#> 99 2 0.000 0.982 0.00 1.00
#> 100 2 0.242 0.949 0.04 0.96
#> 101 1 0.000 0.994 1.00 0.00
#> 102 1 0.000 0.994 1.00 0.00
#> 103 1 0.000 0.994 1.00 0.00
#> 104 1 0.000 0.994 1.00 0.00
#> 105 2 0.000 0.982 0.00 1.00
#> 106 1 0.000 0.994 1.00 0.00
#> 107 2 0.000 0.982 0.00 1.00
#> 108 1 0.000 0.994 1.00 0.00
#> 109 2 0.000 0.982 0.00 1.00
#> 110 1 0.000 0.994 1.00 0.00
#> 111 1 0.000 0.994 1.00 0.00
#> 112 1 0.000 0.994 1.00 0.00
#> 113 2 0.141 0.967 0.02 0.98
#> 114 1 0.000 0.994 1.00 0.00
#> 115 1 0.000 0.994 1.00 0.00
#> 116 1 0.000 0.994 1.00 0.00
#> 117 1 0.000 0.994 1.00 0.00
#> 118 1 0.000 0.994 1.00 0.00
#> 119 1 0.000 0.994 1.00 0.00
#> 120 1 0.000 0.994 1.00 0.00
#> 121 1 0.000 0.994 1.00 0.00
#> 122 1 0.000 0.994 1.00 0.00
#> 123 1 0.000 0.994 1.00 0.00
#> 124 1 0.000 0.994 1.00 0.00
#> 125 1 0.000 0.994 1.00 0.00
#> 126 2 0.000 0.982 0.00 1.00
#> 127 1 0.000 0.994 1.00 0.00
#> 128 1 0.000 0.994 1.00 0.00
#> 129 2 0.000 0.982 0.00 1.00
#> 130 2 0.000 0.982 0.00 1.00
#> 131 2 0.000 0.982 0.00 1.00
#> 132 2 0.000 0.982 0.00 1.00
#> 133 2 0.000 0.982 0.00 1.00
#> 134 2 0.000 0.982 0.00 1.00
#> 135 2 0.000 0.982 0.00 1.00
#> 136 2 0.000 0.982 0.00 1.00
#> 137 2 0.000 0.982 0.00 1.00
#> 138 1 0.000 0.994 1.00 0.00
#> 139 1 0.760 0.713 0.78 0.22
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.9954 0.00 0.00 1.00
#> 2 3 0.0000 0.9954 0.00 0.00 1.00
#> 3 3 0.0000 0.9954 0.00 0.00 1.00
#> 4 3 0.0000 0.9954 0.00 0.00 1.00
#> 5 3 0.0000 0.9954 0.00 0.00 1.00
#> 6 1 0.0000 0.9844 1.00 0.00 0.00
#> 7 2 0.0000 0.9786 0.00 1.00 0.00
#> 8 3 0.0000 0.9954 0.00 0.00 1.00
#> 9 3 0.0000 0.9954 0.00 0.00 1.00
#> 10 3 0.0000 0.9954 0.00 0.00 1.00
#> 11 3 0.0000 0.9954 0.00 0.00 1.00
#> 12 3 0.0000 0.9954 0.00 0.00 1.00
#> 13 3 0.0000 0.9954 0.00 0.00 1.00
#> 14 3 0.0000 0.9954 0.00 0.00 1.00
#> 15 3 0.0000 0.9954 0.00 0.00 1.00
#> 16 3 0.0000 0.9954 0.00 0.00 1.00
#> 17 3 0.0000 0.9954 0.00 0.00 1.00
#> 18 3 0.0000 0.9954 0.00 0.00 1.00
#> 19 3 0.0000 0.9954 0.00 0.00 1.00
#> 20 3 0.0000 0.9954 0.00 0.00 1.00
#> 21 3 0.0000 0.9954 0.00 0.00 1.00
#> 22 2 0.0000 0.9786 0.00 1.00 0.00
#> 23 2 0.0000 0.9786 0.00 1.00 0.00
#> 24 1 0.0892 0.9658 0.98 0.00 0.02
#> 25 2 0.2066 0.9117 0.06 0.94 0.00
#> 26 1 0.0000 0.9844 1.00 0.00 0.00
#> 27 1 0.0000 0.9844 1.00 0.00 0.00
#> 28 1 0.0000 0.9844 1.00 0.00 0.00
#> 29 1 0.0000 0.9844 1.00 0.00 0.00
#> 30 1 0.0000 0.9844 1.00 0.00 0.00
#> 31 1 0.0000 0.9844 1.00 0.00 0.00
#> 32 1 0.0000 0.9844 1.00 0.00 0.00
#> 33 1 0.0000 0.9844 1.00 0.00 0.00
#> 34 1 0.0892 0.9634 0.98 0.02 0.00
#> 35 1 0.0000 0.9844 1.00 0.00 0.00
#> 36 1 0.0000 0.9844 1.00 0.00 0.00
#> 37 1 0.0000 0.9844 1.00 0.00 0.00
#> 38 2 0.0000 0.9786 0.00 1.00 0.00
#> 39 2 0.0000 0.9786 0.00 1.00 0.00
#> 40 2 0.0000 0.9786 0.00 1.00 0.00
#> 41 2 0.0892 0.9577 0.02 0.98 0.00
#> 42 3 0.0000 0.9954 0.00 0.00 1.00
#> 43 2 0.0000 0.9786 0.00 1.00 0.00
#> 44 1 0.0000 0.9844 1.00 0.00 0.00
#> 45 1 0.2537 0.8948 0.92 0.08 0.00
#> 46 1 0.0000 0.9844 1.00 0.00 0.00
#> 47 2 0.0000 0.9786 0.00 1.00 0.00
#> 48 1 0.0000 0.9844 1.00 0.00 0.00
#> 49 1 0.0000 0.9844 1.00 0.00 0.00
#> 50 2 0.0000 0.9786 0.00 1.00 0.00
#> 51 3 0.2959 0.8865 0.00 0.10 0.90
#> 52 2 0.0000 0.9786 0.00 1.00 0.00
#> 53 2 0.1529 0.9352 0.04 0.96 0.00
#> 54 2 0.0000 0.9786 0.00 1.00 0.00
#> 55 2 0.0000 0.9786 0.00 1.00 0.00
#> 56 3 0.0000 0.9954 0.00 0.00 1.00
#> 57 2 0.5835 0.4778 0.00 0.66 0.34
#> 58 1 0.0000 0.9844 1.00 0.00 0.00
#> 59 2 0.6126 0.3243 0.40 0.60 0.00
#> 60 2 0.0000 0.9786 0.00 1.00 0.00
#> 61 1 0.0000 0.9844 1.00 0.00 0.00
#> 62 2 0.0000 0.9786 0.00 1.00 0.00
#> 63 2 0.0000 0.9786 0.00 1.00 0.00
#> 64 1 0.0000 0.9844 1.00 0.00 0.00
#> 65 2 0.0000 0.9786 0.00 1.00 0.00
#> 66 2 0.0000 0.9786 0.00 1.00 0.00
#> 67 1 0.0000 0.9844 1.00 0.00 0.00
#> 68 2 0.0000 0.9786 0.00 1.00 0.00
#> 69 2 0.3832 0.8608 0.02 0.88 0.10
#> 70 2 0.0000 0.9786 0.00 1.00 0.00
#> 71 2 0.0000 0.9786 0.00 1.00 0.00
#> 72 2 0.0000 0.9786 0.00 1.00 0.00
#> 73 2 0.0000 0.9786 0.00 1.00 0.00
#> 74 1 0.5016 0.6789 0.76 0.24 0.00
#> 75 1 0.0000 0.9844 1.00 0.00 0.00
#> 76 2 0.0000 0.9786 0.00 1.00 0.00
#> 77 2 0.0000 0.9786 0.00 1.00 0.00
#> 78 1 0.0000 0.9844 1.00 0.00 0.00
#> 79 1 0.0000 0.9844 1.00 0.00 0.00
#> 80 1 0.0000 0.9844 1.00 0.00 0.00
#> 81 1 0.0000 0.9844 1.00 0.00 0.00
#> 82 1 0.0000 0.9844 1.00 0.00 0.00
#> 83 2 0.0000 0.9786 0.00 1.00 0.00
#> 84 1 0.0000 0.9844 1.00 0.00 0.00
#> 85 1 0.0000 0.9844 1.00 0.00 0.00
#> 86 1 0.0000 0.9844 1.00 0.00 0.00
#> 87 1 0.0000 0.9844 1.00 0.00 0.00
#> 88 1 0.0000 0.9844 1.00 0.00 0.00
#> 89 2 0.0000 0.9786 0.00 1.00 0.00
#> 90 2 0.0000 0.9786 0.00 1.00 0.00
#> 91 1 0.0000 0.9844 1.00 0.00 0.00
#> 92 1 0.0000 0.9844 1.00 0.00 0.00
#> 93 2 0.0000 0.9786 0.00 1.00 0.00
#> 94 1 0.0000 0.9844 1.00 0.00 0.00
#> 95 1 0.0000 0.9844 1.00 0.00 0.00
#> 96 2 0.0000 0.9786 0.00 1.00 0.00
#> 97 2 0.0000 0.9786 0.00 1.00 0.00
#> 98 2 0.0000 0.9786 0.00 1.00 0.00
#> 99 2 0.0000 0.9786 0.00 1.00 0.00
#> 100 2 0.0000 0.9786 0.00 1.00 0.00
#> 101 1 0.0000 0.9844 1.00 0.00 0.00
#> 102 1 0.0000 0.9844 1.00 0.00 0.00
#> 103 1 0.0000 0.9844 1.00 0.00 0.00
#> 104 1 0.0000 0.9844 1.00 0.00 0.00
#> 105 3 0.0000 0.9954 0.00 0.00 1.00
#> 106 1 0.0000 0.9844 1.00 0.00 0.00
#> 107 2 0.0000 0.9786 0.00 1.00 0.00
#> 108 1 0.0000 0.9844 1.00 0.00 0.00
#> 109 2 0.0000 0.9786 0.00 1.00 0.00
#> 110 1 0.0000 0.9844 1.00 0.00 0.00
#> 111 1 0.0000 0.9844 1.00 0.00 0.00
#> 112 1 0.0000 0.9844 1.00 0.00 0.00
#> 113 2 0.0000 0.9786 0.00 1.00 0.00
#> 114 1 0.0000 0.9844 1.00 0.00 0.00
#> 115 1 0.0000 0.9844 1.00 0.00 0.00
#> 116 1 0.0000 0.9844 1.00 0.00 0.00
#> 117 1 0.0000 0.9844 1.00 0.00 0.00
#> 118 1 0.0000 0.9844 1.00 0.00 0.00
#> 119 1 0.0000 0.9844 1.00 0.00 0.00
#> 120 1 0.0000 0.9844 1.00 0.00 0.00
#> 121 1 0.0000 0.9844 1.00 0.00 0.00
#> 122 1 0.0000 0.9844 1.00 0.00 0.00
#> 123 1 0.0000 0.9844 1.00 0.00 0.00
#> 124 1 0.0000 0.9844 1.00 0.00 0.00
#> 125 1 0.0000 0.9844 1.00 0.00 0.00
#> 126 2 0.0000 0.9786 0.00 1.00 0.00
#> 127 1 0.0000 0.9844 1.00 0.00 0.00
#> 128 1 0.0000 0.9844 1.00 0.00 0.00
#> 129 2 0.0000 0.9786 0.00 1.00 0.00
#> 130 2 0.0000 0.9786 0.00 1.00 0.00
#> 131 2 0.0000 0.9786 0.00 1.00 0.00
#> 132 2 0.0000 0.9786 0.00 1.00 0.00
#> 133 2 0.0000 0.9786 0.00 1.00 0.00
#> 134 2 0.0000 0.9786 0.00 1.00 0.00
#> 135 2 0.0000 0.9786 0.00 1.00 0.00
#> 136 2 0.0000 0.9786 0.00 1.00 0.00
#> 137 2 0.0000 0.9786 0.00 1.00 0.00
#> 138 1 0.6302 0.0692 0.52 0.48 0.00
#> 139 2 0.0000 0.9786 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 2 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 3 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 4 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 5 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 6 4 0.0707 0.845 0.02 0.00 0.00 0.98
#> 7 2 0.2345 0.878 0.00 0.90 0.00 0.10
#> 8 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 9 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 10 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 11 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 12 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 13 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 14 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 15 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 16 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 17 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 18 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 19 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 20 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 21 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 22 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 23 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 24 1 0.3037 0.859 0.88 0.00 0.02 0.10
#> 25 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 26 1 0.3975 0.660 0.76 0.00 0.00 0.24
#> 27 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 28 4 0.2345 0.839 0.10 0.00 0.00 0.90
#> 29 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 30 4 0.2011 0.844 0.08 0.00 0.00 0.92
#> 31 4 0.3975 0.721 0.24 0.00 0.00 0.76
#> 32 1 0.4134 0.622 0.74 0.00 0.00 0.26
#> 33 4 0.2345 0.839 0.10 0.00 0.00 0.90
#> 34 1 0.0707 0.953 0.98 0.02 0.00 0.00
#> 35 4 0.3400 0.790 0.18 0.00 0.00 0.82
#> 36 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 37 4 0.3400 0.768 0.18 0.00 0.00 0.82
#> 38 2 0.3610 0.761 0.00 0.80 0.00 0.20
#> 39 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 40 2 0.0707 0.943 0.00 0.98 0.00 0.02
#> 41 4 0.4855 0.356 0.00 0.40 0.00 0.60
#> 42 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 43 2 0.4713 0.426 0.00 0.64 0.00 0.36
#> 44 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 45 1 0.3821 0.810 0.84 0.04 0.00 0.12
#> 46 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 47 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 48 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 49 1 0.2647 0.848 0.88 0.00 0.00 0.12
#> 50 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 51 3 0.4907 0.259 0.00 0.42 0.58 0.00
#> 52 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 53 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 54 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 55 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 56 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 57 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 58 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 59 4 0.4949 0.700 0.06 0.18 0.00 0.76
#> 60 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 61 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 62 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 63 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 64 4 0.2011 0.844 0.08 0.00 0.00 0.92
#> 65 4 0.2011 0.808 0.00 0.08 0.00 0.92
#> 66 2 0.1637 0.914 0.00 0.94 0.00 0.06
#> 67 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 68 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 69 2 0.5911 0.573 0.26 0.68 0.04 0.02
#> 70 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 71 4 0.4855 0.320 0.00 0.40 0.00 0.60
#> 72 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 73 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 74 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 75 4 0.2011 0.844 0.08 0.00 0.00 0.92
#> 76 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 77 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 78 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 79 4 0.2647 0.831 0.12 0.00 0.00 0.88
#> 80 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 81 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 82 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 83 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 84 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 85 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 86 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 87 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 88 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 89 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 90 4 0.4948 0.205 0.00 0.44 0.00 0.56
#> 91 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 92 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 93 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 94 4 0.3801 0.750 0.22 0.00 0.00 0.78
#> 95 4 0.3400 0.790 0.18 0.00 0.00 0.82
#> 96 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 97 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 98 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 99 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 100 4 0.4624 0.468 0.00 0.34 0.00 0.66
#> 101 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 102 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 103 4 0.2647 0.831 0.12 0.00 0.00 0.88
#> 104 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 105 3 0.0000 0.978 0.00 0.00 1.00 0.00
#> 106 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 107 2 0.4134 0.655 0.00 0.74 0.00 0.26
#> 108 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 109 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 110 4 0.2345 0.839 0.10 0.00 0.00 0.90
#> 111 4 0.3400 0.788 0.18 0.00 0.00 0.82
#> 112 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 113 4 0.4406 0.557 0.00 0.30 0.00 0.70
#> 114 4 0.4406 0.622 0.30 0.00 0.00 0.70
#> 115 4 0.0707 0.845 0.02 0.00 0.00 0.98
#> 116 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 117 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 118 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 119 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 120 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 121 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 122 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 123 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 124 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 125 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 126 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 127 1 0.0707 0.956 0.98 0.00 0.00 0.02
#> 128 1 0.0000 0.974 1.00 0.00 0.00 0.00
#> 129 2 0.1637 0.914 0.00 0.94 0.00 0.06
#> 130 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 131 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 132 2 0.3801 0.735 0.00 0.78 0.00 0.22
#> 133 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 134 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 135 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 136 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 137 2 0.0000 0.958 0.00 1.00 0.00 0.00
#> 138 4 0.0000 0.841 0.00 0.00 0.00 1.00
#> 139 4 0.1211 0.830 0.00 0.04 0.00 0.96
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 138 4.54e-04 2
#> ATC:skmeans 136 6.14e-17 3
#> ATC:skmeans 133 9.45e-16 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013 , Node014 , Node021 , Node022 , Node023 , Node031 , Node032 , Node033 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["02"]
A summary of res and all the functions that can be applied to it:
res
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 10389 rows and 500 columns, randomly sampled from 960 columns.
#> Top rows (975) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.978 0.991 0.499 0.502 0.502
#> 3 3 0.999 0.956 0.976 0.221 0.870 0.745
#> 4 4 0.970 0.938 0.975 0.145 0.880 0.705
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
get_classes(res, k = 2)
#> class p
#> 1 2 0.502
#> 2 2 0.747
#> 3 2 0.502
#> 4 1 0.502
#> 5 2 0.000
#> 6 2 0.000
#> 7 2 0.000
#> 8 1 0.000
#> 9 2 0.000
#> 10 2 0.000
#> 11 2 0.000
#> 12 2 0.000
#> 13 2 0.000
#> 14 2 0.000
#> 15 2 1.000
#> 16 2 0.000
#> 17 2 0.000
#> 18 1 0.249
#> 19 2 0.000
#> 20 2 0.000
#> 21 2 0.498
#> 22 2 0.502
#> 23 2 0.000
#> 24 2 0.000
#> 25 2 0.249
#> 26 2 0.000
#> 27 1 0.000
#> 28 1 0.249
#> 29 2 0.000
#> 30 1 0.249
#> 31 2 0.498
#> 32 1 0.000
#> 33 1 0.000
#> 34 1 1.000
#> 35 1 0.000
#> 36 2 0.249
#> 37 2 0.000
#> 38 2 0.498
#> 39 2 0.000
#> 40 2 0.000
#> 41 2 0.249
#> 42 2 0.000
#> 43 2 0.000
#> 44 2 0.000
#> 45 2 0.000
#> 46 2 1.000
#> 47 2 0.249
#> 48 1 0.253
#> 49 2 0.498
#> 50 1 0.502
#> 51 2 0.000
#> 52 2 0.498
#> 53 2 0.498
#> 54 2 0.747
#> 55 1 0.502
#> 56 2 0.000
#> 57 2 0.000
#> 58 1 0.253
#> 59 2 0.498
#> 60 1 1.000
#> 61 1 0.000
#> 62 1 0.502
#> 63 1 0.253
#> 64 1 0.751
#> 65 2 0.498
#> 66 2 0.498
#> 67 2 0.253
#> 68 1 0.253
#> 69 2 0.000
#> 70 1 0.751
#> 71 2 0.747
#> 72 2 0.000
#> 73 2 0.000
#> 74 2 0.249
#> 75 2 0.000
#> 76 2 0.000
#> 77 2 0.249
#> 78 1 0.000
#> 79 2 0.751
#> 80 1 0.249
#> 81 2 0.000
#> 82 2 1.000
#> 83 2 0.249
#> 84 2 1.000
#> 85 2 1.000
#> 86 2 0.249
#> 87 2 0.751
#> 88 1 0.000
#> 89 2 0.000
#> 90 2 0.000
#> 91 2 0.000
#> 92 2 0.000
#> 93 1 0.751
#> 94 2 0.000
#> 95 2 0.000
#> 96 1 0.000
#> 97 2 0.751
#> 98 1 0.000
#> 99 1 0.502
#> 100 1 0.000
#> 101 1 0.502
#> 102 1 0.000
#> 103 2 0.000
#> 104 2 0.751
#> 105 2 0.000
#> 106 2 0.000
#> 107 2 0.000
#> 108 2 0.000
#> 109 2 0.000
#> 110 1 0.502
#> 111 1 0.000
#> 112 2 0.502
#> 113 2 0.000
#> 114 2 0.000
#> 115 2 0.000
#> 116 2 0.000
#> 117 2 0.000
#> 118 2 0.000
#> 119 2 0.000
#> 120 1 0.000
#> 121 2 0.000
#> 122 2 0.000
#> 123 2 0.000
#> 124 2 0.000
#> 125 2 0.000
#> 126 2 0.498
#> 127 2 0.000
#> 128 2 0.000
#> 129 2 0.000
#> 130 1 0.751
#> 131 1 0.249
#> 132 1 0.000
#> 133 2 0.751
#> 134 2 0.000
#> 135 1 0.000
#> 136 1 0.000
#> 137 2 0.747
#> 138 2 0.000
#> 139 1 0.000
#> 140 1 0.000
#> 141 1 0.000
#> 142 2 0.249
#> 143 1 0.000
#> 144 2 0.751
#> 145 2 0.498
#> 146 1 0.000
#> 147 1 0.249
#> 148 1 0.000
#> 149 2 0.498
#> 150 1 0.000
#> 151 1 0.000
#> 152 2 0.249
#> 153 2 0.000
#> 154 2 0.000
#> 155 1 0.249
#> 156 2 0.000
#> 157 2 0.000
#> 158 2 1.000
#> 159 2 0.000
#> 160 2 0.249
#> 161 2 0.253
#> 162 2 0.502
#> 163 1 0.000
#> 164 2 0.000
#> 165 2 0.000
#> 166 1 0.000
#> 167 2 0.249
#> 168 1 0.000
#> 169 1 0.000
#> 170 2 1.000
#> 171 2 0.253
#> 172 1 0.751
#> 173 1 0.253
#> 174 1 0.000
#> 175 1 1.000
#> 176 1 0.253
#> 177 1 0.000
#> 178 1 0.253
#> 179 2 0.000
#> 180 1 0.000
#> 181 1 0.249
#> 182 1 0.000
#> 183 1 0.747
#> 184 1 0.249
#> 185 1 0.000
#> 186 2 0.000
#> 187 1 1.000
#> 188 1 0.751
#> 189 1 0.000
#> 190 2 0.000
#> 191 1 0.000
#> 192 1 0.249
#> 193 2 0.751
#> 194 2 0.249
#> 195 2 0.000
#> 196 2 0.000
#> 197 2 0.000
#> 198 2 0.000
#> 199 1 0.000
#> 200 2 0.249
#> 201 2 0.249
#> 202 2 1.000
#> 203 2 1.000
#> 204 2 0.000
#> 205 2 0.000
#> 206 2 0.000
#> 207 1 1.000
#> 208 1 0.249
#> 209 2 0.000
#> 210 2 0.000
#> 211 2 0.751
#> 212 1 0.000
#> 213 2 0.253
#> 214 1 0.000
#> 215 2 0.249
#> 216 1 0.253
#> 217 2 0.747
#> 218 1 0.502
#> 219 2 0.249
#> 220 2 0.000
#> 221 1 0.751
#> 222 1 0.000
#> 223 1 0.000
#> 224 2 0.000
#> 225 1 1.000
#> 226 1 0.249
#> 227 1 0.000
#> 228 2 0.000
#> 229 2 0.751
#> 230 1 0.000
#> 231 1 0.249
#> 232 1 0.000
#> 233 2 1.000
#> 234 1 0.253
#> 235 1 0.000
#> 236 1 0.000
#> 237 1 0.000
#> 238 1 0.249
#> 239 1 0.000
#> 240 1 0.000
#> 241 1 0.000
#> 242 2 0.249
#> 243 1 0.000
#> 244 2 0.249
#> 245 1 0.000
#> 246 2 0.751
#> 247 1 0.000
#> 248 2 0.751
#> 249 2 1.000
#> 250 1 0.000
#> 251 2 1.000
#> 252 1 0.249
#> 253 1 0.000
#> 254 1 0.000
#> 255 1 0.000
#> 256 1 0.000
#> 257 2 0.249
#> 258 2 0.751
#> 259 1 0.249
#> 260 1 0.000
#> 261 1 0.000
#> 262 2 1.000
#> 263 2 0.253
#> 264 2 0.502
#> 265 2 1.000
#> 266 2 0.000
#> 267 2 1.000
#> 268 2 0.253
#> 269 1 0.000
#> 270 1 0.000
#> 271 2 0.502
#> 272 2 0.000
#> 273 2 0.000
#> 274 2 0.000
#> 275 2 0.000
#> 276 1 0.249
#> 277 1 0.000
#> 278 2 0.751
#> 279 2 0.502
#> 280 1 0.000
#> 281 2 0.249
#> 282 2 0.502
#> 283 2 0.000
#> 284 1 0.000
#> 285 2 0.000
#> 286 1 0.000
#> 287 2 0.000
#> 288 2 0.000
#> 289 2 0.000
#> 290 1 0.000
#> 291 1 0.000
#> 292 1 0.249
#> 293 1 0.000
#> 294 1 0.249
#> 295 2 1.000
#> 296 2 0.498
#> 297 2 0.000
#> 298 1 0.502
#> 299 2 0.751
#> 300 1 0.000
#> 301 1 0.000
#> 302 2 1.000
#> 303 2 0.000
#> 304 2 1.000
#> 305 2 0.000
#> 306 1 0.000
#> 307 2 0.000
#> 308 2 1.000
#> 309 1 0.000
#> 310 2 0.249
#> 311 2 1.000
#> 312 1 0.000
#> 313 1 0.249
#> 314 1 0.000
#> 315 1 0.000
#> 316 1 0.000
#> 317 2 1.000
#> 318 1 0.000
#> 319 2 0.498
#> 320 1 0.249
#> 321 2 1.000
#> 322 1 0.000
#> 323 2 0.498
#> 324 1 0.000
#> 325 1 0.000
#> 326 1 0.000
#> 327 1 0.000
#> 328 1 1.000
#> 329 2 0.253
#> 330 1 0.000
#> 331 1 0.498
#> 332 1 0.249
#> 333 1 0.000
#> 334 1 1.000
#> 335 2 0.253
#> 336 1 0.000
#> 337 2 0.498
#> 338 2 0.000
#> 339 1 0.249
#> 340 2 0.249
#> 341 2 1.000
#> 342 2 0.751
#> 343 1 0.000
#> 344 2 0.000
#> 345 2 0.000
#> 346 1 0.000
#> 347 1 0.249
#> 348 1 0.000
#> 349 1 0.000
#> 350 1 0.000
#> 351 1 0.000
#> 352 1 0.000
#> 353 1 0.000
#> 354 2 1.000
#> 355 1 0.000
#> 356 2 1.000
#> 357 2 0.253
#> 358 1 0.000
#> 359 1 0.000
#> 360 1 0.000
#> 361 1 0.000
#> 362 2 1.000
#> 363 1 0.751
#> 364 2 1.000
#> 365 1 0.000
#> 366 1 0.498
#> 367 2 0.751
#> 368 1 0.000
#> 369 1 0.747
#> 370 2 1.000
#> 371 1 0.000
#> 372 1 0.000
#> 373 1 0.000
#> 374 1 0.000
#> 375 2 0.502
#> 376 1 0.000
#> 377 1 0.000
#> 378 1 0.000
#> 379 1 0.000
#> 380 1 0.498
#> 381 1 1.000
#> 382 2 0.502
#> 383 1 0.000
#> 384 2 0.000
#> 385 2 0.249
#> 386 2 0.502
#> 387 2 0.000
#> 388 2 0.000
#> 389 2 0.253
#> 390 2 0.000
#> 391 2 0.747
#> 392 2 0.249
#> 393 1 0.000
#> 394 1 0.000
#> 395 1 0.751
#> 396 2 0.000
#> 397 2 0.502
#> 398 1 0.000
#> 399 1 0.000
#> 400 2 0.502
#> 401 2 0.249
#> 402 2 0.000
#> 403 1 0.000
#> 404 2 0.000
#> 405 2 0.249
#> 406 2 0.000
#> 407 1 0.000
#> 408 1 0.000
#> 409 2 0.000
#> 410 2 0.000
#> 411 2 0.502
#> 412 2 0.249
#> 413 2 0.249
#> 414 2 0.502
#> 415 2 0.000
#> 416 2 0.000
#> 417 2 0.000
#> 418 2 0.000
#> 419 2 0.000
#> 420 1 0.000
#> 421 1 0.000
#> 422 1 0.253
#> 423 1 0.249
#> 424 2 0.000
#> 425 1 0.249
#> 426 2 0.000
#> 427 2 0.000
#> 428 2 0.000
#> 429 2 0.249
#> 430 2 0.000
#> 431 2 0.000
#> 432 2 1.000
#> 433 2 0.000
#> 434 2 0.000
#> 435 2 0.000
#> 436 1 0.000
#> 437 1 0.000
#> 438 2 0.000
#> 439 1 0.249
#> 440 2 0.000
#> 441 2 0.751
#> 442 1 0.249
#> 443 1 0.000
#> 444 2 0.000
#> 445 1 0.000
#> 446 1 0.751
#> 447 1 0.000
#> 448 2 0.751
#> 449 1 0.000
#> 450 2 0.000
#> 451 1 0.000
#> 452 2 0.253
#> 453 1 0.498
#> 454 1 0.000
#> 455 1 0.000
#> 456 1 0.000
#> 457 1 0.000
#> 458 1 0.747
#> 459 2 0.249
#> 460 1 0.502
#> 461 1 0.000
#> 462 1 0.000
#> 463 2 0.000
#> 464 1 0.000
#> 465 1 0.000
#> 466 1 0.000
#> 467 2 0.253
#> 468 1 0.502
#> 469 2 0.498
#> 470 2 0.249
#> 471 2 0.000
#> 472 2 0.000
#> 473 1 1.000
#> 474 2 0.000
#> 475 2 0.000
#> 476 2 0.000
#> 477 2 0.000
#> 478 2 0.249
#> 479 2 0.000
#> 480 2 0.253
#> 481 2 1.000
#> 482 2 0.000
#> 483 2 0.000
#> 484 2 0.000
#> 485 2 0.249
#> 486 2 0.502
#> 487 2 0.253
#> 488 2 0.502
#> 489 2 0.000
#> 490 2 0.498
#> 491 2 0.000
#> 492 2 0.000
#> 493 1 0.000
#> 494 2 0.000
#> 495 2 0.000
#> 496 2 0.249
#> 497 2 0.502
#> 498 2 1.000
#> 499 2 0.000
#> 500 2 0.751
#> 501 2 0.249
#> 502 2 0.502
#> 503 2 0.000
#> 504 2 0.498
#> 505 2 0.751
#> 506 2 0.000
#> 507 2 0.751
#> 508 2 0.253
#> 509 2 0.000
#> 510 2 0.249
#> 511 2 0.000
#> 512 2 0.000
#> 513 2 0.000
#> 514 2 0.000
#> 515 2 0.000
#> 516 2 0.751
#> 517 2 0.000
#> 518 2 0.000
#> 519 2 0.000
#> 520 2 0.000
#> 521 2 0.253
#> 522 2 0.249
#> 523 2 0.000
#> 524 1 0.000
#> 525 2 0.751
#> 526 2 0.000
#> 527 2 0.000
#> 528 2 0.502
#> 529 2 0.000
#> 530 2 0.000
#> 531 2 0.249
#> 532 2 0.000
#> 533 2 0.253
#> 534 2 0.000
#> 535 2 0.000
#> 536 2 0.000
#> 537 2 0.000
#> 538 2 0.000
#> 539 2 0.000
#> 540 2 0.000
#> 541 1 0.000
#> 542 1 0.000
#> 543 2 0.751
#> 544 2 0.000
#> 545 2 0.000
#> 546 2 0.000
#> 547 2 0.249
#> 548 2 0.498
#> 549 2 0.249
#> 550 2 0.751
#> 551 2 0.000
#> 552 2 0.249
#> 553 1 1.000
#> 554 1 0.000
#> 555 2 0.502
#> 556 1 0.000
#> 557 2 1.000
#> 558 2 1.000
#> 559 1 0.000
#> 560 1 0.000
#> 561 1 0.000
#> 562 2 0.751
#> 563 1 0.000
#> 564 1 0.000
#> 565 2 0.502
#> 566 2 0.000
#> 567 2 1.000
#> 568 2 0.000
#> 569 2 0.249
#> 570 1 0.000
#> 571 2 0.751
#> 572 2 0.498
#> 573 2 0.502
#> 574 2 0.751
#> 575 1 0.747
#> 576 1 0.000
#> 577 2 0.249
#> 578 1 0.000
#> 579 1 0.000
#> 580 1 0.000
#> 581 2 0.747
#> 582 2 0.502
#> 583 2 0.000
#> 584 2 0.249
#> 585 2 0.000
#> 586 1 0.000
#> 587 2 0.751
#> 588 2 0.000
#> 589 2 0.000
#> 590 1 0.000
#> 591 1 0.249
#> 592 1 0.498
#> 593 2 0.000
#> 594 2 0.000
#> 595 1 0.249
#> 596 2 0.000
#> 597 2 1.000
#> 598 1 0.000
#> 599 1 0.249
#> 600 2 0.751
#> 601 2 0.502
#> 602 1 0.000
#> 603 2 0.000
#> 604 1 0.000
#> 605 2 0.000
#> 606 2 0.751
#> 607 2 0.000
#> 608 2 0.498
#> 609 2 0.000
#> 610 2 0.249
#> 611 2 0.000
#> 612 2 0.747
#> 613 1 0.000
#> 614 2 0.502
#> 615 2 0.751
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.502
#> 619 2 0.000
#> 620 2 0.000
#> 621 2 0.000
#> 622 2 0.249
#> 623 2 0.249
#> 624 2 0.000
#> 625 1 0.000
#> 626 2 0.000
#> 627 1 1.000
#> 628 1 0.249
#> 629 1 0.751
#> 630 1 0.000
#> 631 1 0.000
#> 632 1 0.751
#> 633 1 0.000
#> 634 2 0.000
#> 635 1 0.498
#> 636 2 0.502
#> 637 1 0.249
#> 638 2 0.249
#> 639 1 0.498
#> 640 1 0.249
#> 641 2 0.498
#> 642 1 0.000
#> 643 1 1.000
#> 644 1 0.249
#> 645 2 0.000
#> 646 2 0.253
#> 647 2 0.000
#> 648 2 0.000
#> 649 2 0.000
#> 650 1 0.000
#> 651 1 0.000
#> 652 2 0.498
#> 653 1 0.249
#> 654 2 0.498
#> 655 2 0.249
#> 656 2 0.498
#> 657 1 0.000
#> 658 2 0.000
#> 659 2 0.000
#> 660 1 0.000
#> 661 2 0.000
#> 662 2 0.249
#> 663 1 0.502
#> 664 1 0.000
#> 665 1 0.000
#> 666 1 0.253
#> 667 1 0.000
#> 668 1 0.000
#> 669 1 0.249
#> 670 2 0.502
#> 671 1 0.000
#> 672 2 0.000
#> 673 1 0.000
#> 674 1 0.000
#> 675 2 0.253
#> 676 1 0.000
#> 677 2 0.751
#> 678 1 0.249
#> 679 2 0.000
#> 680 2 0.502
#> 681 2 0.000
#> 682 2 0.747
#> 683 2 0.000
#> 684 1 0.000
#> 685 2 0.000
#> 686 1 0.000
#> 687 1 0.502
#> 688 1 0.000
#> 689 1 0.000
#> 690 2 0.498
#> 691 2 0.000
#> 692 1 0.000
#> 693 1 0.249
#> 694 1 0.249
#> 695 2 0.000
#> 696 2 0.000
#> 697 2 0.000
#> 698 1 0.000
#> 699 1 0.253
#> 700 2 0.000
#> 701 2 0.253
#> 702 1 0.000
#> 703 2 1.000
#> 704 1 0.000
#> 705 1 0.000
#> 706 1 0.000
#> 707 2 0.249
#> 708 2 0.000
#> 709 2 0.000
#> 710 2 0.751
#> 711 2 0.000
#> 712 2 0.249
#> 713 2 0.000
#> 714 2 0.000
#> 715 1 0.000
#> 716 2 0.000
#> 717 2 0.000
#> 718 2 0.498
#> 719 2 0.000
#> 720 2 0.000
#> 721 2 0.000
#> 722 1 0.000
#> 723 1 0.000
#> 724 1 0.000
#> 725 2 0.249
#> 726 2 0.000
#> 727 2 0.000
#> 728 2 0.000
#> 729 2 0.000
#> 730 2 0.502
#> 731 2 0.498
#> 732 2 0.000
#> 733 2 0.000
#> 734 1 0.000
#> 735 1 0.249
#> 736 1 0.249
#> 737 2 0.000
#> 738 2 0.249
#> 739 2 0.000
#> 740 2 0.000
#> 741 2 0.000
#> 742 2 0.000
#> 743 1 0.000
#> 744 1 0.249
#> 745 2 0.000
#> 746 1 0.000
#> 747 1 0.000
#> 748 1 0.000
#> 749 1 0.000
#> 750 1 0.000
#> 751 1 0.000
#> 752 1 0.000
#> 753 1 0.000
#> 754 1 0.000
#> 755 1 0.000
#> 756 1 0.000
#> 757 1 0.000
#> 758 1 0.000
#> 759 1 0.000
#> 760 2 1.000
#> 761 1 0.000
#> 762 1 0.000
#> 763 1 0.249
#> 764 1 0.000
#> 765 2 0.249
#> 766 2 0.253
#> 767 2 0.000
#> 768 2 0.000
#> 769 2 1.000
#> 770 2 0.000
#> 771 2 1.000
#> 772 2 0.000
#> 773 1 0.000
#> 774 1 0.747
#> 775 1 0.000
#> 776 2 1.000
#> 777 2 0.000
#> 778 1 0.000
#> 779 1 0.000
#> 780 1 0.000
#> 781 1 0.000
#> 782 1 0.000
#> 783 2 0.249
#> 784 1 0.249
#> 785 2 1.000
#> 786 2 0.000
#> 787 2 0.000
#> 788 2 0.000
#> 789 2 0.000
#> 790 2 0.000
#> 791 1 0.000
#> 792 2 0.751
#> 793 2 0.000
#> 794 2 0.498
#> 795 2 0.000
#> 796 2 0.000
#> 797 2 0.000
#> 798 2 0.000
#> 799 2 0.000
#> 800 2 0.249
#> 801 2 0.751
#> 802 2 0.498
#> 803 2 0.249
#> 804 2 0.000
#> 805 2 0.000
#> 806 2 0.000
#> 807 2 0.000
#> 808 2 0.000
#> 809 2 0.000
#> 810 2 0.000
#> 811 2 0.000
#> 812 2 0.000
#> 813 2 0.000
#> 814 2 0.000
#> 815 2 0.000
#> 816 1 0.000
#> 817 1 0.000
#> 818 1 0.000
#> 819 1 0.000
#> 820 1 0.000
#> 821 1 0.000
#> 822 2 1.000
#> 823 1 0.498
#> 824 2 0.000
#> 825 2 0.502
#> 826 2 0.751
#> 827 2 0.249
#> 828 1 0.249
#> 829 2 0.000
#> 830 2 0.498
#> 831 2 0.249
#> 832 2 0.253
#> 833 2 0.498
#> 834 2 0.000
#> 835 2 1.000
#> 836 2 0.000
#> 837 2 0.000
#> 838 2 0.747
#> 839 1 0.000
#> 840 2 0.751
#> 841 2 0.000
#> 842 2 0.498
#> 843 1 0.000
#> 844 2 0.000
#> 845 2 0.000
#> 846 2 0.000
#> 847 2 0.000
#> 848 1 0.000
#> 849 1 0.000
#> 850 1 0.000
#> 851 1 0.000
#> 852 1 0.000
#> 853 1 0.000
#> 854 1 0.000
#> 855 1 0.000
#> 856 2 0.000
#> 857 1 0.000
#> 858 1 0.000
#> 859 1 0.000
#> 860 1 0.000
#> 861 1 0.000
#> 862 1 0.000
#> 863 1 0.000
#> 864 1 0.000
#> 865 1 0.000
#> 866 1 0.000
#> 867 1 0.000
#> 868 1 0.000
#> 869 1 0.000
#> 870 1 0.000
#> 871 1 0.000
#> 872 1 0.000
#> 873 1 0.000
#> 874 1 0.000
#> 875 1 0.000
#> 876 1 0.000
#> 877 1 0.000
#> 878 1 0.249
#> 879 2 1.000
#> 880 2 0.253
#> 881 1 0.000
#> 882 1 0.000
#> 883 1 0.000
#> 884 1 0.000
#> 885 1 0.000
#> 886 1 0.000
#> 887 1 0.000
#> 888 1 0.000
#> 889 1 0.000
#> 890 1 0.000
#> 891 1 0.000
#> 892 1 0.000
#> 893 1 0.000
#> 894 1 0.000
#> 895 1 0.000
#> 896 1 0.000
#> 897 1 0.000
#> 898 1 0.000
#> 899 1 0.000
#> 900 1 0.000
#> 901 1 0.000
#> 902 1 0.000
#> 903 2 0.000
#> 904 2 0.000
#> 905 1 1.000
#> 906 2 0.253
#> 907 1 0.000
#> 908 1 0.000
#> 909 1 0.000
#> 910 1 0.000
#> 911 1 0.000
#> 912 1 0.000
#> 913 2 1.000
#> 914 1 0.000
#> 915 1 0.000
#> 916 1 0.000
#> 917 1 0.000
#> 918 1 0.249
#> 919 2 1.000
#> 920 1 0.000
#> 921 1 0.000
#> 922 1 0.000
#> 923 2 1.000
#> 924 1 0.000
#> 925 1 0.000
#> 926 1 0.000
#> 927 1 0.000
#> 928 1 0.000
#> 929 1 0.000
#> 930 1 0.502
#> 931 1 0.000
#> 932 1 0.249
#> 933 1 0.000
#> 934 1 0.000
#> 935 1 0.000
#> 936 2 0.751
#> 937 2 0.000
#> 938 1 0.253
#> 939 2 0.498
#> 940 1 0.000
#> 941 1 0.000
#> 942 1 0.000
#> 943 2 0.747
#> 944 2 0.249
#> 945 1 0.000
#> 946 1 0.249
#> 947 1 0.000
#> 948 2 0.751
#> 949 1 0.000
#> 950 2 0.000
#> 951 1 0.000
#> 952 1 0.000
#> 953 1 0.253
#> 954 1 0.249
#> 955 1 0.000
#> 956 1 0.000
#> 957 1 0.000
#> 958 1 0.000
#> 959 1 0.000
#> 960 1 0.000
get_classes(res, k = 3)
#> class p
#> 1 3 1.000
#> 2 2 1.000
#> 3 1 1.000
#> 4 1 1.000
#> 5 2 0.000
#> 6 2 0.000
#> 7 2 0.000
#> 8 1 1.000
#> 9 2 0.000
#> 10 2 0.000
#> 11 2 0.000
#> 12 2 0.000
#> 13 2 0.000
#> 14 2 0.000
#> 15 3 0.000
#> 16 2 0.000
#> 17 2 1.000
#> 18 1 1.000
#> 19 3 1.000
#> 20 2 0.502
#> 21 2 0.000
#> 22 2 1.000
#> 23 2 0.000
#> 24 2 0.000
#> 25 3 1.000
#> 26 2 0.000
#> 27 1 0.000
#> 28 1 1.000
#> 29 2 0.000
#> 30 1 0.498
#> 31 2 0.249
#> 32 1 0.000
#> 33 1 0.000
#> 34 3 1.000
#> 35 1 0.000
#> 36 2 0.000
#> 37 2 0.000
#> 38 2 0.000
#> 39 2 0.502
#> 40 2 0.000
#> 41 2 0.000
#> 42 2 0.249
#> 43 2 0.000
#> 44 2 0.000
#> 45 2 0.000
#> 46 2 0.000
#> 47 1 1.000
#> 48 3 1.000
#> 49 3 0.000
#> 50 3 1.000
#> 51 2 0.000
#> 52 1 1.000
#> 53 3 1.000
#> 54 3 0.502
#> 55 1 1.000
#> 56 2 0.000
#> 57 2 0.000
#> 58 1 1.000
#> 59 2 0.249
#> 60 1 1.000
#> 61 1 0.000
#> 62 1 1.000
#> 63 1 0.498
#> 64 2 1.000
#> 65 2 0.000
#> 66 2 1.000
#> 67 1 1.000
#> 68 1 1.000
#> 69 2 0.000
#> 70 1 1.000
#> 71 2 0.000
#> 72 2 1.000
#> 73 2 0.000
#> 74 2 0.000
#> 75 2 0.000
#> 76 2 1.000
#> 77 3 0.000
#> 78 1 0.498
#> 79 2 0.249
#> 80 3 0.000
#> 81 2 0.000
#> 82 2 0.000
#> 83 3 0.253
#> 84 2 1.000
#> 85 2 0.249
#> 86 2 0.000
#> 87 2 0.000
#> 88 1 0.249
#> 89 2 0.000
#> 90 2 0.000
#> 91 3 0.751
#> 92 2 0.000
#> 93 1 1.000
#> 94 2 1.000
#> 95 2 0.000
#> 96 1 0.000
#> 97 2 0.249
#> 98 3 0.000
#> 99 2 0.498
#> 100 3 0.000
#> 101 1 0.751
#> 102 3 0.249
#> 103 2 0.751
#> 104 1 1.000
#> 105 2 0.000
#> 106 2 0.000
#> 107 2 0.000
#> 108 2 0.000
#> 109 2 0.000
#> 110 1 1.000
#> 111 1 1.000
#> 112 2 0.751
#> 113 1 1.000
#> 114 2 0.000
#> 115 2 0.000
#> 116 2 0.000
#> 117 2 0.498
#> 118 2 0.000
#> 119 2 0.000
#> 120 1 0.498
#> 121 2 0.000
#> 122 2 0.000
#> 123 2 0.000
#> 124 2 0.000
#> 125 2 0.000
#> 126 2 0.000
#> 127 2 0.000
#> 128 2 0.000
#> 129 2 0.498
#> 130 2 0.751
#> 131 1 1.000
#> 132 3 0.000
#> 133 2 0.000
#> 134 2 1.000
#> 135 1 1.000
#> 136 1 1.000
#> 137 2 1.000
#> 138 2 0.000
#> 139 3 0.000
#> 140 1 0.249
#> 141 1 0.000
#> 142 2 0.000
#> 143 1 0.747
#> 144 1 1.000
#> 145 3 1.000
#> 146 1 1.000
#> 147 3 0.000
#> 148 3 0.000
#> 149 3 0.000
#> 150 3 0.000
#> 151 3 0.000
#> 152 2 0.000
#> 153 2 0.000
#> 154 2 0.000
#> 155 1 0.000
#> 156 2 0.000
#> 157 3 1.000
#> 158 3 1.000
#> 159 2 0.000
#> 160 2 0.000
#> 161 2 1.000
#> 162 2 1.000
#> 163 3 0.000
#> 164 3 0.000
#> 165 2 1.000
#> 166 1 0.000
#> 167 2 0.000
#> 168 1 0.000
#> 169 3 0.000
#> 170 2 1.000
#> 171 2 0.253
#> 172 1 1.000
#> 173 1 0.502
#> 174 1 1.000
#> 175 3 1.000
#> 176 3 1.000
#> 177 1 1.000
#> 178 3 0.000
#> 179 2 0.000
#> 180 1 0.000
#> 181 1 0.502
#> 182 3 0.000
#> 183 1 1.000
#> 184 1 1.000
#> 185 1 0.000
#> 186 3 0.498
#> 187 1 1.000
#> 188 1 0.751
#> 189 1 0.000
#> 190 2 0.000
#> 191 1 1.000
#> 192 3 0.000
#> 193 1 1.000
#> 194 1 1.000
#> 195 2 0.000
#> 196 3 0.000
#> 197 3 1.000
#> 198 3 1.000
#> 199 1 0.000
#> 200 2 1.000
#> 201 2 0.502
#> 202 2 0.498
#> 203 2 0.000
#> 204 2 0.000
#> 205 2 0.000
#> 206 2 0.000
#> 207 3 0.000
#> 208 1 0.000
#> 209 2 0.000
#> 210 2 0.000
#> 211 2 1.000
#> 212 1 0.000
#> 213 2 1.000
#> 214 1 0.249
#> 215 2 0.000
#> 216 1 1.000
#> 217 3 0.000
#> 218 1 1.000
#> 219 2 1.000
#> 220 2 0.000
#> 221 3 0.000
#> 222 1 0.249
#> 223 3 0.000
#> 224 2 0.253
#> 225 1 1.000
#> 226 3 0.000
#> 227 1 0.000
#> 228 2 0.000
#> 229 2 1.000
#> 230 1 1.000
#> 231 3 0.000
#> 232 1 1.000
#> 233 1 1.000
#> 234 1 0.000
#> 235 3 0.000
#> 236 1 0.000
#> 237 3 0.000
#> 238 1 0.249
#> 239 1 0.000
#> 240 1 1.000
#> 241 1 1.000
#> 242 2 0.000
#> 243 1 1.000
#> 244 3 0.000
#> 245 3 0.000
#> 246 2 0.000
#> 247 1 0.498
#> 248 3 0.249
#> 249 3 0.000
#> 250 1 0.000
#> 251 3 1.000
#> 252 2 1.000
#> 253 1 0.000
#> 254 1 1.000
#> 255 3 0.000
#> 256 1 1.000
#> 257 2 0.000
#> 258 2 1.000
#> 259 1 1.000
#> 260 3 0.000
#> 261 3 0.000
#> 262 1 1.000
#> 263 3 1.000
#> 264 1 1.000
#> 265 3 0.000
#> 266 2 0.000
#> 267 3 1.000
#> 268 2 0.000
#> 269 1 1.000
#> 270 1 1.000
#> 271 2 1.000
#> 272 2 0.000
#> 273 2 0.000
#> 274 2 0.000
#> 275 2 0.000
#> 276 1 1.000
#> 277 1 1.000
#> 278 2 0.000
#> 279 2 0.000
#> 280 1 1.000
#> 281 2 0.000
#> 282 3 0.000
#> 283 2 0.000
#> 284 1 0.000
#> 285 2 0.498
#> 286 1 0.502
#> 287 2 0.000
#> 288 2 0.000
#> 289 2 0.000
#> 290 1 0.751
#> 291 3 0.000
#> 292 3 0.000
#> 293 1 1.000
#> 294 1 1.000
#> 295 1 1.000
#> 296 1 1.000
#> 297 2 0.000
#> 298 1 1.000
#> 299 3 1.000
#> 300 1 1.000
#> 301 1 1.000
#> 302 1 1.000
#> 303 2 0.000
#> 304 1 1.000
#> 305 2 0.000
#> 306 1 0.000
#> 307 2 0.000
#> 308 2 1.000
#> 309 1 1.000
#> 310 2 0.000
#> 311 2 0.502
#> 312 1 0.747
#> 313 1 1.000
#> 314 1 1.000
#> 315 1 0.000
#> 316 1 0.000
#> 317 2 0.498
#> 318 3 0.000
#> 319 2 0.000
#> 320 1 1.000
#> 321 3 0.000
#> 322 1 0.000
#> 323 2 0.000
#> 324 1 0.249
#> 325 1 0.000
#> 326 1 0.000
#> 327 1 1.000
#> 328 1 1.000
#> 329 3 1.000
#> 330 1 0.249
#> 331 1 1.000
#> 332 1 0.000
#> 333 3 0.000
#> 334 1 1.000
#> 335 2 1.000
#> 336 1 0.000
#> 337 2 0.000
#> 338 2 0.000
#> 339 1 1.000
#> 340 2 0.249
#> 341 2 0.249
#> 342 2 0.000
#> 343 1 1.000
#> 344 2 0.000
#> 345 3 0.000
#> 346 1 0.000
#> 347 1 0.249
#> 348 1 0.249
#> 349 1 1.000
#> 350 1 0.249
#> 351 1 0.000
#> 352 1 0.000
#> 353 1 0.000
#> 354 3 0.751
#> 355 1 1.000
#> 356 3 0.000
#> 357 2 0.751
#> 358 1 0.000
#> 359 1 0.000
#> 360 1 1.000
#> 361 1 0.000
#> 362 2 1.000
#> 363 1 1.000
#> 364 3 0.000
#> 365 3 0.000
#> 366 1 0.751
#> 367 1 1.000
#> 368 3 0.000
#> 369 3 0.000
#> 370 2 0.000
#> 371 1 1.000
#> 372 1 0.249
#> 373 1 1.000
#> 374 1 1.000
#> 375 1 1.000
#> 376 1 0.249
#> 377 1 1.000
#> 378 1 0.000
#> 379 1 0.000
#> 380 1 0.502
#> 381 1 1.000
#> 382 2 0.000
#> 383 1 0.751
#> 384 2 0.000
#> 385 2 0.000
#> 386 1 1.000
#> 387 2 0.000
#> 388 2 0.000
#> 389 2 0.000
#> 390 2 0.000
#> 391 1 1.000
#> 392 2 0.000
#> 393 1 1.000
#> 394 1 0.000
#> 395 1 1.000
#> 396 2 0.000
#> 397 2 1.000
#> 398 1 1.000
#> 399 1 0.249
#> 400 1 1.000
#> 401 2 0.000
#> 402 2 0.000
#> 403 1 0.000
#> 404 2 0.249
#> 405 2 0.000
#> 406 2 0.498
#> 407 1 1.000
#> 408 2 0.502
#> 409 2 0.000
#> 410 2 0.000
#> 411 1 1.000
#> 412 2 0.000
#> 413 2 0.000
#> 414 2 0.000
#> 415 2 0.000
#> 416 1 1.000
#> 417 2 0.000
#> 418 2 0.498
#> 419 2 0.000
#> 420 1 0.000
#> 421 1 1.000
#> 422 1 0.000
#> 423 1 1.000
#> 424 2 0.000
#> 425 3 0.751
#> 426 2 0.000
#> 427 2 0.000
#> 428 2 0.000
#> 429 2 0.000
#> 430 2 0.000
#> 431 2 0.000
#> 432 2 0.000
#> 433 2 1.000
#> 434 2 0.249
#> 435 2 0.000
#> 436 1 1.000
#> 437 1 0.000
#> 438 2 1.000
#> 439 3 0.000
#> 440 3 1.000
#> 441 2 0.249
#> 442 3 0.000
#> 443 3 0.000
#> 444 2 0.000
#> 445 1 1.000
#> 446 1 0.751
#> 447 1 1.000
#> 448 3 0.000
#> 449 3 0.000
#> 450 2 0.000
#> 451 3 0.000
#> 452 2 0.498
#> 453 1 1.000
#> 454 1 1.000
#> 455 1 1.000
#> 456 1 0.000
#> 457 1 1.000
#> 458 1 0.000
#> 459 2 0.249
#> 460 1 0.498
#> 461 1 0.000
#> 462 1 0.000
#> 463 2 0.000
#> 464 1 1.000
#> 465 1 0.000
#> 466 1 1.000
#> 467 2 1.000
#> 468 1 1.000
#> 469 2 0.000
#> 470 2 0.000
#> 471 2 0.000
#> 472 2 0.000
#> 473 1 1.000
#> 474 2 0.000
#> 475 2 0.000
#> 476 2 0.000
#> 477 2 0.000
#> 478 2 0.000
#> 479 2 0.000
#> 480 2 0.000
#> 481 2 0.249
#> 482 2 0.000
#> 483 3 1.000
#> 484 3 0.000
#> 485 2 0.000
#> 486 2 0.000
#> 487 2 1.000
#> 488 2 0.249
#> 489 3 1.000
#> 490 3 0.502
#> 491 2 0.000
#> 492 2 0.000
#> 493 1 1.000
#> 494 2 0.000
#> 495 2 0.000
#> 496 2 0.249
#> 497 2 0.000
#> 498 3 1.000
#> 499 2 0.000
#> 500 2 0.000
#> 501 2 0.000
#> 502 2 0.751
#> 503 2 0.000
#> 504 2 0.000
#> 505 2 0.000
#> 506 2 0.000
#> 507 3 0.000
#> 508 2 0.000
#> 509 2 0.000
#> 510 2 0.000
#> 511 2 0.000
#> 512 2 0.498
#> 513 2 0.000
#> 514 2 0.000
#> 515 2 1.000
#> 516 2 0.502
#> 517 2 0.000
#> 518 2 0.000
#> 519 2 0.000
#> 520 2 0.000
#> 521 2 0.000
#> 522 2 0.000
#> 523 2 0.000
#> 524 1 0.253
#> 525 3 0.000
#> 526 2 0.000
#> 527 2 0.000
#> 528 2 0.000
#> 529 2 0.000
#> 530 2 0.000
#> 531 2 1.000
#> 532 2 0.000
#> 533 2 1.000
#> 534 2 0.000
#> 535 2 0.000
#> 536 2 0.000
#> 537 2 0.000
#> 538 2 0.000
#> 539 2 0.000
#> 540 2 0.000
#> 541 1 1.000
#> 542 3 0.000
#> 543 2 0.000
#> 544 2 0.751
#> 545 2 0.000
#> 546 2 0.000
#> 547 2 0.000
#> 548 2 0.000
#> 549 2 0.000
#> 550 2 0.000
#> 551 2 0.000
#> 552 2 1.000
#> 553 1 1.000
#> 554 1 0.249
#> 555 2 1.000
#> 556 1 1.000
#> 557 2 0.000
#> 558 1 1.000
#> 559 3 0.000
#> 560 1 0.498
#> 561 1 0.000
#> 562 2 0.000
#> 563 1 1.000
#> 564 1 1.000
#> 565 2 1.000
#> 566 2 0.000
#> 567 2 0.000
#> 568 1 1.000
#> 569 2 1.000
#> 570 1 0.751
#> 571 2 0.000
#> 572 2 0.000
#> 573 2 0.000
#> 574 1 1.000
#> 575 1 1.000
#> 576 3 0.000
#> 577 2 0.000
#> 578 1 0.000
#> 579 1 1.000
#> 580 3 0.502
#> 581 2 1.000
#> 582 2 0.751
#> 583 2 0.751
#> 584 2 0.000
#> 585 3 0.000
#> 586 1 0.000
#> 587 2 0.000
#> 588 2 0.000
#> 589 2 1.000
#> 590 1 0.751
#> 591 1 0.751
#> 592 1 0.498
#> 593 2 0.000
#> 594 2 0.000
#> 595 1 1.000
#> 596 2 0.000
#> 597 3 0.000
#> 598 1 1.000
#> 599 2 0.253
#> 600 1 1.000
#> 601 2 0.000
#> 602 1 1.000
#> 603 2 0.000
#> 604 1 0.747
#> 605 2 0.000
#> 606 2 0.000
#> 607 2 0.000
#> 608 2 0.000
#> 609 3 1.000
#> 610 2 0.000
#> 611 2 1.000
#> 612 1 1.000
#> 613 1 0.498
#> 614 3 1.000
#> 615 2 0.000
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.000
#> 619 2 0.000
#> 620 2 0.000
#> 621 2 0.000
#> 622 2 0.000
#> 623 2 0.000
#> 624 2 0.000
#> 625 1 0.747
#> 626 2 0.000
#> 627 1 1.000
#> 628 1 1.000
#> 629 1 1.000
#> 630 1 0.000
#> 631 1 1.000
#> 632 1 1.000
#> 633 1 0.751
#> 634 1 1.000
#> 635 1 1.000
#> 636 3 1.000
#> 637 1 0.747
#> 638 1 1.000
#> 639 1 1.000
#> 640 1 1.000
#> 641 1 1.000
#> 642 1 0.000
#> 643 1 1.000
#> 644 1 1.000
#> 645 2 0.000
#> 646 2 1.000
#> 647 2 0.000
#> 648 2 0.000
#> 649 2 0.249
#> 650 1 1.000
#> 651 1 1.000
#> 652 2 1.000
#> 653 1 1.000
#> 654 2 0.000
#> 655 2 0.000
#> 656 2 0.000
#> 657 1 1.000
#> 658 2 0.000
#> 659 2 0.000
#> 660 1 0.502
#> 661 2 0.000
#> 662 1 1.000
#> 663 1 1.000
#> 664 1 0.747
#> 665 1 1.000
#> 666 1 1.000
#> 667 1 1.000
#> 668 1 0.000
#> 669 1 0.249
#> 670 2 0.000
#> 671 1 1.000
#> 672 2 0.249
#> 673 1 1.000
#> 674 1 1.000
#> 675 2 0.000
#> 676 1 1.000
#> 677 2 0.000
#> 678 1 1.000
#> 679 2 0.000
#> 680 2 0.747
#> 681 2 0.000
#> 682 2 0.000
#> 683 2 0.000
#> 684 1 0.751
#> 685 1 1.000
#> 686 1 0.000
#> 687 1 1.000
#> 688 1 1.000
#> 689 1 1.000
#> 690 1 1.000
#> 691 2 0.000
#> 692 1 1.000
#> 693 1 1.000
#> 694 1 0.502
#> 695 2 0.000
#> 696 2 0.000
#> 697 2 0.000
#> 698 1 1.000
#> 699 1 0.000
#> 700 2 0.000
#> 701 1 1.000
#> 702 1 0.751
#> 703 2 0.747
#> 704 1 0.253
#> 705 1 0.000
#> 706 1 0.000
#> 707 2 0.000
#> 708 2 1.000
#> 709 2 0.253
#> 710 3 0.000
#> 711 2 0.000
#> 712 2 0.000
#> 713 2 0.000
#> 714 2 0.000
#> 715 3 0.000
#> 716 2 0.000
#> 717 2 0.000
#> 718 2 0.000
#> 719 2 0.751
#> 720 1 1.000
#> 721 2 0.000
#> 722 1 1.000
#> 723 1 0.502
#> 724 1 1.000
#> 725 2 0.000
#> 726 2 0.000
#> 727 2 0.000
#> 728 2 0.000
#> 729 2 0.000
#> 730 2 0.000
#> 731 2 0.000
#> 732 2 0.000
#> 733 2 0.000
#> 734 1 0.249
#> 735 1 1.000
#> 736 1 1.000
#> 737 2 0.000
#> 738 2 0.000
#> 739 2 0.000
#> 740 2 0.000
#> 741 2 0.000
#> 742 2 0.000
#> 743 1 1.000
#> 744 3 0.000
#> 745 3 0.249
#> 746 3 0.000
#> 747 1 1.000
#> 748 1 1.000
#> 749 1 1.000
#> 750 1 1.000
#> 751 1 1.000
#> 752 1 1.000
#> 753 3 0.000
#> 754 1 1.000
#> 755 1 1.000
#> 756 1 1.000
#> 757 3 0.000
#> 758 1 1.000
#> 759 3 0.000
#> 760 3 0.000
#> 761 3 0.000
#> 762 3 0.000
#> 763 3 0.000
#> 764 1 0.253
#> 765 3 1.000
#> 766 2 0.000
#> 767 2 1.000
#> 768 2 1.000
#> 769 3 1.000
#> 770 3 0.000
#> 771 3 0.000
#> 772 3 0.000
#> 773 3 0.000
#> 774 1 0.000
#> 775 2 1.000
#> 776 3 0.000
#> 777 3 0.000
#> 778 1 0.249
#> 779 1 0.000
#> 780 1 1.000
#> 781 1 1.000
#> 782 1 0.000
#> 783 2 0.000
#> 784 1 1.000
#> 785 2 0.000
#> 786 2 0.000
#> 787 2 0.000
#> 788 2 1.000
#> 789 2 0.000
#> 790 2 0.000
#> 791 1 0.498
#> 792 2 0.498
#> 793 2 0.000
#> 794 2 0.000
#> 795 2 0.000
#> 796 2 0.000
#> 797 2 0.253
#> 798 2 0.000
#> 799 2 0.000
#> 800 2 0.000
#> 801 3 0.000
#> 802 2 0.000
#> 803 2 0.000
#> 804 2 0.000
#> 805 2 0.000
#> 806 2 0.498
#> 807 2 0.253
#> 808 2 0.000
#> 809 2 1.000
#> 810 2 0.502
#> 811 2 0.000
#> 812 2 0.000
#> 813 2 0.000
#> 814 2 0.000
#> 815 2 0.000
#> 816 3 0.000
#> 817 3 0.000
#> 818 1 0.000
#> 819 1 0.000
#> 820 1 0.249
#> 821 1 1.000
#> 822 3 0.000
#> 823 3 1.000
#> 824 2 0.000
#> 825 1 1.000
#> 826 2 1.000
#> 827 2 0.000
#> 828 1 1.000
#> 829 2 0.498
#> 830 2 0.249
#> 831 2 0.000
#> 832 2 0.000
#> 833 3 1.000
#> 834 2 0.000
#> 835 3 0.747
#> 836 3 0.000
#> 837 2 0.000
#> 838 2 0.000
#> 839 1 0.000
#> 840 1 1.000
#> 841 2 0.000
#> 842 2 0.000
#> 843 1 0.502
#> 844 2 0.000
#> 845 2 0.000
#> 846 2 0.000
#> 847 2 0.000
#> 848 1 1.000
#> 849 1 0.000
#> 850 1 1.000
#> 851 1 1.000
#> 852 1 1.000
#> 853 1 1.000
#> 854 1 0.000
#> 855 3 0.000
#> 856 2 1.000
#> 857 1 1.000
#> 858 1 1.000
#> 859 1 0.000
#> 860 1 1.000
#> 861 1 0.000
#> 862 1 1.000
#> 863 1 0.000
#> 864 1 0.000
#> 865 1 0.000
#> 866 1 0.249
#> 867 1 1.000
#> 868 1 0.000
#> 869 1 0.502
#> 870 3 0.000
#> 871 1 1.000
#> 872 1 0.000
#> 873 1 1.000
#> 874 3 0.000
#> 875 1 1.000
#> 876 3 0.000
#> 877 1 1.000
#> 878 2 1.000
#> 879 3 0.000
#> 880 3 0.000
#> 881 1 0.000
#> 882 1 0.000
#> 883 1 0.000
#> 884 1 0.000
#> 885 1 0.000
#> 886 1 0.000
#> 887 1 1.000
#> 888 1 0.000
#> 889 3 0.000
#> 890 1 0.000
#> 891 1 1.000
#> 892 1 1.000
#> 893 1 1.000
#> 894 3 0.000
#> 895 1 0.502
#> 896 1 0.000
#> 897 1 0.000
#> 898 1 0.000
#> 899 1 0.000
#> 900 1 0.000
#> 901 1 0.000
#> 902 1 0.000
#> 903 2 1.000
#> 904 2 1.000
#> 905 1 0.498
#> 906 3 0.000
#> 907 1 0.000
#> 908 1 0.000
#> 909 1 0.000
#> 910 1 0.249
#> 911 1 0.253
#> 912 1 0.000
#> 913 2 1.000
#> 914 1 0.000
#> 915 1 0.000
#> 916 1 0.000
#> 917 1 0.000
#> 918 3 0.249
#> 919 2 1.000
#> 920 1 1.000
#> 921 1 0.000
#> 922 3 0.000
#> 923 3 1.000
#> 924 1 1.000
#> 925 1 1.000
#> 926 1 0.000
#> 927 1 0.000
#> 928 1 0.000
#> 929 1 0.000
#> 930 1 0.747
#> 931 1 0.000
#> 932 1 0.253
#> 933 1 0.000
#> 934 1 0.249
#> 935 1 0.000
#> 936 3 0.000
#> 937 2 1.000
#> 938 1 1.000
#> 939 2 1.000
#> 940 1 1.000
#> 941 3 0.000
#> 942 3 0.000
#> 943 2 0.249
#> 944 2 0.000
#> 945 1 0.253
#> 946 1 1.000
#> 947 3 0.498
#> 948 2 0.000
#> 949 1 0.000
#> 950 2 1.000
#> 951 1 0.000
#> 952 1 0.000
#> 953 1 0.502
#> 954 1 1.000
#> 955 1 0.000
#> 956 1 1.000
#> 957 2 1.000
#> 958 1 0.000
#> 959 1 0.000
#> 960 1 0.000
get_classes(res, k = 4)
#> class p
#> 1 3 1.000
#> 2 2 1.000
#> 3 4 0.000
#> 4 4 0.000
#> 5 2 0.000
#> 6 2 0.000
#> 7 2 0.000
#> 8 1 0.000
#> 9 2 1.000
#> 10 2 0.751
#> 11 2 0.000
#> 12 2 1.000
#> 13 2 0.000
#> 14 2 1.000
#> 15 3 0.000
#> 16 4 0.000
#> 17 2 1.000
#> 18 4 1.000
#> 19 3 1.000
#> 20 2 0.502
#> 21 4 0.000
#> 22 2 1.000
#> 23 2 0.249
#> 24 2 0.000
#> 25 2 1.000
#> 26 4 0.000
#> 27 1 0.000
#> 28 1 1.000
#> 29 2 1.000
#> 30 4 0.000
#> 31 4 0.000
#> 32 1 0.000
#> 33 1 1.000
#> 34 3 0.000
#> 35 1 1.000
#> 36 4 0.000
#> 37 2 1.000
#> 38 4 0.000
#> 39 2 0.751
#> 40 4 0.000
#> 41 2 1.000
#> 42 4 0.000
#> 43 4 0.000
#> 44 2 1.000
#> 45 2 0.000
#> 46 4 1.000
#> 47 4 0.000
#> 48 3 0.000
#> 49 3 0.000
#> 50 3 0.000
#> 51 2 1.000
#> 52 4 0.000
#> 53 3 1.000
#> 54 3 1.000
#> 55 4 0.000
#> 56 2 1.000
#> 57 4 0.000
#> 58 4 0.000
#> 59 4 0.000
#> 60 4 1.000
#> 61 1 1.000
#> 62 1 1.000
#> 63 1 1.000
#> 64 3 1.000
#> 65 2 1.000
#> 66 2 1.000
#> 67 4 0.000
#> 68 4 0.000
#> 69 4 0.000
#> 70 4 0.000
#> 71 4 0.000
#> 72 2 1.000
#> 73 2 1.000
#> 74 4 1.000
#> 75 2 0.000
#> 76 2 1.000
#> 77 3 0.000
#> 78 1 1.000
#> 79 2 0.249
#> 80 3 0.000
#> 81 2 0.502
#> 82 2 1.000
#> 83 3 0.751
#> 84 2 1.000
#> 85 4 0.000
#> 86 2 0.000
#> 87 2 0.751
#> 88 4 0.000
#> 89 2 1.000
#> 90 2 0.000
#> 91 3 1.000
#> 92 2 0.751
#> 93 4 0.000
#> 94 2 1.000
#> 95 4 0.000
#> 96 1 1.000
#> 97 2 0.751
#> 98 3 0.000
#> 99 4 0.747
#> 100 3 0.000
#> 101 4 0.000
#> 102 3 0.000
#> 103 2 1.000
#> 104 4 0.000
#> 105 2 0.000
#> 106 2 1.000
#> 107 2 0.751
#> 108 2 0.000
#> 109 2 0.747
#> 110 4 0.000
#> 111 1 1.000
#> 112 4 0.000
#> 113 4 0.000
#> 114 2 1.000
#> 115 4 0.000
#> 116 4 0.000
#> 117 2 0.498
#> 118 2 0.249
#> 119 2 1.000
#> 120 1 0.498
#> 121 2 0.000
#> 122 2 1.000
#> 123 2 0.000
#> 124 2 0.000
#> 125 2 0.000
#> 126 2 1.000
#> 127 2 0.000
#> 128 2 0.000
#> 129 2 0.000
#> 130 4 0.000
#> 131 4 0.000
#> 132 3 0.000
#> 133 2 1.000
#> 134 2 1.000
#> 135 1 1.000
#> 136 1 0.000
#> 137 3 1.000
#> 138 4 0.000
#> 139 3 0.000
#> 140 1 1.000
#> 141 1 1.000
#> 142 2 1.000
#> 143 1 1.000
#> 144 4 0.000
#> 145 3 1.000
#> 146 1 0.000
#> 147 3 0.000
#> 148 3 0.000
#> 149 3 0.000
#> 150 3 0.000
#> 151 3 0.000
#> 152 2 0.000
#> 153 2 0.000
#> 154 2 0.000
#> 155 4 0.000
#> 156 2 0.000
#> 157 2 1.000
#> 158 3 0.751
#> 159 2 0.000
#> 160 2 1.000
#> 161 2 1.000
#> 162 2 1.000
#> 163 3 0.000
#> 164 3 0.249
#> 165 2 1.000
#> 166 1 1.000
#> 167 2 1.000
#> 168 4 1.000
#> 169 3 0.000
#> 170 4 0.000
#> 171 4 0.000
#> 172 4 0.000
#> 173 4 0.000
#> 174 1 0.000
#> 175 3 0.000
#> 176 3 0.000
#> 177 1 0.000
#> 178 3 0.000
#> 179 2 0.000
#> 180 1 0.000
#> 181 4 0.000
#> 182 3 0.000
#> 183 1 0.000
#> 184 4 0.000
#> 185 1 0.000
#> 186 3 1.000
#> 187 4 1.000
#> 188 1 1.000
#> 189 1 0.751
#> 190 2 0.000
#> 191 1 0.000
#> 192 3 0.000
#> 193 4 0.000
#> 194 4 0.000
#> 195 2 0.000
#> 196 3 0.000
#> 197 3 1.000
#> 198 3 1.000
#> 199 1 0.253
#> 200 2 1.000
#> 201 2 0.249
#> 202 4 0.000
#> 203 2 0.502
#> 204 2 0.000
#> 205 2 0.747
#> 206 2 1.000
#> 207 3 0.000
#> 208 1 0.000
#> 209 2 0.249
#> 210 2 0.000
#> 211 3 1.000
#> 212 1 0.000
#> 213 2 1.000
#> 214 4 0.000
#> 215 4 0.249
#> 216 4 0.000
#> 217 3 0.000
#> 218 1 1.000
#> 219 2 1.000
#> 220 4 0.000
#> 221 3 0.000
#> 222 1 1.000
#> 223 3 0.000
#> 224 2 1.000
#> 225 4 0.000
#> 226 3 0.000
#> 227 1 1.000
#> 228 2 0.000
#> 229 2 0.249
#> 230 1 1.000
#> 231 3 0.000
#> 232 4 1.000
#> 233 4 0.000
#> 234 1 0.498
#> 235 3 0.253
#> 236 1 0.000
#> 237 3 0.000
#> 238 4 0.751
#> 239 1 0.249
#> 240 1 1.000
#> 241 1 0.000
#> 242 2 0.000
#> 243 1 1.000
#> 244 3 0.000
#> 245 3 0.000
#> 246 4 0.000
#> 247 4 0.000
#> 248 3 1.000
#> 249 3 0.000
#> 250 1 0.249
#> 251 3 0.249
#> 252 4 1.000
#> 253 1 0.000
#> 254 1 0.000
#> 255 3 0.000
#> 256 1 0.000
#> 257 4 0.000
#> 258 2 1.000
#> 259 4 0.000
#> 260 3 0.000
#> 261 3 0.000
#> 262 4 0.000
#> 263 3 1.000
#> 264 4 0.000
#> 265 3 1.000
#> 266 2 1.000
#> 267 3 1.000
#> 268 4 1.000
#> 269 1 0.000
#> 270 4 0.000
#> 271 2 1.000
#> 272 4 0.000
#> 273 2 0.000
#> 274 2 1.000
#> 275 2 0.498
#> 276 4 0.000
#> 277 4 0.000
#> 278 4 0.000
#> 279 2 0.747
#> 280 4 0.000
#> 281 2 1.000
#> 282 3 0.000
#> 283 2 0.249
#> 284 1 0.502
#> 285 2 0.249
#> 286 1 1.000
#> 287 2 0.249
#> 288 4 1.000
#> 289 2 0.000
#> 290 1 1.000
#> 291 3 0.000
#> 292 3 0.000
#> 293 1 0.000
#> 294 4 0.000
#> 295 4 0.000
#> 296 4 0.000
#> 297 2 0.249
#> 298 1 0.000
#> 299 3 1.000
#> 300 1 0.000
#> 301 1 0.000
#> 302 4 0.000
#> 303 2 1.000
#> 304 4 0.000
#> 305 2 1.000
#> 306 1 1.000
#> 307 2 1.000
#> 308 4 0.000
#> 309 4 0.000
#> 310 2 0.000
#> 311 4 1.000
#> 312 1 1.000
#> 313 4 0.000
#> 314 1 1.000
#> 315 1 1.000
#> 316 1 1.000
#> 317 4 0.000
#> 318 3 0.000
#> 319 2 1.000
#> 320 1 0.000
#> 321 3 0.000
#> 322 1 1.000
#> 323 4 0.000
#> 324 1 1.000
#> 325 1 0.751
#> 326 1 1.000
#> 327 4 0.000
#> 328 4 0.000
#> 329 2 1.000
#> 330 1 1.000
#> 331 4 1.000
#> 332 4 1.000
#> 333 3 0.000
#> 334 4 0.000
#> 335 2 1.000
#> 336 4 0.000
#> 337 2 0.249
#> 338 4 0.000
#> 339 1 1.000
#> 340 2 0.249
#> 341 2 1.000
#> 342 2 1.000
#> 343 4 0.000
#> 344 2 1.000
#> 345 3 1.000
#> 346 4 0.249
#> 347 4 0.502
#> 348 4 0.000
#> 349 4 1.000
#> 350 1 1.000
#> 351 1 1.000
#> 352 1 0.747
#> 353 1 0.000
#> 354 3 0.000
#> 355 1 0.000
#> 356 3 0.000
#> 357 4 0.000
#> 358 1 0.000
#> 359 1 0.000
#> 360 1 0.000
#> 361 1 0.000
#> 362 3 1.000
#> 363 4 0.000
#> 364 3 0.000
#> 365 3 0.000
#> 366 4 0.000
#> 367 4 0.000
#> 368 3 0.000
#> 369 3 0.000
#> 370 2 0.502
#> 371 1 0.000
#> 372 1 1.000
#> 373 1 0.000
#> 374 1 1.000
#> 375 4 0.000
#> 376 1 1.000
#> 377 1 0.000
#> 378 1 1.000
#> 379 1 0.498
#> 380 1 1.000
#> 381 4 0.000
#> 382 2 0.751
#> 383 1 1.000
#> 384 2 0.000
#> 385 4 0.000
#> 386 4 0.000
#> 387 2 0.000
#> 388 2 0.000
#> 389 2 0.000
#> 390 2 0.249
#> 391 4 0.000
#> 392 4 1.000
#> 393 4 0.000
#> 394 1 1.000
#> 395 1 1.000
#> 396 2 1.000
#> 397 3 0.000
#> 398 1 1.000
#> 399 1 1.000
#> 400 4 0.000
#> 401 4 0.000
#> 402 2 1.000
#> 403 1 1.000
#> 404 2 0.000
#> 405 4 0.000
#> 406 2 0.502
#> 407 4 1.000
#> 408 4 1.000
#> 409 4 0.000
#> 410 2 0.000
#> 411 4 0.000
#> 412 2 0.751
#> 413 2 1.000
#> 414 2 1.000
#> 415 2 0.000
#> 416 4 0.000
#> 417 2 0.000
#> 418 2 0.000
#> 419 2 0.000
#> 420 1 0.000
#> 421 1 1.000
#> 422 1 0.000
#> 423 4 0.000
#> 424 2 0.000
#> 425 3 0.000
#> 426 4 1.000
#> 427 2 1.000
#> 428 2 0.000
#> 429 2 0.000
#> 430 2 0.000
#> 431 2 0.000
#> 432 2 0.000
#> 433 2 1.000
#> 434 2 0.751
#> 435 2 0.000
#> 436 1 0.000
#> 437 1 1.000
#> 438 2 0.751
#> 439 3 0.000
#> 440 2 1.000
#> 441 4 0.000
#> 442 3 0.000
#> 443 3 0.000
#> 444 2 0.502
#> 445 1 0.000
#> 446 4 0.498
#> 447 1 0.000
#> 448 3 0.000
#> 449 3 0.502
#> 450 2 0.000
#> 451 3 0.000
#> 452 4 1.000
#> 453 4 0.000
#> 454 4 0.000
#> 455 1 0.000
#> 456 1 1.000
#> 457 1 0.000
#> 458 1 0.751
#> 459 2 0.000
#> 460 4 0.249
#> 461 1 0.000
#> 462 1 0.000
#> 463 2 1.000
#> 464 1 0.000
#> 465 1 0.000
#> 466 1 0.000
#> 467 2 1.000
#> 468 4 0.000
#> 469 4 0.000
#> 470 4 0.000
#> 471 4 1.000
#> 472 2 1.000
#> 473 4 0.000
#> 474 2 0.000
#> 475 2 0.000
#> 476 2 0.249
#> 477 2 0.000
#> 478 2 1.000
#> 479 2 0.000
#> 480 2 0.502
#> 481 2 1.000
#> 482 2 0.498
#> 483 2 1.000
#> 484 3 0.000
#> 485 2 0.000
#> 486 2 1.000
#> 487 2 1.000
#> 488 2 0.253
#> 489 2 1.000
#> 490 3 1.000
#> 491 2 0.000
#> 492 2 0.000
#> 493 1 0.000
#> 494 2 0.000
#> 495 2 0.000
#> 496 2 1.000
#> 497 4 0.000
#> 498 3 1.000
#> 499 2 0.000
#> 500 4 0.249
#> 501 2 0.751
#> 502 2 1.000
#> 503 2 0.747
#> 504 4 0.000
#> 505 2 0.249
#> 506 2 0.000
#> 507 3 0.000
#> 508 4 0.502
#> 509 2 0.000
#> 510 2 0.000
#> 511 2 0.000
#> 512 2 0.000
#> 513 2 0.000
#> 514 2 0.000
#> 515 2 1.000
#> 516 2 0.253
#> 517 2 0.000
#> 518 2 0.000
#> 519 2 0.000
#> 520 2 0.751
#> 521 4 0.000
#> 522 2 1.000
#> 523 2 0.000
#> 524 1 1.000
#> 525 3 1.000
#> 526 2 0.249
#> 527 2 0.000
#> 528 2 0.751
#> 529 2 0.249
#> 530 2 0.000
#> 531 2 1.000
#> 532 2 0.000
#> 533 2 1.000
#> 534 2 0.000
#> 535 2 0.000
#> 536 2 0.000
#> 537 2 0.000
#> 538 4 0.000
#> 539 2 0.249
#> 540 2 0.000
#> 541 4 1.000
#> 542 3 0.000
#> 543 2 0.000
#> 544 4 0.000
#> 545 2 1.000
#> 546 2 0.000
#> 547 2 0.502
#> 548 4 0.000
#> 549 4 0.000
#> 550 4 0.751
#> 551 2 0.000
#> 552 2 1.000
#> 553 4 0.000
#> 554 1 0.249
#> 555 2 1.000
#> 556 1 1.000
#> 557 2 0.747
#> 558 4 0.000
#> 559 3 0.000
#> 560 4 0.000
#> 561 1 0.249
#> 562 2 0.249
#> 563 1 0.000
#> 564 1 0.000
#> 565 2 1.000
#> 566 4 0.000
#> 567 4 0.000
#> 568 4 0.000
#> 569 2 0.253
#> 570 4 1.000
#> 571 4 0.751
#> 572 2 0.000
#> 573 2 1.000
#> 574 4 0.000
#> 575 4 0.000
#> 576 3 0.000
#> 577 4 0.000
#> 578 1 1.000
#> 579 4 1.000
#> 580 3 0.000
#> 581 2 0.751
#> 582 2 0.000
#> 583 2 0.000
#> 584 2 1.000
#> 585 3 0.000
#> 586 1 0.747
#> 587 4 0.000
#> 588 2 0.502
#> 589 2 1.000
#> 590 1 1.000
#> 591 4 0.000
#> 592 1 1.000
#> 593 2 1.000
#> 594 2 0.000
#> 595 1 1.000
#> 596 2 0.000
#> 597 3 0.000
#> 598 1 0.000
#> 599 4 0.000
#> 600 4 0.000
#> 601 4 0.000
#> 602 1 1.000
#> 603 2 0.498
#> 604 4 1.000
#> 605 2 0.249
#> 606 4 0.498
#> 607 2 0.000
#> 608 4 0.000
#> 609 2 1.000
#> 610 4 0.000
#> 611 2 1.000
#> 612 4 0.000
#> 613 1 1.000
#> 614 3 1.000
#> 615 2 1.000
#> 616 2 0.000
#> 617 4 1.000
#> 618 4 1.000
#> 619 2 0.000
#> 620 2 0.000
#> 621 2 0.000
#> 622 4 0.000
#> 623 4 0.000
#> 624 2 0.502
#> 625 4 0.000
#> 626 2 0.751
#> 627 4 0.000
#> 628 4 1.000
#> 629 4 1.000
#> 630 1 1.000
#> 631 1 1.000
#> 632 4 0.000
#> 633 1 1.000
#> 634 4 0.000
#> 635 4 0.000
#> 636 3 1.000
#> 637 1 1.000
#> 638 4 0.000
#> 639 4 0.000
#> 640 1 1.000
#> 641 4 0.000
#> 642 1 1.000
#> 643 4 0.000
#> 644 4 0.000
#> 645 2 0.000
#> 646 2 1.000
#> 647 2 0.249
#> 648 2 0.000
#> 649 2 0.249
#> 650 4 0.249
#> 651 4 1.000
#> 652 2 1.000
#> 653 4 0.000
#> 654 4 0.000
#> 655 2 1.000
#> 656 2 0.000
#> 657 4 1.000
#> 658 4 1.000
#> 659 2 0.747
#> 660 1 1.000
#> 661 4 1.000
#> 662 4 0.000
#> 663 4 1.000
#> 664 4 1.000
#> 665 1 1.000
#> 666 4 1.000
#> 667 4 0.000
#> 668 1 0.000
#> 669 1 0.498
#> 670 4 1.000
#> 671 4 0.000
#> 672 2 1.000
#> 673 4 1.000
#> 674 4 0.253
#> 675 2 0.000
#> 676 4 0.000
#> 677 2 1.000
#> 678 4 0.000
#> 679 4 0.000
#> 680 4 0.751
#> 681 2 0.000
#> 682 4 0.000
#> 683 2 0.000
#> 684 1 1.000
#> 685 4 0.000
#> 686 1 1.000
#> 687 4 0.000
#> 688 4 0.000
#> 689 1 1.000
#> 690 4 0.000
#> 691 2 1.000
#> 692 4 0.000
#> 693 4 0.000
#> 694 1 0.253
#> 695 2 0.000
#> 696 2 0.000
#> 697 2 0.000
#> 698 4 0.000
#> 699 1 1.000
#> 700 2 0.498
#> 701 4 0.000
#> 702 1 1.000
#> 703 4 0.249
#> 704 1 1.000
#> 705 1 1.000
#> 706 1 1.000
#> 707 2 0.249
#> 708 2 1.000
#> 709 2 0.253
#> 710 3 0.000
#> 711 2 0.000
#> 712 2 0.502
#> 713 2 0.000
#> 714 2 0.000
#> 715 3 0.000
#> 716 2 0.000
#> 717 2 0.502
#> 718 4 0.000
#> 719 2 0.751
#> 720 4 0.000
#> 721 2 0.000
#> 722 1 1.000
#> 723 1 1.000
#> 724 4 0.000
#> 725 2 1.000
#> 726 2 0.000
#> 727 2 0.000
#> 728 2 0.000
#> 729 2 0.000
#> 730 2 0.751
#> 731 4 0.000
#> 732 2 0.000
#> 733 2 0.000
#> 734 1 1.000
#> 735 4 0.000
#> 736 1 1.000
#> 737 4 0.000
#> 738 2 1.000
#> 739 2 0.000
#> 740 2 0.000
#> 741 2 0.000
#> 742 2 0.000
#> 743 1 0.000
#> 744 3 0.000
#> 745 3 1.000
#> 746 3 0.000
#> 747 1 0.000
#> 748 1 0.000
#> 749 1 0.000
#> 750 1 0.000
#> 751 1 0.000
#> 752 1 0.000
#> 753 3 0.000
#> 754 1 0.000
#> 755 1 0.000
#> 756 1 0.000
#> 757 3 0.000
#> 758 1 0.000
#> 759 3 0.000
#> 760 3 0.000
#> 761 3 0.000
#> 762 3 0.253
#> 763 3 0.000
#> 764 1 1.000
#> 765 3 1.000
#> 766 2 0.498
#> 767 2 0.751
#> 768 2 1.000
#> 769 3 0.253
#> 770 3 1.000
#> 771 3 0.000
#> 772 3 0.000
#> 773 3 0.000
#> 774 1 0.000
#> 775 3 0.000
#> 776 3 0.000
#> 777 3 0.000
#> 778 1 1.000
#> 779 1 1.000
#> 780 1 0.000
#> 781 1 0.000
#> 782 1 0.000
#> 783 4 0.000
#> 784 4 0.000
#> 785 2 1.000
#> 786 2 1.000
#> 787 2 0.000
#> 788 2 1.000
#> 789 2 0.000
#> 790 2 0.000
#> 791 1 1.000
#> 792 2 0.498
#> 793 2 0.000
#> 794 4 0.249
#> 795 2 0.751
#> 796 2 0.000
#> 797 2 0.000
#> 798 2 0.000
#> 799 2 0.000
#> 800 2 1.000
#> 801 3 0.000
#> 802 2 0.253
#> 803 2 0.000
#> 804 2 0.000
#> 805 2 0.000
#> 806 2 0.747
#> 807 2 0.000
#> 808 2 0.000
#> 809 2 1.000
#> 810 2 0.498
#> 811 2 0.000
#> 812 2 0.000
#> 813 2 0.000
#> 814 2 0.000
#> 815 2 0.000
#> 816 3 0.000
#> 817 3 0.000
#> 818 1 0.000
#> 819 1 0.000
#> 820 1 1.000
#> 821 4 0.249
#> 822 3 0.000
#> 823 3 0.000
#> 824 2 0.498
#> 825 4 0.000
#> 826 2 1.000
#> 827 2 0.000
#> 828 1 0.000
#> 829 2 0.249
#> 830 2 0.000
#> 831 2 0.000
#> 832 2 0.000
#> 833 3 1.000
#> 834 4 0.751
#> 835 3 1.000
#> 836 3 0.000
#> 837 4 0.000
#> 838 4 0.000
#> 839 1 0.000
#> 840 4 0.000
#> 841 2 0.000
#> 842 2 0.000
#> 843 4 1.000
#> 844 2 0.000
#> 845 2 0.000
#> 846 2 0.000
#> 847 2 0.000
#> 848 1 0.000
#> 849 1 0.502
#> 850 1 0.000
#> 851 1 0.000
#> 852 1 0.000
#> 853 1 0.000
#> 854 1 0.000
#> 855 3 0.000
#> 856 2 1.000
#> 857 1 0.000
#> 858 1 0.000
#> 859 1 0.000
#> 860 1 0.000
#> 861 1 0.502
#> 862 1 0.000
#> 863 1 1.000
#> 864 1 0.000
#> 865 1 0.249
#> 866 1 0.000
#> 867 1 0.000
#> 868 1 0.000
#> 869 1 0.000
#> 870 3 0.000
#> 871 1 0.000
#> 872 1 0.000
#> 873 1 0.000
#> 874 3 0.000
#> 875 1 0.000
#> 876 3 0.000
#> 877 1 0.000
#> 878 4 0.751
#> 879 3 0.000
#> 880 3 0.249
#> 881 1 0.249
#> 882 1 0.253
#> 883 1 0.000
#> 884 1 0.249
#> 885 1 0.000
#> 886 1 0.000
#> 887 1 0.000
#> 888 1 1.000
#> 889 3 0.000
#> 890 1 0.000
#> 891 1 0.000
#> 892 1 0.000
#> 893 1 0.000
#> 894 3 0.000
#> 895 1 0.000
#> 896 1 0.000
#> 897 1 0.000
#> 898 1 0.498
#> 899 1 0.751
#> 900 1 0.000
#> 901 1 1.000
#> 902 1 0.751
#> 903 2 1.000
#> 904 2 1.000
#> 905 4 1.000
#> 906 3 0.000
#> 907 1 0.000
#> 908 1 1.000
#> 909 1 0.502
#> 910 1 0.000
#> 911 4 0.000
#> 912 1 0.000
#> 913 4 1.000
#> 914 1 1.000
#> 915 1 0.000
#> 916 1 0.000
#> 917 4 1.000
#> 918 3 0.000
#> 919 2 1.000
#> 920 1 0.253
#> 921 4 0.751
#> 922 3 0.000
#> 923 3 0.000
#> 924 1 0.000
#> 925 1 0.000
#> 926 1 1.000
#> 927 1 1.000
#> 928 4 0.249
#> 929 1 1.000
#> 930 1 1.000
#> 931 1 0.000
#> 932 1 0.751
#> 933 1 0.000
#> 934 1 0.000
#> 935 1 0.000
#> 936 3 0.000
#> 937 2 1.000
#> 938 4 0.000
#> 939 2 1.000
#> 940 1 0.000
#> 941 3 0.000
#> 942 3 0.498
#> 943 4 0.000
#> 944 2 1.000
#> 945 4 0.000
#> 946 4 0.000
#> 947 3 1.000
#> 948 2 0.000
#> 949 4 1.000
#> 950 2 1.000
#> 951 1 0.000
#> 952 4 1.000
#> 953 4 0.000
#> 954 1 0.000
#> 955 1 0.253
#> 956 1 0.000
#> 957 4 0.751
#> 958 1 1.000
#> 959 4 1.000
#> 960 4 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 590 7.23e-08 2
#> ATC:skmeans 526 2.08e-04 3
#> ATC:skmeans 542 4.67e-06 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["021"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9688 rows and 386 columns.
#> Top rows (969) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.970 0.988 0.4999 0.501 0.501
#> 3 3 0.941 0.944 0.976 0.2689 0.824 0.663
#> 4 4 0.820 0.863 0.926 0.0885 0.940 0.836
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.987 0.00 1.00
#> 2 2 0.000 0.987 0.00 1.00
#> 3 1 0.000 0.988 1.00 0.00
#> 4 2 0.000 0.987 0.00 1.00
#> 5 1 0.000 0.988 1.00 0.00
#> 6 1 0.958 0.389 0.62 0.38
#> 7 2 0.000 0.987 0.00 1.00
#> 8 1 0.000 0.988 1.00 0.00
#> 9 1 0.000 0.988 1.00 0.00
#> 10 1 0.000 0.988 1.00 0.00
#> 11 2 0.000 0.987 0.00 1.00
#> 12 2 0.000 0.987 0.00 1.00
#> 13 2 0.000 0.987 0.00 1.00
#> 14 2 0.990 0.215 0.44 0.56
#> 15 2 0.000 0.987 0.00 1.00
#> 16 1 0.000 0.988 1.00 0.00
#> 17 1 0.000 0.988 1.00 0.00
#> 18 1 0.000 0.988 1.00 0.00
#> 19 2 0.000 0.987 0.00 1.00
#> 20 2 0.000 0.987 0.00 1.00
#> 21 2 0.000 0.987 0.00 1.00
#> 22 1 0.000 0.988 1.00 0.00
#> 23 2 0.000 0.987 0.00 1.00
#> 24 2 0.000 0.987 0.00 1.00
#> 25 1 0.000 0.988 1.00 0.00
#> 26 2 0.000 0.987 0.00 1.00
#> 27 2 0.000 0.987 0.00 1.00
#> 28 2 0.000 0.987 0.00 1.00
#> 29 2 0.000 0.987 0.00 1.00
#> 30 2 0.000 0.987 0.00 1.00
#> 31 1 0.000 0.988 1.00 0.00
#> 32 2 0.000 0.987 0.00 1.00
#> 33 2 0.000 0.987 0.00 1.00
#> 34 1 0.000 0.988 1.00 0.00
#> 35 1 0.000 0.988 1.00 0.00
#> 36 1 0.000 0.988 1.00 0.00
#> 37 2 0.000 0.987 0.00 1.00
#> 38 2 0.000 0.987 0.00 1.00
#> 39 1 0.000 0.988 1.00 0.00
#> 40 2 0.000 0.987 0.00 1.00
#> 41 1 0.000 0.988 1.00 0.00
#> 42 2 0.000 0.987 0.00 1.00
#> 43 2 0.000 0.987 0.00 1.00
#> 44 2 0.000 0.987 0.00 1.00
#> 45 1 0.000 0.988 1.00 0.00
#> 46 1 0.000 0.988 1.00 0.00
#> 47 1 0.000 0.988 1.00 0.00
#> 48 2 0.000 0.987 0.00 1.00
#> 49 1 0.000 0.988 1.00 0.00
#> 50 2 0.000 0.987 0.00 1.00
#> 51 1 0.000 0.988 1.00 0.00
#> 52 1 0.000 0.988 1.00 0.00
#> 53 1 0.000 0.988 1.00 0.00
#> 54 1 0.000 0.988 1.00 0.00
#> 55 1 0.000 0.988 1.00 0.00
#> 56 2 0.000 0.987 0.00 1.00
#> 57 2 0.000 0.987 0.00 1.00
#> 58 2 0.000 0.987 0.00 1.00
#> 59 2 0.000 0.987 0.00 1.00
#> 60 2 0.925 0.487 0.34 0.66
#> 61 2 0.000 0.987 0.00 1.00
#> 62 2 0.000 0.987 0.00 1.00
#> 63 1 0.000 0.988 1.00 0.00
#> 64 1 0.000 0.988 1.00 0.00
#> 65 2 0.000 0.987 0.00 1.00
#> 66 1 0.000 0.988 1.00 0.00
#> 67 1 0.958 0.391 0.62 0.38
#> 68 2 0.000 0.987 0.00 1.00
#> 69 2 0.000 0.987 0.00 1.00
#> 70 2 0.000 0.987 0.00 1.00
#> 71 1 0.000 0.988 1.00 0.00
#> 72 2 0.000 0.987 0.00 1.00
#> 73 2 0.000 0.987 0.00 1.00
#> 74 2 0.141 0.969 0.02 0.98
#> 75 1 0.000 0.988 1.00 0.00
#> 76 2 0.000 0.987 0.00 1.00
#> 77 2 0.000 0.987 0.00 1.00
#> 78 1 0.000 0.988 1.00 0.00
#> 79 1 0.000 0.988 1.00 0.00
#> 80 1 0.000 0.988 1.00 0.00
#> 81 1 0.000 0.988 1.00 0.00
#> 82 2 0.000 0.987 0.00 1.00
#> 83 2 0.000 0.987 0.00 1.00
#> 84 2 0.000 0.987 0.00 1.00
#> 85 1 0.000 0.988 1.00 0.00
#> 86 2 0.000 0.987 0.00 1.00
#> 87 2 0.000 0.987 0.00 1.00
#> 88 2 0.000 0.987 0.00 1.00
#> 89 2 0.000 0.987 0.00 1.00
#> 90 1 0.000 0.988 1.00 0.00
#> 91 1 0.000 0.988 1.00 0.00
#> 92 1 0.000 0.988 1.00 0.00
#> 93 1 0.000 0.988 1.00 0.00
#> 94 2 0.000 0.987 0.00 1.00
#> 95 2 0.000 0.987 0.00 1.00
#> 96 2 0.000 0.987 0.00 1.00
#> 97 1 0.000 0.988 1.00 0.00
#> 98 1 0.000 0.988 1.00 0.00
#> 99 1 0.000 0.988 1.00 0.00
#> 100 2 0.000 0.987 0.00 1.00
#> 101 2 0.000 0.987 0.00 1.00
#> 102 1 0.000 0.988 1.00 0.00
#> 103 2 0.000 0.987 0.00 1.00
#> 104 2 0.000 0.987 0.00 1.00
#> 105 2 0.000 0.987 0.00 1.00
#> 106 2 0.000 0.987 0.00 1.00
#> 107 1 0.000 0.988 1.00 0.00
#> 108 1 0.000 0.988 1.00 0.00
#> 109 1 0.000 0.988 1.00 0.00
#> 110 1 0.402 0.907 0.92 0.08
#> 111 1 0.000 0.988 1.00 0.00
#> 112 1 0.000 0.988 1.00 0.00
#> 113 1 0.000 0.988 1.00 0.00
#> 114 2 0.000 0.987 0.00 1.00
#> 115 2 0.000 0.987 0.00 1.00
#> 116 2 0.000 0.987 0.00 1.00
#> 117 2 0.000 0.987 0.00 1.00
#> 118 2 0.000 0.987 0.00 1.00
#> 119 2 0.000 0.987 0.00 1.00
#> 120 2 0.000 0.987 0.00 1.00
#> 121 2 0.000 0.987 0.00 1.00
#> 122 2 0.000 0.987 0.00 1.00
#> 123 2 0.000 0.987 0.00 1.00
#> 124 2 0.000 0.987 0.00 1.00
#> 125 2 0.000 0.987 0.00 1.00
#> 126 2 0.000 0.987 0.00 1.00
#> 127 1 0.000 0.988 1.00 0.00
#> 128 1 0.000 0.988 1.00 0.00
#> 129 2 0.000 0.987 0.00 1.00
#> 130 1 0.000 0.988 1.00 0.00
#> 131 1 0.000 0.988 1.00 0.00
#> 132 1 0.000 0.988 1.00 0.00
#> 133 1 0.000 0.988 1.00 0.00
#> 134 1 0.000 0.988 1.00 0.00
#> 135 1 0.000 0.988 1.00 0.00
#> 136 2 0.000 0.987 0.00 1.00
#> 137 2 0.000 0.987 0.00 1.00
#> 138 2 0.000 0.987 0.00 1.00
#> 139 2 0.000 0.987 0.00 1.00
#> 140 1 0.000 0.988 1.00 0.00
#> 141 1 0.000 0.988 1.00 0.00
#> 142 2 0.925 0.489 0.34 0.66
#> 143 2 0.000 0.987 0.00 1.00
#> 144 1 0.141 0.969 0.98 0.02
#> 145 1 0.000 0.988 1.00 0.00
#> 146 1 0.000 0.988 1.00 0.00
#> 147 2 0.000 0.987 0.00 1.00
#> 148 2 0.469 0.883 0.10 0.90
#> 149 2 0.000 0.987 0.00 1.00
#> 150 2 0.000 0.987 0.00 1.00
#> 151 2 0.000 0.987 0.00 1.00
#> 152 2 0.000 0.987 0.00 1.00
#> 153 2 0.000 0.987 0.00 1.00
#> 154 1 0.000 0.988 1.00 0.00
#> 155 2 0.000 0.987 0.00 1.00
#> 156 2 0.000 0.987 0.00 1.00
#> 157 1 0.000 0.988 1.00 0.00
#> 158 2 0.000 0.987 0.00 1.00
#> 159 1 0.584 0.835 0.86 0.14
#> 160 2 0.000 0.987 0.00 1.00
#> 161 2 0.000 0.987 0.00 1.00
#> 162 2 0.000 0.987 0.00 1.00
#> 163 1 0.000 0.988 1.00 0.00
#> 164 2 0.000 0.987 0.00 1.00
#> 165 2 0.141 0.969 0.02 0.98
#> 166 2 0.000 0.987 0.00 1.00
#> 167 1 0.000 0.988 1.00 0.00
#> 168 1 0.000 0.988 1.00 0.00
#> 169 2 0.469 0.884 0.10 0.90
#> 170 2 0.000 0.987 0.00 1.00
#> 171 2 0.881 0.573 0.30 0.70
#> 172 2 0.000 0.987 0.00 1.00
#> 173 2 0.000 0.987 0.00 1.00
#> 174 1 0.000 0.988 1.00 0.00
#> 175 1 0.000 0.988 1.00 0.00
#> 176 2 0.000 0.987 0.00 1.00
#> 177 2 0.000 0.987 0.00 1.00
#> 178 2 0.000 0.987 0.00 1.00
#> 179 2 0.000 0.987 0.00 1.00
#> 180 2 0.000 0.987 0.00 1.00
#> 181 1 0.000 0.988 1.00 0.00
#> 182 1 0.000 0.988 1.00 0.00
#> 183 2 0.000 0.987 0.00 1.00
#> 184 2 0.000 0.987 0.00 1.00
#> 185 2 0.000 0.987 0.00 1.00
#> 186 1 0.000 0.988 1.00 0.00
#> 187 2 0.000 0.987 0.00 1.00
#> 188 2 0.000 0.987 0.00 1.00
#> 189 2 0.000 0.987 0.00 1.00
#> 190 1 0.000 0.988 1.00 0.00
#> 191 2 0.000 0.987 0.00 1.00
#> 192 2 0.000 0.987 0.00 1.00
#> 193 2 0.000 0.987 0.00 1.00
#> 194 2 0.529 0.859 0.12 0.88
#> 195 1 0.958 0.389 0.62 0.38
#> 196 1 0.000 0.988 1.00 0.00
#> 197 2 0.000 0.987 0.00 1.00
#> 198 2 0.000 0.987 0.00 1.00
#> 199 2 0.000 0.987 0.00 1.00
#> 200 2 0.000 0.987 0.00 1.00
#> 201 1 0.000 0.988 1.00 0.00
#> 202 2 0.000 0.987 0.00 1.00
#> 203 1 0.000 0.988 1.00 0.00
#> 204 1 0.000 0.988 1.00 0.00
#> 205 2 0.000 0.987 0.00 1.00
#> 206 2 0.000 0.987 0.00 1.00
#> 207 2 0.000 0.987 0.00 1.00
#> 208 1 0.000 0.988 1.00 0.00
#> 209 2 0.000 0.987 0.00 1.00
#> 210 2 0.000 0.987 0.00 1.00
#> 211 2 0.000 0.987 0.00 1.00
#> 212 2 0.000 0.987 0.00 1.00
#> 213 2 0.000 0.987 0.00 1.00
#> 214 2 0.000 0.987 0.00 1.00
#> 215 2 0.000 0.987 0.00 1.00
#> 216 2 0.000 0.987 0.00 1.00
#> 217 2 0.000 0.987 0.00 1.00
#> 218 1 0.000 0.988 1.00 0.00
#> 219 2 0.000 0.987 0.00 1.00
#> 220 2 0.000 0.987 0.00 1.00
#> 221 1 0.584 0.835 0.86 0.14
#> 222 2 0.000 0.987 0.00 1.00
#> 223 2 0.000 0.987 0.00 1.00
#> 224 2 0.795 0.685 0.24 0.76
#> 225 2 0.000 0.987 0.00 1.00
#> 226 2 0.000 0.987 0.00 1.00
#> 227 2 0.141 0.969 0.02 0.98
#> 228 2 0.000 0.987 0.00 1.00
#> 229 2 0.000 0.987 0.00 1.00
#> 230 2 0.000 0.987 0.00 1.00
#> 231 2 0.000 0.987 0.00 1.00
#> 232 2 0.000 0.987 0.00 1.00
#> 233 2 0.000 0.987 0.00 1.00
#> 234 2 0.000 0.987 0.00 1.00
#> 235 2 0.000 0.987 0.00 1.00
#> 236 1 0.000 0.988 1.00 0.00
#> 237 2 0.000 0.987 0.00 1.00
#> 238 2 0.000 0.987 0.00 1.00
#> 239 2 0.000 0.987 0.00 1.00
#> 240 1 0.000 0.988 1.00 0.00
#> 241 2 0.000 0.987 0.00 1.00
#> 242 2 0.000 0.987 0.00 1.00
#> 243 2 0.995 0.149 0.46 0.54
#> 244 1 0.000 0.988 1.00 0.00
#> 245 2 0.000 0.987 0.00 1.00
#> 246 2 0.000 0.987 0.00 1.00
#> 247 2 0.000 0.987 0.00 1.00
#> 248 2 0.000 0.987 0.00 1.00
#> 249 2 0.000 0.987 0.00 1.00
#> 250 2 0.242 0.949 0.04 0.96
#> 251 2 0.000 0.987 0.00 1.00
#> 252 1 0.000 0.988 1.00 0.00
#> 253 2 0.000 0.987 0.00 1.00
#> 254 2 0.000 0.987 0.00 1.00
#> 255 1 0.881 0.574 0.70 0.30
#> 256 2 0.000 0.987 0.00 1.00
#> 257 2 0.000 0.987 0.00 1.00
#> 258 2 0.000 0.987 0.00 1.00
#> 259 1 0.000 0.988 1.00 0.00
#> 260 2 0.000 0.987 0.00 1.00
#> 261 1 0.795 0.686 0.76 0.24
#> 262 2 0.000 0.987 0.00 1.00
#> 263 1 0.000 0.988 1.00 0.00
#> 264 2 0.000 0.987 0.00 1.00
#> 265 1 0.000 0.988 1.00 0.00
#> 266 2 0.000 0.987 0.00 1.00
#> 267 2 0.000 0.987 0.00 1.00
#> 268 1 0.000 0.988 1.00 0.00
#> 269 2 0.000 0.987 0.00 1.00
#> 270 2 0.000 0.987 0.00 1.00
#> 271 1 0.000 0.988 1.00 0.00
#> 272 2 0.000 0.987 0.00 1.00
#> 273 2 0.000 0.987 0.00 1.00
#> 274 1 0.000 0.988 1.00 0.00
#> 275 1 0.000 0.988 1.00 0.00
#> 276 1 0.000 0.988 1.00 0.00
#> 277 1 0.000 0.988 1.00 0.00
#> 278 1 0.000 0.988 1.00 0.00
#> 279 1 0.000 0.988 1.00 0.00
#> 280 1 0.000 0.988 1.00 0.00
#> 281 1 0.000 0.988 1.00 0.00
#> 282 1 0.000 0.988 1.00 0.00
#> 283 1 0.000 0.988 1.00 0.00
#> 284 1 0.000 0.988 1.00 0.00
#> 285 1 0.000 0.988 1.00 0.00
#> 286 1 0.000 0.988 1.00 0.00
#> 287 1 0.000 0.988 1.00 0.00
#> 288 1 0.000 0.988 1.00 0.00
#> 289 2 0.000 0.987 0.00 1.00
#> 290 1 0.000 0.988 1.00 0.00
#> 291 1 0.000 0.988 1.00 0.00
#> 292 2 0.000 0.987 0.00 1.00
#> 293 2 0.000 0.987 0.00 1.00
#> 294 1 0.000 0.988 1.00 0.00
#> 295 1 0.469 0.884 0.90 0.10
#> 296 2 0.000 0.987 0.00 1.00
#> 297 2 0.000 0.987 0.00 1.00
#> 298 2 0.000 0.987 0.00 1.00
#> 299 2 0.000 0.987 0.00 1.00
#> 300 1 0.000 0.988 1.00 0.00
#> 301 2 0.000 0.987 0.00 1.00
#> 302 2 0.000 0.987 0.00 1.00
#> 303 1 0.000 0.988 1.00 0.00
#> 304 1 0.000 0.988 1.00 0.00
#> 305 1 0.000 0.988 1.00 0.00
#> 306 1 0.000 0.988 1.00 0.00
#> 307 1 0.000 0.988 1.00 0.00
#> 308 1 0.000 0.988 1.00 0.00
#> 309 1 0.000 0.988 1.00 0.00
#> 310 1 0.000 0.988 1.00 0.00
#> 311 1 0.000 0.988 1.00 0.00
#> 312 1 0.000 0.988 1.00 0.00
#> 313 1 0.000 0.988 1.00 0.00
#> 314 1 0.000 0.988 1.00 0.00
#> 315 1 0.000 0.988 1.00 0.00
#> 316 1 0.000 0.988 1.00 0.00
#> 317 1 0.000 0.988 1.00 0.00
#> 318 1 0.000 0.988 1.00 0.00
#> 319 1 0.000 0.988 1.00 0.00
#> 320 1 0.000 0.988 1.00 0.00
#> 321 1 0.000 0.988 1.00 0.00
#> 322 1 0.000 0.988 1.00 0.00
#> 323 1 0.000 0.988 1.00 0.00
#> 324 1 0.000 0.988 1.00 0.00
#> 325 1 0.000 0.988 1.00 0.00
#> 326 1 0.000 0.988 1.00 0.00
#> 327 1 0.000 0.988 1.00 0.00
#> 328 1 0.000 0.988 1.00 0.00
#> 329 1 0.000 0.988 1.00 0.00
#> 330 1 0.000 0.988 1.00 0.00
#> 331 1 0.000 0.988 1.00 0.00
#> 332 1 0.000 0.988 1.00 0.00
#> 333 1 0.000 0.988 1.00 0.00
#> 334 1 0.000 0.988 1.00 0.00
#> 335 1 0.000 0.988 1.00 0.00
#> 336 1 0.000 0.988 1.00 0.00
#> 337 1 0.000 0.988 1.00 0.00
#> 338 1 0.000 0.988 1.00 0.00
#> 339 1 0.000 0.988 1.00 0.00
#> 340 1 0.000 0.988 1.00 0.00
#> 341 1 0.000 0.988 1.00 0.00
#> 342 1 0.000 0.988 1.00 0.00
#> 343 1 0.000 0.988 1.00 0.00
#> 344 1 0.000 0.988 1.00 0.00
#> 345 1 0.000 0.988 1.00 0.00
#> 346 1 0.000 0.988 1.00 0.00
#> 347 1 0.000 0.988 1.00 0.00
#> 348 2 0.000 0.987 0.00 1.00
#> 349 1 0.000 0.988 1.00 0.00
#> 350 1 0.000 0.988 1.00 0.00
#> 351 2 0.000 0.987 0.00 1.00
#> 352 1 0.000 0.988 1.00 0.00
#> 353 2 0.000 0.987 0.00 1.00
#> 354 1 0.000 0.988 1.00 0.00
#> 355 2 0.000 0.987 0.00 1.00
#> 356 2 0.000 0.987 0.00 1.00
#> 357 1 0.000 0.988 1.00 0.00
#> 358 2 0.000 0.987 0.00 1.00
#> 359 2 0.000 0.987 0.00 1.00
#> 360 2 0.000 0.987 0.00 1.00
#> 361 1 0.000 0.988 1.00 0.00
#> 362 1 0.000 0.988 1.00 0.00
#> 363 1 0.000 0.988 1.00 0.00
#> 364 1 0.000 0.988 1.00 0.00
#> 365 2 0.000 0.987 0.00 1.00
#> 366 1 0.000 0.988 1.00 0.00
#> 367 1 0.000 0.988 1.00 0.00
#> 368 1 0.000 0.988 1.00 0.00
#> 369 1 0.000 0.988 1.00 0.00
#> 370 1 0.000 0.988 1.00 0.00
#> 371 1 0.000 0.988 1.00 0.00
#> 372 1 0.000 0.988 1.00 0.00
#> 373 2 0.000 0.987 0.00 1.00
#> 374 1 0.000 0.988 1.00 0.00
#> 375 2 0.000 0.987 0.00 1.00
#> 376 2 0.000 0.987 0.00 1.00
#> 377 2 0.000 0.987 0.00 1.00
#> 378 1 0.141 0.969 0.98 0.02
#> 379 2 0.000 0.987 0.00 1.00
#> 380 2 0.000 0.987 0.00 1.00
#> 381 1 0.000 0.988 1.00 0.00
#> 382 1 0.000 0.988 1.00 0.00
#> 383 2 0.000 0.987 0.00 1.00
#> 384 2 0.000 0.987 0.00 1.00
#> 385 2 0.000 0.987 0.00 1.00
#> 386 2 0.000 0.987 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 2 0.0000 0.992 0.00 1.00 0.00
#> 2 2 0.0000 0.992 0.00 1.00 0.00
#> 3 1 0.1529 0.935 0.96 0.00 0.04
#> 4 2 0.0000 0.992 0.00 1.00 0.00
#> 5 1 0.0000 0.969 1.00 0.00 0.00
#> 6 1 0.4209 0.815 0.86 0.12 0.02
#> 7 2 0.0000 0.992 0.00 1.00 0.00
#> 8 1 0.0892 0.953 0.98 0.00 0.02
#> 9 1 0.0000 0.969 1.00 0.00 0.00
#> 10 1 0.1529 0.935 0.96 0.00 0.04
#> 11 2 0.0000 0.992 0.00 1.00 0.00
#> 12 2 0.0000 0.992 0.00 1.00 0.00
#> 13 2 0.0000 0.992 0.00 1.00 0.00
#> 14 1 0.8321 0.506 0.62 0.24 0.14
#> 15 2 0.0000 0.992 0.00 1.00 0.00
#> 16 1 0.4002 0.802 0.84 0.00 0.16
#> 17 1 0.4291 0.778 0.82 0.00 0.18
#> 18 1 0.5948 0.451 0.64 0.00 0.36
#> 19 2 0.0000 0.992 0.00 1.00 0.00
#> 20 2 0.0000 0.992 0.00 1.00 0.00
#> 21 2 0.0000 0.992 0.00 1.00 0.00
#> 22 1 0.5706 0.541 0.68 0.00 0.32
#> 23 2 0.0000 0.992 0.00 1.00 0.00
#> 24 2 0.0000 0.992 0.00 1.00 0.00
#> 25 1 0.5560 0.579 0.70 0.00 0.30
#> 26 2 0.0000 0.992 0.00 1.00 0.00
#> 27 2 0.0000 0.992 0.00 1.00 0.00
#> 28 2 0.0000 0.992 0.00 1.00 0.00
#> 29 2 0.3340 0.861 0.00 0.88 0.12
#> 30 2 0.0000 0.992 0.00 1.00 0.00
#> 31 1 0.0892 0.953 0.98 0.00 0.02
#> 32 2 0.0000 0.992 0.00 1.00 0.00
#> 33 2 0.5397 0.611 0.00 0.72 0.28
#> 34 3 0.2959 0.866 0.10 0.00 0.90
#> 35 3 0.0000 0.945 0.00 0.00 1.00
#> 36 3 0.0000 0.945 0.00 0.00 1.00
#> 37 2 0.0000 0.992 0.00 1.00 0.00
#> 38 2 0.0000 0.992 0.00 1.00 0.00
#> 39 3 0.0000 0.945 0.00 0.00 1.00
#> 40 2 0.0000 0.992 0.00 1.00 0.00
#> 41 3 0.0000 0.945 0.00 0.00 1.00
#> 42 2 0.0000 0.992 0.00 1.00 0.00
#> 43 2 0.0000 0.992 0.00 1.00 0.00
#> 44 2 0.0000 0.992 0.00 1.00 0.00
#> 45 1 0.0000 0.969 1.00 0.00 0.00
#> 46 1 0.0892 0.953 0.98 0.00 0.02
#> 47 1 0.0000 0.969 1.00 0.00 0.00
#> 48 2 0.0000 0.992 0.00 1.00 0.00
#> 49 1 0.0000 0.969 1.00 0.00 0.00
#> 50 2 0.0000 0.992 0.00 1.00 0.00
#> 51 3 0.0000 0.945 0.00 0.00 1.00
#> 52 1 0.0000 0.969 1.00 0.00 0.00
#> 53 1 0.0000 0.969 1.00 0.00 0.00
#> 54 3 0.0000 0.945 0.00 0.00 1.00
#> 55 3 0.0892 0.932 0.02 0.00 0.98
#> 56 2 0.0000 0.992 0.00 1.00 0.00
#> 57 2 0.0000 0.992 0.00 1.00 0.00
#> 58 3 0.5706 0.534 0.00 0.32 0.68
#> 59 2 0.2959 0.886 0.00 0.90 0.10
#> 60 3 0.3340 0.828 0.00 0.12 0.88
#> 61 2 0.0000 0.992 0.00 1.00 0.00
#> 62 2 0.0000 0.992 0.00 1.00 0.00
#> 63 3 0.0000 0.945 0.00 0.00 1.00
#> 64 3 0.0000 0.945 0.00 0.00 1.00
#> 65 2 0.0000 0.992 0.00 1.00 0.00
#> 66 1 0.4002 0.802 0.84 0.00 0.16
#> 67 3 0.6500 0.737 0.14 0.10 0.76
#> 68 2 0.0000 0.992 0.00 1.00 0.00
#> 69 2 0.0000 0.992 0.00 1.00 0.00
#> 70 2 0.3340 0.861 0.12 0.88 0.00
#> 71 3 0.0000 0.945 0.00 0.00 1.00
#> 72 2 0.0000 0.992 0.00 1.00 0.00
#> 73 3 0.0000 0.945 0.00 0.00 1.00
#> 74 2 0.4796 0.716 0.00 0.78 0.22
#> 75 3 0.0000 0.945 0.00 0.00 1.00
#> 76 2 0.0000 0.992 0.00 1.00 0.00
#> 77 2 0.0000 0.992 0.00 1.00 0.00
#> 78 3 0.0000 0.945 0.00 0.00 1.00
#> 79 1 0.2959 0.874 0.90 0.00 0.10
#> 80 3 0.2066 0.902 0.06 0.00 0.94
#> 81 3 0.0000 0.945 0.00 0.00 1.00
#> 82 2 0.0000 0.992 0.00 1.00 0.00
#> 83 2 0.0000 0.992 0.00 1.00 0.00
#> 84 2 0.0000 0.992 0.00 1.00 0.00
#> 85 3 0.0000 0.945 0.00 0.00 1.00
#> 86 2 0.0000 0.992 0.00 1.00 0.00
#> 87 2 0.0000 0.992 0.00 1.00 0.00
#> 88 2 0.0000 0.992 0.00 1.00 0.00
#> 89 2 0.0000 0.992 0.00 1.00 0.00
#> 90 1 0.5835 0.494 0.66 0.00 0.34
#> 91 3 0.0000 0.945 0.00 0.00 1.00
#> 92 3 0.0000 0.945 0.00 0.00 1.00
#> 93 3 0.0000 0.945 0.00 0.00 1.00
#> 94 2 0.0000 0.992 0.00 1.00 0.00
#> 95 2 0.0000 0.992 0.00 1.00 0.00
#> 96 2 0.0000 0.992 0.00 1.00 0.00
#> 97 1 0.0000 0.969 1.00 0.00 0.00
#> 98 3 0.0000 0.945 0.00 0.00 1.00
#> 99 3 0.0000 0.945 0.00 0.00 1.00
#> 100 2 0.0000 0.992 0.00 1.00 0.00
#> 101 2 0.0000 0.992 0.00 1.00 0.00
#> 102 3 0.0000 0.945 0.00 0.00 1.00
#> 103 2 0.0000 0.992 0.00 1.00 0.00
#> 104 3 0.4002 0.782 0.00 0.16 0.84
#> 105 2 0.0000 0.992 0.00 1.00 0.00
#> 106 2 0.0000 0.992 0.00 1.00 0.00
#> 107 3 0.0000 0.945 0.00 0.00 1.00
#> 108 3 0.0000 0.945 0.00 0.00 1.00
#> 109 1 0.0000 0.969 1.00 0.00 0.00
#> 110 3 0.0000 0.945 0.00 0.00 1.00
#> 111 3 0.0000 0.945 0.00 0.00 1.00
#> 112 3 0.5835 0.497 0.34 0.00 0.66
#> 113 3 0.0000 0.945 0.00 0.00 1.00
#> 114 2 0.0000 0.992 0.00 1.00 0.00
#> 115 2 0.0000 0.992 0.00 1.00 0.00
#> 116 2 0.0000 0.992 0.00 1.00 0.00
#> 117 2 0.0000 0.992 0.00 1.00 0.00
#> 118 2 0.0000 0.992 0.00 1.00 0.00
#> 119 2 0.0000 0.992 0.00 1.00 0.00
#> 120 2 0.0000 0.992 0.00 1.00 0.00
#> 121 2 0.0000 0.992 0.00 1.00 0.00
#> 122 2 0.0000 0.992 0.00 1.00 0.00
#> 123 2 0.0000 0.992 0.00 1.00 0.00
#> 124 2 0.0000 0.992 0.00 1.00 0.00
#> 125 2 0.0000 0.992 0.00 1.00 0.00
#> 126 2 0.0000 0.992 0.00 1.00 0.00
#> 127 3 0.0000 0.945 0.00 0.00 1.00
#> 128 3 0.0000 0.945 0.00 0.00 1.00
#> 129 2 0.0000 0.992 0.00 1.00 0.00
#> 130 3 0.0000 0.945 0.00 0.00 1.00
#> 131 3 0.3686 0.826 0.14 0.00 0.86
#> 132 3 0.4002 0.802 0.16 0.00 0.84
#> 133 3 0.0000 0.945 0.00 0.00 1.00
#> 134 3 0.0000 0.945 0.00 0.00 1.00
#> 135 3 0.0000 0.945 0.00 0.00 1.00
#> 136 2 0.0000 0.992 0.00 1.00 0.00
#> 137 2 0.0000 0.992 0.00 1.00 0.00
#> 138 2 0.0000 0.992 0.00 1.00 0.00
#> 139 3 0.6302 0.083 0.00 0.48 0.52
#> 140 3 0.0000 0.945 0.00 0.00 1.00
#> 141 3 0.0892 0.932 0.02 0.00 0.98
#> 142 3 0.0000 0.945 0.00 0.00 1.00
#> 143 2 0.0000 0.992 0.00 1.00 0.00
#> 144 3 0.0000 0.945 0.00 0.00 1.00
#> 145 3 0.3686 0.826 0.14 0.00 0.86
#> 146 3 0.0000 0.945 0.00 0.00 1.00
#> 147 2 0.0000 0.992 0.00 1.00 0.00
#> 148 3 0.0000 0.945 0.00 0.00 1.00
#> 149 2 0.0000 0.992 0.00 1.00 0.00
#> 150 2 0.0000 0.992 0.00 1.00 0.00
#> 151 2 0.0000 0.992 0.00 1.00 0.00
#> 152 2 0.0000 0.992 0.00 1.00 0.00
#> 153 2 0.0000 0.992 0.00 1.00 0.00
#> 154 3 0.0000 0.945 0.00 0.00 1.00
#> 155 2 0.0000 0.992 0.00 1.00 0.00
#> 156 2 0.0000 0.992 0.00 1.00 0.00
#> 157 3 0.0000 0.945 0.00 0.00 1.00
#> 158 2 0.0000 0.992 0.00 1.00 0.00
#> 159 3 0.0000 0.945 0.00 0.00 1.00
#> 160 2 0.0000 0.992 0.00 1.00 0.00
#> 161 2 0.0000 0.992 0.00 1.00 0.00
#> 162 2 0.0000 0.992 0.00 1.00 0.00
#> 163 1 0.0000 0.969 1.00 0.00 0.00
#> 164 2 0.0892 0.972 0.00 0.98 0.02
#> 165 3 0.0000 0.945 0.00 0.00 1.00
#> 166 2 0.0000 0.992 0.00 1.00 0.00
#> 167 1 0.2066 0.917 0.94 0.00 0.06
#> 168 3 0.0000 0.945 0.00 0.00 1.00
#> 169 3 0.2959 0.852 0.00 0.10 0.90
#> 170 2 0.0000 0.992 0.00 1.00 0.00
#> 171 3 0.0000 0.945 0.00 0.00 1.00
#> 172 2 0.0000 0.992 0.00 1.00 0.00
#> 173 2 0.0000 0.992 0.00 1.00 0.00
#> 174 3 0.3340 0.847 0.12 0.00 0.88
#> 175 3 0.0000 0.945 0.00 0.00 1.00
#> 176 2 0.0000 0.992 0.00 1.00 0.00
#> 177 2 0.0000 0.992 0.00 1.00 0.00
#> 178 2 0.0000 0.992 0.00 1.00 0.00
#> 179 2 0.4796 0.719 0.00 0.78 0.22
#> 180 2 0.0000 0.992 0.00 1.00 0.00
#> 181 3 0.1529 0.918 0.04 0.00 0.96
#> 182 3 0.0000 0.945 0.00 0.00 1.00
#> 183 3 0.4002 0.780 0.00 0.16 0.84
#> 184 2 0.0000 0.992 0.00 1.00 0.00
#> 185 2 0.0000 0.992 0.00 1.00 0.00
#> 186 3 0.0000 0.945 0.00 0.00 1.00
#> 187 2 0.0000 0.992 0.00 1.00 0.00
#> 188 2 0.0000 0.992 0.00 1.00 0.00
#> 189 2 0.0000 0.992 0.00 1.00 0.00
#> 190 3 0.6280 0.189 0.46 0.00 0.54
#> 191 2 0.0000 0.992 0.00 1.00 0.00
#> 192 2 0.0000 0.992 0.00 1.00 0.00
#> 193 2 0.0000 0.992 0.00 1.00 0.00
#> 194 3 0.0000 0.945 0.00 0.00 1.00
#> 195 3 0.0000 0.945 0.00 0.00 1.00
#> 196 3 0.0000 0.945 0.00 0.00 1.00
#> 197 2 0.0000 0.992 0.00 1.00 0.00
#> 198 2 0.0000 0.992 0.00 1.00 0.00
#> 199 2 0.0000 0.992 0.00 1.00 0.00
#> 200 2 0.0000 0.992 0.00 1.00 0.00
#> 201 3 0.0000 0.945 0.00 0.00 1.00
#> 202 2 0.0000 0.992 0.00 1.00 0.00
#> 203 3 0.0000 0.945 0.00 0.00 1.00
#> 204 1 0.0000 0.969 1.00 0.00 0.00
#> 205 2 0.0000 0.992 0.00 1.00 0.00
#> 206 2 0.0000 0.992 0.00 1.00 0.00
#> 207 2 0.0000 0.992 0.00 1.00 0.00
#> 208 1 0.0000 0.969 1.00 0.00 0.00
#> 209 2 0.0000 0.992 0.00 1.00 0.00
#> 210 2 0.5397 0.610 0.00 0.72 0.28
#> 211 2 0.0000 0.992 0.00 1.00 0.00
#> 212 2 0.0000 0.992 0.00 1.00 0.00
#> 213 2 0.0000 0.992 0.00 1.00 0.00
#> 214 2 0.0000 0.992 0.00 1.00 0.00
#> 215 2 0.0000 0.992 0.00 1.00 0.00
#> 216 2 0.0000 0.992 0.00 1.00 0.00
#> 217 2 0.0000 0.992 0.00 1.00 0.00
#> 218 3 0.3686 0.826 0.14 0.00 0.86
#> 219 2 0.0000 0.992 0.00 1.00 0.00
#> 220 2 0.0000 0.992 0.00 1.00 0.00
#> 221 3 0.0000 0.945 0.00 0.00 1.00
#> 222 2 0.0000 0.992 0.00 1.00 0.00
#> 223 2 0.0000 0.992 0.00 1.00 0.00
#> 224 3 0.0000 0.945 0.00 0.00 1.00
#> 225 2 0.0000 0.992 0.00 1.00 0.00
#> 226 2 0.0000 0.992 0.00 1.00 0.00
#> 227 2 0.3042 0.917 0.04 0.92 0.04
#> 228 2 0.0000 0.992 0.00 1.00 0.00
#> 229 2 0.0000 0.992 0.00 1.00 0.00
#> 230 2 0.0000 0.992 0.00 1.00 0.00
#> 231 2 0.0000 0.992 0.00 1.00 0.00
#> 232 2 0.0000 0.992 0.00 1.00 0.00
#> 233 3 0.0892 0.928 0.00 0.02 0.98
#> 234 2 0.0000 0.992 0.00 1.00 0.00
#> 235 2 0.0000 0.992 0.00 1.00 0.00
#> 236 3 0.0000 0.945 0.00 0.00 1.00
#> 237 2 0.0000 0.992 0.00 1.00 0.00
#> 238 2 0.0000 0.992 0.00 1.00 0.00
#> 239 2 0.0000 0.992 0.00 1.00 0.00
#> 240 1 0.0000 0.969 1.00 0.00 0.00
#> 241 2 0.0000 0.992 0.00 1.00 0.00
#> 242 2 0.0000 0.992 0.00 1.00 0.00
#> 243 3 0.0000 0.945 0.00 0.00 1.00
#> 244 3 0.0892 0.932 0.02 0.00 0.98
#> 245 2 0.0000 0.992 0.00 1.00 0.00
#> 246 2 0.0892 0.973 0.00 0.98 0.02
#> 247 2 0.0000 0.992 0.00 1.00 0.00
#> 248 2 0.0000 0.992 0.00 1.00 0.00
#> 249 2 0.0000 0.992 0.00 1.00 0.00
#> 250 3 0.0000 0.945 0.00 0.00 1.00
#> 251 2 0.0000 0.992 0.00 1.00 0.00
#> 252 3 0.0000 0.945 0.00 0.00 1.00
#> 253 2 0.0000 0.992 0.00 1.00 0.00
#> 254 2 0.0000 0.992 0.00 1.00 0.00
#> 255 3 0.0000 0.945 0.00 0.00 1.00
#> 256 2 0.0000 0.992 0.00 1.00 0.00
#> 257 2 0.0000 0.992 0.00 1.00 0.00
#> 258 2 0.0000 0.992 0.00 1.00 0.00
#> 259 3 0.0000 0.945 0.00 0.00 1.00
#> 260 2 0.0000 0.992 0.00 1.00 0.00
#> 261 3 0.0000 0.945 0.00 0.00 1.00
#> 262 2 0.0000 0.992 0.00 1.00 0.00
#> 263 3 0.5706 0.525 0.32 0.00 0.68
#> 264 3 0.3340 0.829 0.00 0.12 0.88
#> 265 3 0.0000 0.945 0.00 0.00 1.00
#> 266 2 0.0000 0.992 0.00 1.00 0.00
#> 267 2 0.0000 0.992 0.00 1.00 0.00
#> 268 3 0.0000 0.945 0.00 0.00 1.00
#> 269 2 0.0000 0.992 0.00 1.00 0.00
#> 270 2 0.0000 0.992 0.00 1.00 0.00
#> 271 3 0.0000 0.945 0.00 0.00 1.00
#> 272 2 0.0000 0.992 0.00 1.00 0.00
#> 273 2 0.0000 0.992 0.00 1.00 0.00
#> 274 1 0.6126 0.297 0.60 0.00 0.40
#> 275 1 0.0000 0.969 1.00 0.00 0.00
#> 276 1 0.0000 0.969 1.00 0.00 0.00
#> 277 1 0.0000 0.969 1.00 0.00 0.00
#> 278 1 0.0000 0.969 1.00 0.00 0.00
#> 279 1 0.0000 0.969 1.00 0.00 0.00
#> 280 1 0.0000 0.969 1.00 0.00 0.00
#> 281 1 0.0000 0.969 1.00 0.00 0.00
#> 282 1 0.0000 0.969 1.00 0.00 0.00
#> 283 1 0.0000 0.969 1.00 0.00 0.00
#> 284 1 0.0000 0.969 1.00 0.00 0.00
#> 285 3 0.6280 0.179 0.46 0.00 0.54
#> 286 3 0.0000 0.945 0.00 0.00 1.00
#> 287 3 0.0000 0.945 0.00 0.00 1.00
#> 288 3 0.0000 0.945 0.00 0.00 1.00
#> 289 3 0.0000 0.945 0.00 0.00 1.00
#> 290 3 0.4796 0.720 0.22 0.00 0.78
#> 291 3 0.4555 0.749 0.20 0.00 0.80
#> 292 2 0.0000 0.992 0.00 1.00 0.00
#> 293 2 0.0000 0.992 0.00 1.00 0.00
#> 294 1 0.1529 0.934 0.96 0.00 0.04
#> 295 3 0.0000 0.945 0.00 0.00 1.00
#> 296 2 0.0000 0.992 0.00 1.00 0.00
#> 297 2 0.0000 0.992 0.00 1.00 0.00
#> 298 2 0.0000 0.992 0.00 1.00 0.00
#> 299 2 0.0000 0.992 0.00 1.00 0.00
#> 300 3 0.0000 0.945 0.00 0.00 1.00
#> 301 2 0.0000 0.992 0.00 1.00 0.00
#> 302 2 0.0000 0.992 0.00 1.00 0.00
#> 303 1 0.0000 0.969 1.00 0.00 0.00
#> 304 1 0.0000 0.969 1.00 0.00 0.00
#> 305 1 0.0000 0.969 1.00 0.00 0.00
#> 306 1 0.0000 0.969 1.00 0.00 0.00
#> 307 1 0.0000 0.969 1.00 0.00 0.00
#> 308 1 0.0000 0.969 1.00 0.00 0.00
#> 309 1 0.0000 0.969 1.00 0.00 0.00
#> 310 1 0.0000 0.969 1.00 0.00 0.00
#> 311 1 0.0000 0.969 1.00 0.00 0.00
#> 312 1 0.0000 0.969 1.00 0.00 0.00
#> 313 1 0.0000 0.969 1.00 0.00 0.00
#> 314 1 0.0000 0.969 1.00 0.00 0.00
#> 315 1 0.0000 0.969 1.00 0.00 0.00
#> 316 1 0.0000 0.969 1.00 0.00 0.00
#> 317 1 0.0000 0.969 1.00 0.00 0.00
#> 318 1 0.0000 0.969 1.00 0.00 0.00
#> 319 1 0.0000 0.969 1.00 0.00 0.00
#> 320 1 0.0000 0.969 1.00 0.00 0.00
#> 321 1 0.0000 0.969 1.00 0.00 0.00
#> 322 1 0.0000 0.969 1.00 0.00 0.00
#> 323 1 0.0000 0.969 1.00 0.00 0.00
#> 324 1 0.0000 0.969 1.00 0.00 0.00
#> 325 1 0.0000 0.969 1.00 0.00 0.00
#> 326 1 0.0000 0.969 1.00 0.00 0.00
#> 327 1 0.0000 0.969 1.00 0.00 0.00
#> 328 1 0.0000 0.969 1.00 0.00 0.00
#> 329 1 0.0000 0.969 1.00 0.00 0.00
#> 330 1 0.0000 0.969 1.00 0.00 0.00
#> 331 1 0.0000 0.969 1.00 0.00 0.00
#> 332 1 0.0000 0.969 1.00 0.00 0.00
#> 333 1 0.0000 0.969 1.00 0.00 0.00
#> 334 1 0.0000 0.969 1.00 0.00 0.00
#> 335 1 0.0000 0.969 1.00 0.00 0.00
#> 336 1 0.0000 0.969 1.00 0.00 0.00
#> 337 1 0.0000 0.969 1.00 0.00 0.00
#> 338 1 0.0000 0.969 1.00 0.00 0.00
#> 339 1 0.0000 0.969 1.00 0.00 0.00
#> 340 1 0.0000 0.969 1.00 0.00 0.00
#> 341 1 0.0000 0.969 1.00 0.00 0.00
#> 342 1 0.0000 0.969 1.00 0.00 0.00
#> 343 1 0.0000 0.969 1.00 0.00 0.00
#> 344 1 0.0000 0.969 1.00 0.00 0.00
#> 345 1 0.0000 0.969 1.00 0.00 0.00
#> 346 1 0.0000 0.969 1.00 0.00 0.00
#> 347 1 0.0000 0.969 1.00 0.00 0.00
#> 348 2 0.0000 0.992 0.00 1.00 0.00
#> 349 1 0.0000 0.969 1.00 0.00 0.00
#> 350 1 0.0000 0.969 1.00 0.00 0.00
#> 351 2 0.0000 0.992 0.00 1.00 0.00
#> 352 1 0.0000 0.969 1.00 0.00 0.00
#> 353 2 0.0000 0.992 0.00 1.00 0.00
#> 354 1 0.0000 0.969 1.00 0.00 0.00
#> 355 2 0.0000 0.992 0.00 1.00 0.00
#> 356 2 0.0892 0.972 0.02 0.98 0.00
#> 357 1 0.0000 0.969 1.00 0.00 0.00
#> 358 2 0.0000 0.992 0.00 1.00 0.00
#> 359 2 0.0000 0.992 0.00 1.00 0.00
#> 360 2 0.0000 0.992 0.00 1.00 0.00
#> 361 1 0.0000 0.969 1.00 0.00 0.00
#> 362 1 0.0000 0.969 1.00 0.00 0.00
#> 363 1 0.0000 0.969 1.00 0.00 0.00
#> 364 1 0.0000 0.969 1.00 0.00 0.00
#> 365 2 0.0000 0.992 0.00 1.00 0.00
#> 366 1 0.0000 0.969 1.00 0.00 0.00
#> 367 1 0.0000 0.969 1.00 0.00 0.00
#> 368 1 0.0000 0.969 1.00 0.00 0.00
#> 369 1 0.0000 0.969 1.00 0.00 0.00
#> 370 1 0.0000 0.969 1.00 0.00 0.00
#> 371 1 0.0000 0.969 1.00 0.00 0.00
#> 372 1 0.0000 0.969 1.00 0.00 0.00
#> 373 2 0.0000 0.992 0.00 1.00 0.00
#> 374 3 0.1529 0.918 0.04 0.00 0.96
#> 375 2 0.0000 0.992 0.00 1.00 0.00
#> 376 2 0.0000 0.992 0.00 1.00 0.00
#> 377 2 0.0000 0.992 0.00 1.00 0.00
#> 378 1 0.0000 0.969 1.00 0.00 0.00
#> 379 2 0.0000 0.992 0.00 1.00 0.00
#> 380 2 0.0000 0.992 0.00 1.00 0.00
#> 381 1 0.0000 0.969 1.00 0.00 0.00
#> 382 1 0.0000 0.969 1.00 0.00 0.00
#> 383 2 0.0000 0.992 0.00 1.00 0.00
#> 384 2 0.0000 0.992 0.00 1.00 0.00
#> 385 2 0.0000 0.992 0.00 1.00 0.00
#> 386 2 0.0000 0.992 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 2 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 3 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 4 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 5 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 6 4 0.4753 0.7205 0.18 0.02 0.02 0.78
#> 7 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 8 4 0.4284 0.6975 0.20 0.00 0.02 0.78
#> 9 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 10 4 0.4284 0.6975 0.20 0.00 0.02 0.78
#> 11 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 12 4 0.4790 0.6069 0.00 0.38 0.00 0.62
#> 13 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 14 4 0.5006 0.7408 0.16 0.04 0.02 0.78
#> 15 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 16 4 0.5271 0.4839 0.34 0.00 0.02 0.64
#> 17 4 0.4284 0.6975 0.20 0.00 0.02 0.78
#> 18 4 0.4284 0.6975 0.20 0.00 0.02 0.78
#> 19 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 20 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 21 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 22 4 0.4284 0.6975 0.20 0.00 0.02 0.78
#> 23 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 24 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 25 4 0.4284 0.6975 0.20 0.00 0.02 0.78
#> 26 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 27 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 28 2 0.1637 0.9061 0.00 0.94 0.00 0.06
#> 29 4 0.4332 0.8049 0.00 0.16 0.04 0.80
#> 30 2 0.2647 0.8280 0.00 0.88 0.00 0.12
#> 31 1 0.1211 0.8971 0.96 0.00 0.04 0.00
#> 32 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 33 4 0.5677 0.7390 0.00 0.14 0.14 0.72
#> 34 3 0.4134 0.6838 0.26 0.00 0.74 0.00
#> 35 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 36 3 0.1637 0.8439 0.06 0.00 0.94 0.00
#> 37 2 0.4522 0.3937 0.00 0.68 0.00 0.32
#> 38 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 39 3 0.2345 0.8245 0.10 0.00 0.90 0.00
#> 40 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 41 3 0.1211 0.8517 0.04 0.00 0.96 0.00
#> 42 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 43 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 44 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 45 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 46 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 47 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 48 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 49 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 50 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 51 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 52 1 0.5606 -0.0683 0.50 0.00 0.02 0.48
#> 53 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 54 3 0.3400 0.7631 0.18 0.00 0.82 0.00
#> 55 3 0.3610 0.7477 0.20 0.00 0.80 0.00
#> 56 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 57 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 58 3 0.5173 0.3652 0.00 0.32 0.66 0.02
#> 59 2 0.3335 0.8029 0.00 0.86 0.12 0.02
#> 60 3 0.5902 0.5437 0.00 0.14 0.70 0.16
#> 61 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 62 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 63 3 0.1211 0.8445 0.00 0.00 0.96 0.04
#> 64 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 65 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 66 1 0.1637 0.8776 0.94 0.00 0.06 0.00
#> 67 3 0.7828 0.0815 0.06 0.08 0.50 0.36
#> 68 2 0.0707 0.9483 0.00 0.98 0.00 0.02
#> 69 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 70 2 0.6286 0.4879 0.14 0.66 0.00 0.20
#> 71 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 72 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 73 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 74 4 0.4894 0.7437 0.00 0.10 0.12 0.78
#> 75 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 76 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 77 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 78 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 79 1 0.1913 0.8859 0.94 0.00 0.02 0.04
#> 80 3 0.3975 0.7038 0.24 0.00 0.76 0.00
#> 81 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 82 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 83 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 84 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 85 3 0.2647 0.8116 0.12 0.00 0.88 0.00
#> 86 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 87 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 88 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 89 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 90 1 0.6649 0.2270 0.56 0.00 0.34 0.10
#> 91 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 92 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 93 3 0.3400 0.7631 0.18 0.00 0.82 0.00
#> 94 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 95 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 96 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 97 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 98 3 0.2345 0.8245 0.10 0.00 0.90 0.00
#> 99 3 0.2647 0.8116 0.12 0.00 0.88 0.00
#> 100 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 101 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 102 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 103 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 104 3 0.4994 0.1356 0.00 0.00 0.52 0.48
#> 105 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 106 2 0.1211 0.9265 0.00 0.96 0.04 0.00
#> 107 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 108 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 109 1 0.0707 0.9133 0.98 0.00 0.02 0.00
#> 110 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 111 3 0.0707 0.8572 0.02 0.00 0.98 0.00
#> 112 3 0.4855 0.4596 0.40 0.00 0.60 0.00
#> 113 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 114 2 0.0707 0.9485 0.00 0.98 0.00 0.02
#> 115 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 116 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 117 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 118 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 119 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 120 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 121 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 122 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 123 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 124 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 125 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 126 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 127 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 128 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 129 2 0.0707 0.9487 0.00 0.98 0.00 0.02
#> 130 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 131 3 0.4624 0.5845 0.34 0.00 0.66 0.00
#> 132 3 0.4406 0.6365 0.30 0.00 0.70 0.00
#> 133 3 0.2011 0.8351 0.08 0.00 0.92 0.00
#> 134 3 0.2647 0.8116 0.12 0.00 0.88 0.00
#> 135 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 136 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 137 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 138 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 139 3 0.5606 0.0176 0.00 0.48 0.50 0.02
#> 140 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 141 3 0.3400 0.7649 0.18 0.00 0.82 0.00
#> 142 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 143 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 144 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 145 3 0.4713 0.5467 0.36 0.00 0.64 0.00
#> 146 3 0.1211 0.8515 0.04 0.00 0.96 0.00
#> 147 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 148 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 149 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 150 2 0.1411 0.9296 0.00 0.96 0.02 0.02
#> 151 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 152 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 153 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 154 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 155 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 156 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 157 3 0.1637 0.8439 0.06 0.00 0.94 0.00
#> 158 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 159 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 160 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 161 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 162 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 163 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 164 2 0.2706 0.8581 0.00 0.90 0.08 0.02
#> 165 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 166 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 167 1 0.3400 0.7228 0.82 0.00 0.18 0.00
#> 168 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 169 3 0.2706 0.7873 0.00 0.08 0.90 0.02
#> 170 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 171 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 172 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 173 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 174 3 0.4277 0.6598 0.28 0.00 0.72 0.00
#> 175 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 176 2 0.0707 0.9487 0.00 0.98 0.00 0.02
#> 177 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 178 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 179 2 0.4642 0.6008 0.00 0.74 0.24 0.02
#> 180 2 0.0707 0.9487 0.00 0.98 0.00 0.02
#> 181 3 0.3172 0.7825 0.16 0.00 0.84 0.00
#> 182 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 183 3 0.3606 0.6983 0.00 0.14 0.84 0.02
#> 184 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 185 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 186 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 187 2 0.0707 0.9487 0.00 0.98 0.00 0.02
#> 188 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 189 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 190 3 0.6649 0.3922 0.34 0.00 0.56 0.10
#> 191 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 192 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 193 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 194 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 195 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 196 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 197 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 198 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 199 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 200 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 201 3 0.2345 0.8245 0.10 0.00 0.90 0.00
#> 202 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 203 3 0.3172 0.7811 0.16 0.00 0.84 0.00
#> 204 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 205 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 206 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 207 2 0.1637 0.9060 0.00 0.94 0.00 0.06
#> 208 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 209 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 210 2 0.5883 0.4054 0.00 0.64 0.30 0.06
#> 211 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 212 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 213 2 0.0707 0.9498 0.00 0.98 0.00 0.02
#> 214 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 215 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 216 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 217 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 218 3 0.4406 0.6367 0.30 0.00 0.70 0.00
#> 219 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 220 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 221 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 222 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 223 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 224 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 225 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 226 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 227 4 0.4284 0.8309 0.02 0.20 0.00 0.78
#> 228 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 229 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 230 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 231 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 232 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 233 3 0.4790 0.3692 0.00 0.00 0.62 0.38
#> 234 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 235 4 0.5570 0.4520 0.00 0.44 0.02 0.54
#> 236 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 237 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 238 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 239 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 240 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 241 4 0.3801 0.8366 0.00 0.22 0.00 0.78
#> 242 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 243 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 244 3 0.2345 0.8255 0.10 0.00 0.90 0.00
#> 245 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 246 4 0.4134 0.8023 0.00 0.26 0.00 0.74
#> 247 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 248 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 249 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 250 3 0.1411 0.8467 0.00 0.02 0.96 0.02
#> 251 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 252 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 253 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 254 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 255 3 0.3172 0.7416 0.00 0.00 0.84 0.16
#> 256 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 257 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 258 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 259 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 260 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 261 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 262 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 263 4 0.4284 0.6975 0.20 0.00 0.02 0.78
#> 264 3 0.4553 0.6129 0.00 0.18 0.78 0.04
#> 265 3 0.2647 0.8116 0.12 0.00 0.88 0.00
#> 266 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 267 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 268 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 269 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 270 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 271 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 272 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 273 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 274 1 0.4713 0.3244 0.64 0.00 0.36 0.00
#> 275 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 276 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 277 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 278 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 279 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 280 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 281 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 282 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 283 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 284 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 285 3 0.5957 0.2993 0.42 0.00 0.54 0.04
#> 286 3 0.2011 0.8347 0.08 0.00 0.92 0.00
#> 287 3 0.3801 0.6671 0.00 0.00 0.78 0.22
#> 288 3 0.0000 0.8610 0.00 0.00 1.00 0.00
#> 289 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 290 3 0.4624 0.5849 0.34 0.00 0.66 0.00
#> 291 3 0.4522 0.6114 0.32 0.00 0.68 0.00
#> 292 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 293 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 294 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 295 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 296 2 0.3400 0.7717 0.00 0.82 0.00 0.18
#> 297 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 298 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 299 2 0.3400 0.7717 0.00 0.82 0.00 0.18
#> 300 3 0.0707 0.8598 0.00 0.00 0.98 0.02
#> 301 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 302 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 303 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 304 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 305 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 306 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 307 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 308 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 309 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 310 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 311 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 312 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 313 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 314 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 315 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 316 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 317 1 0.2011 0.8934 0.92 0.00 0.00 0.08
#> 318 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 319 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 320 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 321 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 322 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 323 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 324 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 325 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 326 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 327 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 328 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 329 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 330 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 331 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 332 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 333 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 334 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 335 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 336 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 337 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 338 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 339 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 340 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 341 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 342 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 343 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 344 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 345 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 346 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 347 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 348 2 0.0707 0.9500 0.00 0.98 0.00 0.02
#> 349 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 350 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 351 2 0.3610 0.7441 0.00 0.80 0.00 0.20
#> 352 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 353 2 0.3172 0.7975 0.00 0.84 0.00 0.16
#> 354 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 355 2 0.3610 0.7441 0.00 0.80 0.00 0.20
#> 356 2 0.4284 0.7147 0.02 0.78 0.00 0.20
#> 357 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 358 2 0.3172 0.7975 0.00 0.84 0.00 0.16
#> 359 2 0.3610 0.7441 0.00 0.80 0.00 0.20
#> 360 2 0.3400 0.7717 0.00 0.82 0.00 0.18
#> 361 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 362 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 363 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 364 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 365 2 0.2011 0.8907 0.00 0.92 0.00 0.08
#> 366 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 367 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 368 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 369 1 0.0707 0.9237 0.98 0.00 0.00 0.02
#> 370 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 371 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 372 1 0.1211 0.9176 0.96 0.00 0.00 0.04
#> 373 2 0.2011 0.8903 0.00 0.92 0.00 0.08
#> 374 3 0.3801 0.7259 0.22 0.00 0.78 0.00
#> 375 2 0.2921 0.8222 0.00 0.86 0.00 0.14
#> 376 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 377 2 0.3172 0.7975 0.00 0.84 0.00 0.16
#> 378 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 379 2 0.0000 0.9666 0.00 1.00 0.00 0.00
#> 380 4 0.4134 0.8020 0.00 0.26 0.00 0.74
#> 381 1 0.0000 0.9254 1.00 0.00 0.00 0.00
#> 382 1 0.3610 0.8054 0.80 0.00 0.00 0.20
#> 383 2 0.1211 0.9338 0.00 0.96 0.00 0.04
#> 384 2 0.3400 0.7717 0.00 0.82 0.00 0.18
#> 385 2 0.3172 0.7975 0.00 0.84 0.00 0.16
#> 386 2 0.0000 0.9666 0.00 1.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 379 2.59e-01 2
#> ATC:skmeans 379 5.32e-02 3
#> ATC:skmeans 370 2.65e-30 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node021. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0211"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9465 rows and 181 columns.
#> Top rows (946) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.988 0.995 0.490 0.510 0.510
#> 3 3 1.000 0.975 0.990 0.346 0.769 0.574
#> 4 4 0.793 0.800 0.871 0.110 0.915 0.757
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.996 0.00 1.00
#> 2 1 0.000 0.994 1.00 0.00
#> 3 2 0.000 0.996 0.00 1.00
#> 4 2 0.000 0.996 0.00 1.00
#> 5 2 0.000 0.996 0.00 1.00
#> 6 2 0.000 0.996 0.00 1.00
#> 7 2 0.000 0.996 0.00 1.00
#> 8 2 0.000 0.996 0.00 1.00
#> 9 2 0.000 0.996 0.00 1.00
#> 10 2 0.000 0.996 0.00 1.00
#> 11 2 0.000 0.996 0.00 1.00
#> 12 2 0.000 0.996 0.00 1.00
#> 13 2 0.000 0.996 0.00 1.00
#> 14 2 0.000 0.996 0.00 1.00
#> 15 2 0.000 0.996 0.00 1.00
#> 16 2 0.000 0.996 0.00 1.00
#> 17 2 0.000 0.996 0.00 1.00
#> 18 2 0.000 0.996 0.00 1.00
#> 19 2 0.000 0.996 0.00 1.00
#> 20 2 0.000 0.996 0.00 1.00
#> 21 2 0.000 0.996 0.00 1.00
#> 22 2 0.000 0.996 0.00 1.00
#> 23 2 0.000 0.996 0.00 1.00
#> 24 2 0.000 0.996 0.00 1.00
#> 25 2 0.000 0.996 0.00 1.00
#> 26 2 0.000 0.996 0.00 1.00
#> 27 2 0.000 0.996 0.00 1.00
#> 28 2 0.000 0.996 0.00 1.00
#> 29 2 0.000 0.996 0.00 1.00
#> 30 2 0.000 0.996 0.00 1.00
#> 31 2 0.000 0.996 0.00 1.00
#> 32 2 0.000 0.996 0.00 1.00
#> 33 2 0.000 0.996 0.00 1.00
#> 34 2 0.000 0.996 0.00 1.00
#> 35 2 0.000 0.996 0.00 1.00
#> 36 2 0.000 0.996 0.00 1.00
#> 37 2 0.000 0.996 0.00 1.00
#> 38 2 0.000 0.996 0.00 1.00
#> 39 2 0.000 0.996 0.00 1.00
#> 40 2 0.000 0.996 0.00 1.00
#> 41 2 0.000 0.996 0.00 1.00
#> 42 2 0.000 0.996 0.00 1.00
#> 43 2 0.000 0.996 0.00 1.00
#> 44 2 0.000 0.996 0.00 1.00
#> 45 2 0.000 0.996 0.00 1.00
#> 46 2 0.000 0.996 0.00 1.00
#> 47 2 0.000 0.996 0.00 1.00
#> 48 2 0.000 0.996 0.00 1.00
#> 49 2 0.000 0.996 0.00 1.00
#> 50 2 0.000 0.996 0.00 1.00
#> 51 2 0.000 0.996 0.00 1.00
#> 52 2 0.000 0.996 0.00 1.00
#> 53 2 0.000 0.996 0.00 1.00
#> 54 2 0.000 0.996 0.00 1.00
#> 55 2 0.000 0.996 0.00 1.00
#> 56 2 0.000 0.996 0.00 1.00
#> 57 2 0.000 0.996 0.00 1.00
#> 58 2 0.000 0.996 0.00 1.00
#> 59 2 0.000 0.996 0.00 1.00
#> 60 2 0.000 0.996 0.00 1.00
#> 61 2 0.000 0.996 0.00 1.00
#> 62 2 0.000 0.996 0.00 1.00
#> 63 2 0.000 0.996 0.00 1.00
#> 64 2 0.000 0.996 0.00 1.00
#> 65 2 0.000 0.996 0.00 1.00
#> 66 2 0.000 0.996 0.00 1.00
#> 67 2 0.000 0.996 0.00 1.00
#> 68 2 0.000 0.996 0.00 1.00
#> 69 1 0.000 0.994 1.00 0.00
#> 70 2 0.000 0.996 0.00 1.00
#> 71 2 0.000 0.996 0.00 1.00
#> 72 2 0.000 0.996 0.00 1.00
#> 73 2 0.000 0.996 0.00 1.00
#> 74 2 0.000 0.996 0.00 1.00
#> 75 2 0.000 0.996 0.00 1.00
#> 76 2 0.000 0.996 0.00 1.00
#> 77 1 0.943 0.437 0.64 0.36
#> 78 2 0.000 0.996 0.00 1.00
#> 79 2 0.000 0.996 0.00 1.00
#> 80 2 0.000 0.996 0.00 1.00
#> 81 2 0.000 0.996 0.00 1.00
#> 82 1 0.000 0.994 1.00 0.00
#> 83 1 0.000 0.994 1.00 0.00
#> 84 2 0.000 0.996 0.00 1.00
#> 85 2 0.000 0.996 0.00 1.00
#> 86 2 0.000 0.996 0.00 1.00
#> 87 2 0.000 0.996 0.00 1.00
#> 88 2 0.000 0.996 0.00 1.00
#> 89 2 0.000 0.996 0.00 1.00
#> 90 2 0.000 0.996 0.00 1.00
#> 91 2 0.000 0.996 0.00 1.00
#> 92 2 0.000 0.996 0.00 1.00
#> 93 2 0.000 0.996 0.00 1.00
#> 94 2 0.000 0.996 0.00 1.00
#> 95 2 0.000 0.996 0.00 1.00
#> 96 2 0.000 0.996 0.00 1.00
#> 97 1 0.000 0.994 1.00 0.00
#> 98 1 0.000 0.994 1.00 0.00
#> 99 2 0.000 0.996 0.00 1.00
#> 100 1 0.000 0.994 1.00 0.00
#> 101 1 0.000 0.994 1.00 0.00
#> 102 2 0.000 0.996 0.00 1.00
#> 103 1 0.000 0.994 1.00 0.00
#> 104 1 0.000 0.994 1.00 0.00
#> 105 1 0.000 0.994 1.00 0.00
#> 106 1 0.000 0.994 1.00 0.00
#> 107 1 0.000 0.994 1.00 0.00
#> 108 2 0.000 0.996 0.00 1.00
#> 109 2 0.000 0.996 0.00 1.00
#> 110 2 0.000 0.996 0.00 1.00
#> 111 2 0.000 0.996 0.00 1.00
#> 112 2 0.000 0.996 0.00 1.00
#> 113 2 0.000 0.996 0.00 1.00
#> 114 1 0.529 0.861 0.88 0.12
#> 115 2 0.000 0.996 0.00 1.00
#> 116 2 0.000 0.996 0.00 1.00
#> 117 1 0.000 0.994 1.00 0.00
#> 118 1 0.000 0.994 1.00 0.00
#> 119 1 0.000 0.994 1.00 0.00
#> 120 1 0.000 0.994 1.00 0.00
#> 121 1 0.000 0.994 1.00 0.00
#> 122 1 0.000 0.994 1.00 0.00
#> 123 1 0.000 0.994 1.00 0.00
#> 124 1 0.000 0.994 1.00 0.00
#> 125 1 0.000 0.994 1.00 0.00
#> 126 1 0.000 0.994 1.00 0.00
#> 127 1 0.000 0.994 1.00 0.00
#> 128 1 0.000 0.994 1.00 0.00
#> 129 1 0.000 0.994 1.00 0.00
#> 130 1 0.000 0.994 1.00 0.00
#> 131 1 0.000 0.994 1.00 0.00
#> 132 1 0.000 0.994 1.00 0.00
#> 133 1 0.000 0.994 1.00 0.00
#> 134 1 0.000 0.994 1.00 0.00
#> 135 1 0.000 0.994 1.00 0.00
#> 136 1 0.000 0.994 1.00 0.00
#> 137 1 0.000 0.994 1.00 0.00
#> 138 1 0.000 0.994 1.00 0.00
#> 139 1 0.000 0.994 1.00 0.00
#> 140 1 0.000 0.994 1.00 0.00
#> 141 1 0.000 0.994 1.00 0.00
#> 142 1 0.000 0.994 1.00 0.00
#> 143 1 0.000 0.994 1.00 0.00
#> 144 1 0.000 0.994 1.00 0.00
#> 145 1 0.000 0.994 1.00 0.00
#> 146 1 0.000 0.994 1.00 0.00
#> 147 1 0.000 0.994 1.00 0.00
#> 148 1 0.000 0.994 1.00 0.00
#> 149 1 0.000 0.994 1.00 0.00
#> 150 1 0.000 0.994 1.00 0.00
#> 151 1 0.000 0.994 1.00 0.00
#> 152 1 0.000 0.994 1.00 0.00
#> 153 1 0.000 0.994 1.00 0.00
#> 154 1 0.000 0.994 1.00 0.00
#> 155 1 0.000 0.994 1.00 0.00
#> 156 1 0.000 0.994 1.00 0.00
#> 157 1 0.000 0.994 1.00 0.00
#> 158 1 0.000 0.994 1.00 0.00
#> 159 1 0.000 0.994 1.00 0.00
#> 160 1 0.000 0.994 1.00 0.00
#> 161 1 0.000 0.994 1.00 0.00
#> 162 1 0.000 0.994 1.00 0.00
#> 163 1 0.000 0.994 1.00 0.00
#> 164 1 0.000 0.994 1.00 0.00
#> 165 1 0.000 0.994 1.00 0.00
#> 166 1 0.000 0.994 1.00 0.00
#> 167 1 0.000 0.994 1.00 0.00
#> 168 1 0.000 0.994 1.00 0.00
#> 169 1 0.000 0.994 1.00 0.00
#> 170 2 0.000 0.996 0.00 1.00
#> 171 1 0.000 0.994 1.00 0.00
#> 172 2 0.000 0.996 0.00 1.00
#> 173 1 0.000 0.994 1.00 0.00
#> 174 1 0.000 0.994 1.00 0.00
#> 175 2 0.971 0.326 0.40 0.60
#> 176 1 0.000 0.994 1.00 0.00
#> 177 1 0.000 0.994 1.00 0.00
#> 178 2 0.000 0.996 0.00 1.00
#> 179 1 0.000 0.994 1.00 0.00
#> 180 1 0.000 0.994 1.00 0.00
#> 181 1 0.000 0.994 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.9883 0.00 0.00 1.00
#> 2 3 0.0000 0.9883 0.00 0.00 1.00
#> 3 3 0.0000 0.9883 0.00 0.00 1.00
#> 4 3 0.0000 0.9883 0.00 0.00 1.00
#> 5 3 0.0000 0.9883 0.00 0.00 1.00
#> 6 3 0.0000 0.9883 0.00 0.00 1.00
#> 7 3 0.0000 0.9883 0.00 0.00 1.00
#> 8 3 0.0000 0.9883 0.00 0.00 1.00
#> 9 3 0.0000 0.9883 0.00 0.00 1.00
#> 10 3 0.0000 0.9883 0.00 0.00 1.00
#> 11 3 0.0000 0.9883 0.00 0.00 1.00
#> 12 3 0.0000 0.9883 0.00 0.00 1.00
#> 13 3 0.0000 0.9883 0.00 0.00 1.00
#> 14 3 0.0000 0.9883 0.00 0.00 1.00
#> 15 3 0.0000 0.9883 0.00 0.00 1.00
#> 16 3 0.0000 0.9883 0.00 0.00 1.00
#> 17 3 0.0000 0.9883 0.00 0.00 1.00
#> 18 3 0.0000 0.9883 0.00 0.00 1.00
#> 19 3 0.0000 0.9883 0.00 0.00 1.00
#> 20 3 0.0000 0.9883 0.00 0.00 1.00
#> 21 3 0.0000 0.9883 0.00 0.00 1.00
#> 22 3 0.0000 0.9883 0.00 0.00 1.00
#> 23 3 0.0000 0.9883 0.00 0.00 1.00
#> 24 3 0.0000 0.9883 0.00 0.00 1.00
#> 25 3 0.0000 0.9883 0.00 0.00 1.00
#> 26 3 0.0000 0.9883 0.00 0.00 1.00
#> 27 2 0.0000 0.9965 0.00 1.00 0.00
#> 28 2 0.0000 0.9965 0.00 1.00 0.00
#> 29 3 0.0000 0.9883 0.00 0.00 1.00
#> 30 2 0.0000 0.9965 0.00 1.00 0.00
#> 31 2 0.0000 0.9965 0.00 1.00 0.00
#> 32 2 0.0000 0.9965 0.00 1.00 0.00
#> 33 2 0.0000 0.9965 0.00 1.00 0.00
#> 34 3 0.0000 0.9883 0.00 0.00 1.00
#> 35 2 0.0000 0.9965 0.00 1.00 0.00
#> 36 2 0.0000 0.9965 0.00 1.00 0.00
#> 37 2 0.0000 0.9965 0.00 1.00 0.00
#> 38 3 0.0000 0.9883 0.00 0.00 1.00
#> 39 2 0.0000 0.9965 0.00 1.00 0.00
#> 40 2 0.0000 0.9965 0.00 1.00 0.00
#> 41 3 0.0000 0.9883 0.00 0.00 1.00
#> 42 3 0.0892 0.9686 0.00 0.02 0.98
#> 43 3 0.0000 0.9883 0.00 0.00 1.00
#> 44 2 0.0000 0.9965 0.00 1.00 0.00
#> 45 2 0.0000 0.9965 0.00 1.00 0.00
#> 46 2 0.0000 0.9965 0.00 1.00 0.00
#> 47 2 0.0000 0.9965 0.00 1.00 0.00
#> 48 3 0.0000 0.9883 0.00 0.00 1.00
#> 49 2 0.0000 0.9965 0.00 1.00 0.00
#> 50 2 0.0000 0.9965 0.00 1.00 0.00
#> 51 2 0.0000 0.9965 0.00 1.00 0.00
#> 52 2 0.0000 0.9965 0.00 1.00 0.00
#> 53 2 0.0000 0.9965 0.00 1.00 0.00
#> 54 2 0.0000 0.9965 0.00 1.00 0.00
#> 55 2 0.0000 0.9965 0.00 1.00 0.00
#> 56 2 0.0000 0.9965 0.00 1.00 0.00
#> 57 2 0.0000 0.9965 0.00 1.00 0.00
#> 58 2 0.0000 0.9965 0.00 1.00 0.00
#> 59 2 0.0000 0.9965 0.00 1.00 0.00
#> 60 2 0.0000 0.9965 0.00 1.00 0.00
#> 61 2 0.0000 0.9965 0.00 1.00 0.00
#> 62 2 0.0000 0.9965 0.00 1.00 0.00
#> 63 2 0.0000 0.9965 0.00 1.00 0.00
#> 64 2 0.0000 0.9965 0.00 1.00 0.00
#> 65 2 0.0000 0.9965 0.00 1.00 0.00
#> 66 2 0.0000 0.9965 0.00 1.00 0.00
#> 67 2 0.0000 0.9965 0.00 1.00 0.00
#> 68 2 0.0000 0.9965 0.00 1.00 0.00
#> 69 1 0.0000 0.9844 1.00 0.00 0.00
#> 70 3 0.0000 0.9883 0.00 0.00 1.00
#> 71 2 0.0000 0.9965 0.00 1.00 0.00
#> 72 2 0.0000 0.9965 0.00 1.00 0.00
#> 73 2 0.0000 0.9965 0.00 1.00 0.00
#> 74 2 0.0000 0.9965 0.00 1.00 0.00
#> 75 2 0.0000 0.9965 0.00 1.00 0.00
#> 76 2 0.0000 0.9965 0.00 1.00 0.00
#> 77 2 0.0000 0.9965 0.00 1.00 0.00
#> 78 2 0.0000 0.9965 0.00 1.00 0.00
#> 79 2 0.0000 0.9965 0.00 1.00 0.00
#> 80 2 0.0000 0.9965 0.00 1.00 0.00
#> 81 3 0.0000 0.9883 0.00 0.00 1.00
#> 82 1 0.0000 0.9844 1.00 0.00 0.00
#> 83 1 0.0000 0.9844 1.00 0.00 0.00
#> 84 2 0.0000 0.9965 0.00 1.00 0.00
#> 85 2 0.0000 0.9965 0.00 1.00 0.00
#> 86 2 0.0000 0.9965 0.00 1.00 0.00
#> 87 2 0.0000 0.9965 0.00 1.00 0.00
#> 88 2 0.0000 0.9965 0.00 1.00 0.00
#> 89 2 0.0000 0.9965 0.00 1.00 0.00
#> 90 2 0.0000 0.9965 0.00 1.00 0.00
#> 91 2 0.0000 0.9965 0.00 1.00 0.00
#> 92 2 0.0000 0.9965 0.00 1.00 0.00
#> 93 3 0.0000 0.9883 0.00 0.00 1.00
#> 94 2 0.0000 0.9965 0.00 1.00 0.00
#> 95 2 0.0000 0.9965 0.00 1.00 0.00
#> 96 2 0.0000 0.9965 0.00 1.00 0.00
#> 97 1 0.0000 0.9844 1.00 0.00 0.00
#> 98 3 0.0000 0.9883 0.00 0.00 1.00
#> 99 3 0.0000 0.9883 0.00 0.00 1.00
#> 100 1 0.0000 0.9844 1.00 0.00 0.00
#> 101 3 0.0000 0.9883 0.00 0.00 1.00
#> 102 3 0.0000 0.9883 0.00 0.00 1.00
#> 103 1 0.0000 0.9844 1.00 0.00 0.00
#> 104 3 0.1529 0.9483 0.04 0.00 0.96
#> 105 1 0.0000 0.9844 1.00 0.00 0.00
#> 106 1 0.6302 0.0762 0.52 0.00 0.48
#> 107 3 0.0000 0.9883 0.00 0.00 1.00
#> 108 2 0.0000 0.9965 0.00 1.00 0.00
#> 109 2 0.0000 0.9965 0.00 1.00 0.00
#> 110 2 0.0000 0.9965 0.00 1.00 0.00
#> 111 2 0.0000 0.9965 0.00 1.00 0.00
#> 112 2 0.0000 0.9965 0.00 1.00 0.00
#> 113 2 0.0000 0.9965 0.00 1.00 0.00
#> 114 2 0.0000 0.9965 0.00 1.00 0.00
#> 115 2 0.0000 0.9965 0.00 1.00 0.00
#> 116 2 0.0000 0.9965 0.00 1.00 0.00
#> 117 1 0.0000 0.9844 1.00 0.00 0.00
#> 118 1 0.0000 0.9844 1.00 0.00 0.00
#> 119 1 0.0000 0.9844 1.00 0.00 0.00
#> 120 1 0.3686 0.8297 0.86 0.00 0.14
#> 121 1 0.0000 0.9844 1.00 0.00 0.00
#> 122 1 0.0000 0.9844 1.00 0.00 0.00
#> 123 1 0.0000 0.9844 1.00 0.00 0.00
#> 124 1 0.0000 0.9844 1.00 0.00 0.00
#> 125 3 0.0000 0.9883 0.00 0.00 1.00
#> 126 1 0.0000 0.9844 1.00 0.00 0.00
#> 127 1 0.0000 0.9844 1.00 0.00 0.00
#> 128 1 0.0000 0.9844 1.00 0.00 0.00
#> 129 1 0.0000 0.9844 1.00 0.00 0.00
#> 130 1 0.0000 0.9844 1.00 0.00 0.00
#> 131 1 0.0000 0.9844 1.00 0.00 0.00
#> 132 1 0.0000 0.9844 1.00 0.00 0.00
#> 133 1 0.0000 0.9844 1.00 0.00 0.00
#> 134 1 0.0000 0.9844 1.00 0.00 0.00
#> 135 1 0.0000 0.9844 1.00 0.00 0.00
#> 136 1 0.0000 0.9844 1.00 0.00 0.00
#> 137 1 0.0000 0.9844 1.00 0.00 0.00
#> 138 1 0.0000 0.9844 1.00 0.00 0.00
#> 139 1 0.0000 0.9844 1.00 0.00 0.00
#> 140 1 0.0000 0.9844 1.00 0.00 0.00
#> 141 1 0.0000 0.9844 1.00 0.00 0.00
#> 142 1 0.0000 0.9844 1.00 0.00 0.00
#> 143 1 0.0000 0.9844 1.00 0.00 0.00
#> 144 1 0.0000 0.9844 1.00 0.00 0.00
#> 145 1 0.0000 0.9844 1.00 0.00 0.00
#> 146 1 0.5706 0.5289 0.68 0.00 0.32
#> 147 1 0.0000 0.9844 1.00 0.00 0.00
#> 148 1 0.0000 0.9844 1.00 0.00 0.00
#> 149 3 0.0000 0.9883 0.00 0.00 1.00
#> 150 1 0.0000 0.9844 1.00 0.00 0.00
#> 151 3 0.6244 0.1906 0.44 0.00 0.56
#> 152 1 0.0000 0.9844 1.00 0.00 0.00
#> 153 1 0.0000 0.9844 1.00 0.00 0.00
#> 154 1 0.0000 0.9844 1.00 0.00 0.00
#> 155 1 0.0000 0.9844 1.00 0.00 0.00
#> 156 1 0.0000 0.9844 1.00 0.00 0.00
#> 157 1 0.0000 0.9844 1.00 0.00 0.00
#> 158 1 0.0000 0.9844 1.00 0.00 0.00
#> 159 1 0.0000 0.9844 1.00 0.00 0.00
#> 160 1 0.0000 0.9844 1.00 0.00 0.00
#> 161 1 0.0000 0.9844 1.00 0.00 0.00
#> 162 1 0.0000 0.9844 1.00 0.00 0.00
#> 163 1 0.0000 0.9844 1.00 0.00 0.00
#> 164 1 0.0000 0.9844 1.00 0.00 0.00
#> 165 1 0.0000 0.9844 1.00 0.00 0.00
#> 166 1 0.0000 0.9844 1.00 0.00 0.00
#> 167 1 0.0000 0.9844 1.00 0.00 0.00
#> 168 1 0.0000 0.9844 1.00 0.00 0.00
#> 169 2 0.0892 0.9753 0.02 0.98 0.00
#> 170 2 0.0000 0.9965 0.00 1.00 0.00
#> 171 1 0.0000 0.9844 1.00 0.00 0.00
#> 172 2 0.0000 0.9965 0.00 1.00 0.00
#> 173 1 0.0000 0.9844 1.00 0.00 0.00
#> 174 1 0.0000 0.9844 1.00 0.00 0.00
#> 175 2 0.6500 0.7226 0.10 0.76 0.14
#> 176 1 0.0000 0.9844 1.00 0.00 0.00
#> 177 1 0.0000 0.9844 1.00 0.00 0.00
#> 178 2 0.0000 0.9965 0.00 1.00 0.00
#> 179 1 0.0000 0.9844 1.00 0.00 0.00
#> 180 1 0.1529 0.9462 0.96 0.00 0.04
#> 181 1 0.0000 0.9844 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 2 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 3 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 4 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 5 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 6 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 7 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 8 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 9 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 10 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 11 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 12 3 0.2345 0.830 0.00 0.10 0.90 0.00
#> 13 3 0.4790 0.540 0.00 0.00 0.62 0.38
#> 14 3 0.2647 0.807 0.00 0.12 0.88 0.00
#> 15 3 0.1637 0.868 0.00 0.06 0.94 0.00
#> 16 3 0.4713 0.563 0.00 0.00 0.64 0.36
#> 17 3 0.2345 0.829 0.00 0.10 0.90 0.00
#> 18 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 19 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 20 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 21 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 22 3 0.4790 0.540 0.00 0.00 0.62 0.38
#> 23 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 24 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 25 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 26 3 0.1211 0.891 0.00 0.00 0.96 0.04
#> 27 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 28 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 29 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 30 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 31 2 0.4624 0.740 0.00 0.66 0.00 0.34
#> 32 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 33 2 0.2345 0.803 0.00 0.90 0.00 0.10
#> 34 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 35 2 0.1637 0.773 0.00 0.94 0.00 0.06
#> 36 2 0.4713 0.731 0.00 0.64 0.00 0.36
#> 37 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 38 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 39 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 40 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 41 3 0.4790 0.540 0.00 0.00 0.62 0.38
#> 42 4 0.6656 0.562 0.00 0.16 0.22 0.62
#> 43 3 0.2411 0.869 0.00 0.04 0.92 0.04
#> 44 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 45 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 46 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 47 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 48 4 0.0707 0.495 0.00 0.00 0.02 0.98
#> 49 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 50 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 51 2 0.4522 0.328 0.00 0.68 0.00 0.32
#> 52 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 53 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 54 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 55 2 0.4713 0.731 0.00 0.64 0.00 0.36
#> 56 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 57 2 0.1637 0.812 0.00 0.94 0.00 0.06
#> 58 2 0.4624 0.741 0.00 0.66 0.00 0.34
#> 59 2 0.4522 0.747 0.00 0.68 0.00 0.32
#> 60 2 0.4624 0.740 0.00 0.66 0.00 0.34
#> 61 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 62 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 63 2 0.4624 0.741 0.00 0.66 0.00 0.34
#> 64 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 65 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 66 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 67 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 68 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 69 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 70 3 0.4790 0.540 0.00 0.00 0.62 0.38
#> 71 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 72 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 73 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 74 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 75 2 0.4522 0.747 0.00 0.68 0.00 0.32
#> 76 2 0.4624 0.740 0.00 0.66 0.00 0.34
#> 77 2 0.5271 0.730 0.02 0.64 0.00 0.34
#> 78 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 79 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 80 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 81 3 0.2647 0.807 0.00 0.12 0.88 0.00
#> 82 1 0.0707 0.940 0.98 0.02 0.00 0.00
#> 83 1 0.1211 0.915 0.96 0.00 0.00 0.04
#> 84 2 0.2345 0.732 0.00 0.90 0.00 0.10
#> 85 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 86 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 87 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 88 2 0.1211 0.816 0.00 0.96 0.00 0.04
#> 89 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 90 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 91 2 0.4713 0.731 0.00 0.64 0.00 0.36
#> 92 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 93 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 94 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 95 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 96 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 97 1 0.4790 0.347 0.62 0.00 0.00 0.38
#> 98 4 0.4790 0.449 0.00 0.00 0.38 0.62
#> 99 3 0.0000 0.913 0.00 0.00 1.00 0.00
#> 100 4 0.4790 0.585 0.38 0.00 0.00 0.62
#> 101 3 0.0707 0.903 0.00 0.00 0.98 0.02
#> 102 3 0.0707 0.903 0.00 0.00 0.98 0.02
#> 103 4 0.4907 0.535 0.42 0.00 0.00 0.58
#> 104 4 0.4790 0.449 0.00 0.00 0.38 0.62
#> 105 1 0.1637 0.892 0.94 0.00 0.00 0.06
#> 106 4 0.6513 0.675 0.18 0.00 0.18 0.64
#> 107 4 0.0707 0.495 0.00 0.00 0.02 0.98
#> 108 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 109 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 110 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 111 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 112 2 0.4907 0.688 0.00 0.58 0.00 0.42
#> 113 2 0.2011 0.754 0.00 0.92 0.00 0.08
#> 114 2 0.7139 0.568 0.14 0.50 0.00 0.36
#> 115 2 0.4713 0.731 0.00 0.64 0.00 0.36
#> 116 2 0.4713 0.731 0.00 0.64 0.00 0.36
#> 117 4 0.5355 0.624 0.36 0.00 0.02 0.62
#> 118 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 119 4 0.4790 0.607 0.38 0.00 0.00 0.62
#> 120 4 0.6370 0.604 0.10 0.00 0.28 0.62
#> 121 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 122 4 0.4790 0.607 0.38 0.00 0.00 0.62
#> 123 4 0.4790 0.607 0.38 0.00 0.00 0.62
#> 124 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 125 3 0.0707 0.903 0.00 0.00 0.98 0.02
#> 126 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 127 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 128 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 129 1 0.4977 -0.238 0.54 0.00 0.00 0.46
#> 130 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 131 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 132 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 133 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 134 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 135 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 136 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 137 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 138 4 0.4790 0.607 0.38 0.00 0.00 0.62
#> 139 4 0.5986 0.650 0.32 0.00 0.06 0.62
#> 140 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 141 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 142 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 143 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 144 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 145 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 146 4 0.6201 0.580 0.08 0.00 0.30 0.62
#> 147 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 148 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 149 4 0.4948 0.331 0.00 0.00 0.44 0.56
#> 150 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 151 4 0.6609 0.607 0.08 0.02 0.26 0.64
#> 152 4 0.4790 0.607 0.38 0.00 0.00 0.62
#> 153 1 0.0707 0.942 0.98 0.00 0.00 0.02
#> 154 4 0.4790 0.607 0.38 0.00 0.00 0.62
#> 155 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 156 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 157 4 0.6594 0.663 0.24 0.14 0.00 0.62
#> 158 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 159 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 160 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 161 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 162 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 163 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 164 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 165 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 166 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 167 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 168 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 169 4 0.4855 0.451 0.00 0.40 0.00 0.60
#> 170 4 0.4790 0.483 0.00 0.38 0.00 0.62
#> 171 4 0.6686 0.662 0.18 0.20 0.00 0.62
#> 172 2 0.0000 0.822 0.00 1.00 0.00 0.00
#> 173 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 174 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 175 4 0.4855 0.452 0.00 0.40 0.00 0.60
#> 176 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 177 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 178 2 0.4790 0.720 0.00 0.62 0.00 0.38
#> 179 1 0.0000 0.966 1.00 0.00 0.00 0.00
#> 180 1 0.5175 0.608 0.76 0.00 0.12 0.12
#> 181 1 0.0000 0.966 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 179 1.78e-04 2
#> ATC:skmeans 179 2.24e-17 3
#> ATC:skmeans 170 5.53e-18 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0211. Child nodes: Node021111-leaf , Node021112-leaf , Node021121-leaf , Node021122-leaf , Node031221-leaf , Node031222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["02111"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9056 rows and 65 columns.
#> Top rows (906) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.994 0.997 0.507 0.493 0.493
#> 3 3 0.932 0.908 0.961 0.299 0.805 0.622
#> 4 4 0.689 0.640 0.829 0.094 0.877 0.670
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.469 0.889 0.10 0.90
#> 2 2 0.000 0.997 0.00 1.00
#> 3 2 0.000 0.997 0.00 1.00
#> 4 2 0.000 0.997 0.00 1.00
#> 5 1 0.000 0.998 1.00 0.00
#> 6 1 0.000 0.998 1.00 0.00
#> 7 1 0.000 0.998 1.00 0.00
#> 8 1 0.000 0.998 1.00 0.00
#> 9 1 0.000 0.998 1.00 0.00
#> 10 1 0.000 0.998 1.00 0.00
#> 11 1 0.000 0.998 1.00 0.00
#> 12 1 0.000 0.998 1.00 0.00
#> 13 1 0.000 0.998 1.00 0.00
#> 14 1 0.000 0.998 1.00 0.00
#> 15 1 0.000 0.998 1.00 0.00
#> 16 1 0.000 0.998 1.00 0.00
#> 17 2 0.000 0.997 0.00 1.00
#> 18 2 0.000 0.997 0.00 1.00
#> 19 1 0.000 0.998 1.00 0.00
#> 20 1 0.000 0.998 1.00 0.00
#> 21 1 0.000 0.998 1.00 0.00
#> 22 2 0.000 0.997 0.00 1.00
#> 23 1 0.000 0.998 1.00 0.00
#> 24 1 0.000 0.998 1.00 0.00
#> 25 2 0.000 0.997 0.00 1.00
#> 26 2 0.000 0.997 0.00 1.00
#> 27 2 0.000 0.997 0.00 1.00
#> 28 1 0.000 0.998 1.00 0.00
#> 29 1 0.000 0.998 1.00 0.00
#> 30 1 0.000 0.998 1.00 0.00
#> 31 1 0.000 0.998 1.00 0.00
#> 32 1 0.000 0.998 1.00 0.00
#> 33 2 0.000 0.997 0.00 1.00
#> 34 2 0.000 0.997 0.00 1.00
#> 35 2 0.000 0.997 0.00 1.00
#> 36 2 0.000 0.997 0.00 1.00
#> 37 1 0.000 0.998 1.00 0.00
#> 38 1 0.000 0.998 1.00 0.00
#> 39 1 0.000 0.998 1.00 0.00
#> 40 2 0.000 0.997 0.00 1.00
#> 41 1 0.000 0.998 1.00 0.00
#> 42 2 0.000 0.997 0.00 1.00
#> 43 1 0.000 0.998 1.00 0.00
#> 44 1 0.000 0.998 1.00 0.00
#> 45 1 0.000 0.998 1.00 0.00
#> 46 1 0.000 0.998 1.00 0.00
#> 47 2 0.000 0.997 0.00 1.00
#> 48 2 0.000 0.997 0.00 1.00
#> 49 1 0.000 0.998 1.00 0.00
#> 50 2 0.000 0.997 0.00 1.00
#> 51 2 0.000 0.997 0.00 1.00
#> 52 2 0.000 0.997 0.00 1.00
#> 53 2 0.000 0.997 0.00 1.00
#> 54 2 0.000 0.997 0.00 1.00
#> 55 2 0.000 0.997 0.00 1.00
#> 56 2 0.000 0.997 0.00 1.00
#> 57 2 0.000 0.997 0.00 1.00
#> 58 1 0.000 0.998 1.00 0.00
#> 59 2 0.000 0.997 0.00 1.00
#> 60 2 0.000 0.997 0.00 1.00
#> 61 2 0.000 0.997 0.00 1.00
#> 62 1 0.402 0.913 0.92 0.08
#> 63 2 0.000 0.997 0.00 1.00
#> 64 1 0.000 0.998 1.00 0.00
#> 65 2 0.000 0.997 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.7277 0.556 0.06 0.28 0.66
#> 2 2 0.0000 0.978 0.00 1.00 0.00
#> 3 2 0.0000 0.978 0.00 1.00 0.00
#> 4 2 0.0000 0.978 0.00 1.00 0.00
#> 5 1 0.0000 0.950 1.00 0.00 0.00
#> 6 1 0.0000 0.950 1.00 0.00 0.00
#> 7 1 0.0000 0.950 1.00 0.00 0.00
#> 8 1 0.0000 0.950 1.00 0.00 0.00
#> 9 1 0.0000 0.950 1.00 0.00 0.00
#> 10 3 0.5835 0.447 0.34 0.00 0.66
#> 11 1 0.0000 0.950 1.00 0.00 0.00
#> 12 1 0.0000 0.950 1.00 0.00 0.00
#> 13 1 0.0000 0.950 1.00 0.00 0.00
#> 14 1 0.0000 0.950 1.00 0.00 0.00
#> 15 1 0.0000 0.950 1.00 0.00 0.00
#> 16 1 0.0000 0.950 1.00 0.00 0.00
#> 17 2 0.2537 0.905 0.00 0.92 0.08
#> 18 2 0.0000 0.978 0.00 1.00 0.00
#> 19 3 0.0000 0.936 0.00 0.00 1.00
#> 20 1 0.0000 0.950 1.00 0.00 0.00
#> 21 3 0.0000 0.936 0.00 0.00 1.00
#> 22 3 0.0000 0.936 0.00 0.00 1.00
#> 23 3 0.0892 0.924 0.02 0.00 0.98
#> 24 1 0.5560 0.567 0.70 0.00 0.30
#> 25 2 0.0000 0.978 0.00 1.00 0.00
#> 26 2 0.0000 0.978 0.00 1.00 0.00
#> 27 3 0.2537 0.874 0.00 0.08 0.92
#> 28 1 0.0000 0.950 1.00 0.00 0.00
#> 29 1 0.0000 0.950 1.00 0.00 0.00
#> 30 1 0.0000 0.950 1.00 0.00 0.00
#> 31 1 0.0000 0.950 1.00 0.00 0.00
#> 32 1 0.0000 0.950 1.00 0.00 0.00
#> 33 2 0.5706 0.527 0.00 0.68 0.32
#> 34 2 0.2537 0.905 0.00 0.92 0.08
#> 35 2 0.0000 0.978 0.00 1.00 0.00
#> 36 2 0.0000 0.978 0.00 1.00 0.00
#> 37 1 0.0000 0.950 1.00 0.00 0.00
#> 38 1 0.6244 0.222 0.56 0.00 0.44
#> 39 1 0.0000 0.950 1.00 0.00 0.00
#> 40 2 0.0000 0.978 0.00 1.00 0.00
#> 41 1 0.0000 0.950 1.00 0.00 0.00
#> 42 2 0.0000 0.978 0.00 1.00 0.00
#> 43 1 0.0000 0.950 1.00 0.00 0.00
#> 44 3 0.0000 0.936 0.00 0.00 1.00
#> 45 1 0.2537 0.882 0.92 0.00 0.08
#> 46 1 0.5397 0.609 0.72 0.00 0.28
#> 47 3 0.0000 0.936 0.00 0.00 1.00
#> 48 2 0.0000 0.978 0.00 1.00 0.00
#> 49 3 0.1529 0.908 0.04 0.00 0.96
#> 50 2 0.0000 0.978 0.00 1.00 0.00
#> 51 2 0.0000 0.978 0.00 1.00 0.00
#> 52 3 0.0000 0.936 0.00 0.00 1.00
#> 53 2 0.0000 0.978 0.00 1.00 0.00
#> 54 2 0.0000 0.978 0.00 1.00 0.00
#> 55 3 0.0000 0.936 0.00 0.00 1.00
#> 56 2 0.0000 0.978 0.00 1.00 0.00
#> 57 2 0.0000 0.978 0.00 1.00 0.00
#> 58 1 0.0000 0.950 1.00 0.00 0.00
#> 59 2 0.0000 0.978 0.00 1.00 0.00
#> 60 3 0.0000 0.936 0.00 0.00 1.00
#> 61 2 0.0000 0.978 0.00 1.00 0.00
#> 62 3 0.0000 0.936 0.00 0.00 1.00
#> 63 2 0.0000 0.978 0.00 1.00 0.00
#> 64 1 0.3340 0.824 0.88 0.12 0.00
#> 65 2 0.0000 0.978 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 4 0.8724 -0.2934 0.06 0.26 0.22 0.46
#> 2 2 0.2830 0.8898 0.00 0.90 0.04 0.06
#> 3 2 0.0707 0.9380 0.00 0.98 0.00 0.02
#> 4 2 0.0707 0.9380 0.00 0.98 0.00 0.02
#> 5 4 0.5000 0.5558 0.50 0.00 0.00 0.50
#> 6 1 0.4977 -0.5191 0.54 0.00 0.00 0.46
#> 7 4 0.5000 0.5558 0.50 0.00 0.00 0.50
#> 8 1 0.4522 0.0124 0.68 0.00 0.00 0.32
#> 9 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 10 1 0.6656 0.3822 0.62 0.00 0.16 0.22
#> 11 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 12 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 13 1 0.4134 0.2410 0.74 0.00 0.00 0.26
#> 14 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 15 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 16 1 0.4907 -0.2717 0.58 0.00 0.00 0.42
#> 17 2 0.4610 0.7738 0.00 0.80 0.10 0.10
#> 18 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 19 3 0.4227 0.7254 0.06 0.00 0.82 0.12
#> 20 1 0.3801 0.3623 0.78 0.00 0.00 0.22
#> 21 3 0.6382 0.3631 0.34 0.00 0.58 0.08
#> 22 3 0.2011 0.7624 0.00 0.00 0.92 0.08
#> 23 3 0.7869 0.2455 0.34 0.00 0.38 0.28
#> 24 1 0.4841 0.5470 0.78 0.00 0.14 0.08
#> 25 2 0.2011 0.9059 0.00 0.92 0.00 0.08
#> 26 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 27 3 0.6320 0.6400 0.00 0.18 0.66 0.16
#> 28 4 0.4907 0.6386 0.42 0.00 0.00 0.58
#> 29 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 30 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 31 4 0.4948 0.6272 0.44 0.00 0.00 0.56
#> 32 4 0.4948 0.6370 0.44 0.00 0.00 0.56
#> 33 3 0.6605 0.0553 0.00 0.44 0.48 0.08
#> 34 2 0.6649 0.2846 0.00 0.56 0.34 0.10
#> 35 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 36 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 37 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 38 1 0.5902 0.4685 0.70 0.00 0.16 0.14
#> 39 4 0.4713 0.6291 0.36 0.00 0.00 0.64
#> 40 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 41 1 0.2345 0.5949 0.90 0.00 0.00 0.10
#> 42 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 43 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 44 3 0.0000 0.7637 0.00 0.00 1.00 0.00
#> 45 1 0.5956 0.4078 0.68 0.00 0.10 0.22
#> 46 1 0.3821 0.5961 0.84 0.00 0.12 0.04
#> 47 3 0.2345 0.7580 0.00 0.00 0.90 0.10
#> 48 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 49 1 0.7139 0.0525 0.50 0.00 0.36 0.14
#> 50 2 0.3335 0.8354 0.00 0.86 0.12 0.02
#> 51 2 0.0707 0.9375 0.00 0.98 0.00 0.02
#> 52 3 0.1211 0.7613 0.00 0.00 0.96 0.04
#> 53 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 54 2 0.1211 0.9331 0.00 0.96 0.00 0.04
#> 55 3 0.2345 0.7462 0.00 0.00 0.90 0.10
#> 56 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 57 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 58 1 0.0000 0.7024 1.00 0.00 0.00 0.00
#> 59 2 0.1637 0.9231 0.00 0.94 0.00 0.06
#> 60 3 0.5489 0.7041 0.00 0.06 0.70 0.24
#> 61 2 0.2011 0.9012 0.00 0.92 0.00 0.08
#> 62 3 0.1637 0.7653 0.00 0.00 0.94 0.06
#> 63 2 0.0000 0.9441 0.00 1.00 0.00 0.00
#> 64 4 0.5616 0.5028 0.18 0.06 0.02 0.74
#> 65 2 0.1211 0.9296 0.00 0.96 0.00 0.04
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 65 NA 2
#> ATC:skmeans 63 NA 3
#> ATC:skmeans 51 NA 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0211. Child nodes: Node021111-leaf , Node021112-leaf , Node021121-leaf , Node021122-leaf , Node031221-leaf , Node031222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["02112"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9147 rows and 71 columns.
#> Top rows (915) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.999 0.964 0.984 0.506 0.495 0.495
#> 3 3 0.906 0.914 0.964 0.227 0.868 0.738
#> 4 4 0.762 0.744 0.886 0.101 0.930 0.823
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.000 0.978 1.00 0.00
#> 2 1 0.327 0.930 0.94 0.06
#> 3 1 0.634 0.820 0.84 0.16
#> 4 2 0.000 0.988 0.00 1.00
#> 5 2 0.000 0.988 0.00 1.00
#> 6 2 0.000 0.988 0.00 1.00
#> 7 1 0.000 0.978 1.00 0.00
#> 8 2 0.000 0.988 0.00 1.00
#> 9 2 0.000 0.988 0.00 1.00
#> 10 1 0.000 0.978 1.00 0.00
#> 11 1 0.000 0.978 1.00 0.00
#> 12 2 0.000 0.988 0.00 1.00
#> 13 1 0.000 0.978 1.00 0.00
#> 14 1 0.000 0.978 1.00 0.00
#> 15 1 0.000 0.978 1.00 0.00
#> 16 2 0.000 0.988 0.00 1.00
#> 17 1 0.000 0.978 1.00 0.00
#> 18 1 0.000 0.978 1.00 0.00
#> 19 1 0.000 0.978 1.00 0.00
#> 20 1 0.000 0.978 1.00 0.00
#> 21 1 0.000 0.978 1.00 0.00
#> 22 2 0.000 0.988 0.00 1.00
#> 23 2 0.000 0.988 0.00 1.00
#> 24 1 0.760 0.728 0.78 0.22
#> 25 2 0.000 0.988 0.00 1.00
#> 26 2 0.000 0.988 0.00 1.00
#> 27 2 0.000 0.988 0.00 1.00
#> 28 1 0.000 0.978 1.00 0.00
#> 29 2 0.000 0.988 0.00 1.00
#> 30 2 0.000 0.988 0.00 1.00
#> 31 2 0.000 0.988 0.00 1.00
#> 32 1 0.000 0.978 1.00 0.00
#> 33 1 0.000 0.978 1.00 0.00
#> 34 1 0.000 0.978 1.00 0.00
#> 35 1 0.680 0.789 0.82 0.18
#> 36 1 0.000 0.978 1.00 0.00
#> 37 2 0.000 0.988 0.00 1.00
#> 38 1 0.000 0.978 1.00 0.00
#> 39 2 0.000 0.988 0.00 1.00
#> 40 2 0.000 0.988 0.00 1.00
#> 41 2 0.000 0.988 0.00 1.00
#> 42 2 0.000 0.988 0.00 1.00
#> 43 2 0.000 0.988 0.00 1.00
#> 44 2 0.000 0.988 0.00 1.00
#> 45 1 0.000 0.978 1.00 0.00
#> 46 1 0.000 0.978 1.00 0.00
#> 47 1 0.000 0.978 1.00 0.00
#> 48 1 0.000 0.978 1.00 0.00
#> 49 1 0.000 0.978 1.00 0.00
#> 50 2 0.000 0.988 0.00 1.00
#> 51 1 0.000 0.978 1.00 0.00
#> 52 1 0.000 0.978 1.00 0.00
#> 53 2 0.000 0.988 0.00 1.00
#> 54 2 0.000 0.988 0.00 1.00
#> 55 1 0.000 0.978 1.00 0.00
#> 56 2 0.000 0.988 0.00 1.00
#> 57 1 0.000 0.978 1.00 0.00
#> 58 1 0.402 0.911 0.92 0.08
#> 59 2 0.000 0.988 0.00 1.00
#> 60 1 0.000 0.978 1.00 0.00
#> 61 2 0.943 0.421 0.36 0.64
#> 62 2 0.000 0.988 0.00 1.00
#> 63 1 0.000 0.978 1.00 0.00
#> 64 2 0.000 0.988 0.00 1.00
#> 65 2 0.000 0.988 0.00 1.00
#> 66 2 0.000 0.988 0.00 1.00
#> 67 1 0.000 0.978 1.00 0.00
#> 68 1 0.000 0.978 1.00 0.00
#> 69 1 0.000 0.978 1.00 0.00
#> 70 1 0.402 0.912 0.92 0.08
#> 71 2 0.000 0.988 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.870 0.00 0.00 1.00
#> 2 3 0.0000 0.870 0.00 0.00 1.00
#> 3 3 0.0000 0.870 0.00 0.00 1.00
#> 4 2 0.0000 0.965 0.00 1.00 0.00
#> 5 2 0.0000 0.965 0.00 1.00 0.00
#> 6 2 0.2959 0.876 0.00 0.90 0.10
#> 7 1 0.0000 0.981 1.00 0.00 0.00
#> 8 2 0.0000 0.965 0.00 1.00 0.00
#> 9 2 0.0000 0.965 0.00 1.00 0.00
#> 10 1 0.0000 0.981 1.00 0.00 0.00
#> 11 1 0.0000 0.981 1.00 0.00 0.00
#> 12 2 0.0000 0.965 0.00 1.00 0.00
#> 13 3 0.6280 0.186 0.46 0.00 0.54
#> 14 1 0.0000 0.981 1.00 0.00 0.00
#> 15 1 0.0000 0.981 1.00 0.00 0.00
#> 16 2 0.6244 0.208 0.00 0.56 0.44
#> 17 1 0.0000 0.981 1.00 0.00 0.00
#> 18 1 0.0000 0.981 1.00 0.00 0.00
#> 19 1 0.0000 0.981 1.00 0.00 0.00
#> 20 1 0.0000 0.981 1.00 0.00 0.00
#> 21 1 0.0000 0.981 1.00 0.00 0.00
#> 22 2 0.0000 0.965 0.00 1.00 0.00
#> 23 2 0.0000 0.965 0.00 1.00 0.00
#> 24 1 0.8838 0.264 0.58 0.20 0.22
#> 25 2 0.4291 0.773 0.00 0.82 0.18
#> 26 2 0.1529 0.936 0.00 0.96 0.04
#> 27 2 0.0000 0.965 0.00 1.00 0.00
#> 28 1 0.0000 0.981 1.00 0.00 0.00
#> 29 2 0.0000 0.965 0.00 1.00 0.00
#> 30 2 0.0000 0.965 0.00 1.00 0.00
#> 31 2 0.0000 0.965 0.00 1.00 0.00
#> 32 1 0.0000 0.981 1.00 0.00 0.00
#> 33 1 0.1529 0.942 0.96 0.00 0.04
#> 34 1 0.0000 0.981 1.00 0.00 0.00
#> 35 3 0.2959 0.831 0.10 0.00 0.90
#> 36 1 0.0000 0.981 1.00 0.00 0.00
#> 37 2 0.0000 0.965 0.00 1.00 0.00
#> 38 1 0.0000 0.981 1.00 0.00 0.00
#> 39 2 0.0000 0.965 0.00 1.00 0.00
#> 40 2 0.0892 0.951 0.00 0.98 0.02
#> 41 2 0.0000 0.965 0.00 1.00 0.00
#> 42 2 0.0000 0.965 0.00 1.00 0.00
#> 43 2 0.0000 0.965 0.00 1.00 0.00
#> 44 2 0.0000 0.965 0.00 1.00 0.00
#> 45 1 0.1529 0.942 0.96 0.00 0.04
#> 46 1 0.0000 0.981 1.00 0.00 0.00
#> 47 1 0.0000 0.981 1.00 0.00 0.00
#> 48 1 0.0000 0.981 1.00 0.00 0.00
#> 49 1 0.0000 0.981 1.00 0.00 0.00
#> 50 2 0.4449 0.833 0.04 0.86 0.10
#> 51 1 0.0000 0.981 1.00 0.00 0.00
#> 52 1 0.0000 0.981 1.00 0.00 0.00
#> 53 2 0.0000 0.965 0.00 1.00 0.00
#> 54 3 0.2959 0.816 0.00 0.10 0.90
#> 55 1 0.0000 0.981 1.00 0.00 0.00
#> 56 3 0.4555 0.701 0.00 0.20 0.80
#> 57 1 0.0000 0.981 1.00 0.00 0.00
#> 58 3 0.5147 0.752 0.18 0.02 0.80
#> 59 2 0.0000 0.965 0.00 1.00 0.00
#> 60 1 0.0892 0.962 0.98 0.00 0.02
#> 61 3 0.1529 0.858 0.00 0.04 0.96
#> 62 2 0.0000 0.965 0.00 1.00 0.00
#> 63 1 0.0000 0.981 1.00 0.00 0.00
#> 64 2 0.1529 0.936 0.00 0.96 0.04
#> 65 2 0.0000 0.965 0.00 1.00 0.00
#> 66 2 0.0000 0.965 0.00 1.00 0.00
#> 67 1 0.0000 0.981 1.00 0.00 0.00
#> 68 1 0.0000 0.981 1.00 0.00 0.00
#> 69 1 0.0000 0.981 1.00 0.00 0.00
#> 70 3 0.0000 0.870 0.00 0.00 1.00
#> 71 2 0.0000 0.965 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.0707 0.7763 0.00 0.00 0.98 0.02
#> 2 3 0.0000 0.7818 0.00 0.00 1.00 0.00
#> 3 3 0.0000 0.7818 0.00 0.00 1.00 0.00
#> 4 2 0.3606 0.7719 0.00 0.84 0.02 0.14
#> 5 2 0.0707 0.8473 0.00 0.98 0.00 0.02
#> 6 2 0.4949 0.6390 0.00 0.76 0.18 0.06
#> 7 1 0.4855 0.3717 0.60 0.00 0.00 0.40
#> 8 2 0.0707 0.8401 0.00 0.98 0.00 0.02
#> 9 2 0.4713 0.4944 0.00 0.64 0.00 0.36
#> 10 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 11 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 12 2 0.0707 0.8473 0.00 0.98 0.00 0.02
#> 13 1 0.7805 -0.1368 0.42 0.00 0.28 0.30
#> 14 1 0.0707 0.9303 0.98 0.00 0.02 0.00
#> 15 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 16 2 0.7674 -0.0789 0.00 0.46 0.28 0.26
#> 17 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 18 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 19 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 20 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 21 1 0.1211 0.9159 0.96 0.00 0.00 0.04
#> 22 2 0.5000 0.0931 0.00 0.50 0.00 0.50
#> 23 2 0.2345 0.8232 0.00 0.90 0.00 0.10
#> 24 4 0.6367 0.4595 0.14 0.08 0.06 0.72
#> 25 2 0.6299 0.3733 0.00 0.60 0.08 0.32
#> 26 4 0.4790 0.2057 0.00 0.38 0.00 0.62
#> 27 2 0.0707 0.8401 0.00 0.98 0.00 0.02
#> 28 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 29 2 0.0707 0.8473 0.00 0.98 0.00 0.02
#> 30 2 0.0707 0.8473 0.00 0.98 0.00 0.02
#> 31 2 0.0707 0.8473 0.00 0.98 0.00 0.02
#> 32 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 33 1 0.5820 0.5644 0.68 0.00 0.08 0.24
#> 34 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 35 4 0.7040 -0.0447 0.12 0.00 0.42 0.46
#> 36 1 0.0707 0.9310 0.98 0.00 0.02 0.00
#> 37 2 0.0000 0.8433 0.00 1.00 0.00 0.00
#> 38 1 0.1211 0.9147 0.96 0.00 0.04 0.00
#> 39 2 0.1637 0.8280 0.00 0.94 0.00 0.06
#> 40 2 0.4642 0.6653 0.00 0.74 0.02 0.24
#> 41 2 0.3610 0.7559 0.00 0.80 0.00 0.20
#> 42 2 0.1637 0.8183 0.00 0.94 0.06 0.00
#> 43 2 0.1211 0.8411 0.00 0.96 0.00 0.04
#> 44 2 0.0707 0.8473 0.00 0.98 0.00 0.02
#> 45 1 0.3525 0.8212 0.86 0.00 0.10 0.04
#> 46 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 47 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 48 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 49 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 50 4 0.1913 0.5277 0.00 0.04 0.02 0.94
#> 51 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 52 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 53 2 0.4277 0.6385 0.00 0.72 0.00 0.28
#> 54 3 0.6497 0.3009 0.00 0.20 0.64 0.16
#> 55 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 56 4 0.7414 0.2136 0.00 0.18 0.34 0.48
#> 57 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 58 3 0.7201 0.3758 0.18 0.14 0.64 0.04
#> 59 2 0.0707 0.8473 0.00 0.98 0.00 0.02
#> 60 1 0.2335 0.8854 0.92 0.00 0.06 0.02
#> 61 3 0.2706 0.7361 0.00 0.02 0.90 0.08
#> 62 4 0.3400 0.5560 0.00 0.18 0.00 0.82
#> 63 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 64 2 0.3172 0.7539 0.00 0.84 0.00 0.16
#> 65 2 0.0000 0.8433 0.00 1.00 0.00 0.00
#> 66 2 0.0000 0.8433 0.00 1.00 0.00 0.00
#> 67 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 68 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 69 1 0.0000 0.9436 1.00 0.00 0.00 0.00
#> 70 3 0.1637 0.7566 0.00 0.00 0.94 0.06
#> 71 2 0.0707 0.8473 0.00 0.98 0.00 0.02
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 70 3.02e-01 2
#> ATC:skmeans 68 3.41e-05 3
#> ATC:skmeans 59 1.85e-07 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node021. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0212"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9018 rows and 205 columns.
#> Top rows (902) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.975 0.989 0.502 0.498 0.498
#> 3 3 0.991 0.955 0.983 0.242 0.777 0.592
#> 4 4 0.691 0.734 0.841 0.166 0.848 0.618
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.980 0.00 1.00
#> 2 2 0.000 0.980 0.00 1.00
#> 3 2 0.000 0.980 0.00 1.00
#> 4 2 0.000 0.980 0.00 1.00
#> 5 2 0.000 0.980 0.00 1.00
#> 6 2 0.000 0.980 0.00 1.00
#> 7 2 0.000 0.980 0.00 1.00
#> 8 2 0.000 0.980 0.00 1.00
#> 9 2 0.000 0.980 0.00 1.00
#> 10 2 0.000 0.980 0.00 1.00
#> 11 2 0.000 0.980 0.00 1.00
#> 12 2 0.000 0.980 0.00 1.00
#> 13 2 0.402 0.907 0.08 0.92
#> 14 2 0.000 0.980 0.00 1.00
#> 15 2 0.000 0.980 0.00 1.00
#> 16 1 0.000 0.997 1.00 0.00
#> 17 2 0.000 0.980 0.00 1.00
#> 18 2 0.000 0.980 0.00 1.00
#> 19 2 0.000 0.980 0.00 1.00
#> 20 2 0.000 0.980 0.00 1.00
#> 21 2 0.000 0.980 0.00 1.00
#> 22 2 0.000 0.980 0.00 1.00
#> 23 2 0.000 0.980 0.00 1.00
#> 24 1 0.000 0.997 1.00 0.00
#> 25 1 0.000 0.997 1.00 0.00
#> 26 1 0.000 0.997 1.00 0.00
#> 27 1 0.000 0.997 1.00 0.00
#> 28 1 0.000 0.997 1.00 0.00
#> 29 2 0.990 0.242 0.44 0.56
#> 30 2 0.000 0.980 0.00 1.00
#> 31 2 0.000 0.980 0.00 1.00
#> 32 1 0.000 0.997 1.00 0.00
#> 33 1 0.000 0.997 1.00 0.00
#> 34 2 0.827 0.664 0.26 0.74
#> 35 1 0.000 0.997 1.00 0.00
#> 36 1 0.141 0.978 0.98 0.02
#> 37 2 0.000 0.980 0.00 1.00
#> 38 2 0.000 0.980 0.00 1.00
#> 39 2 0.000 0.980 0.00 1.00
#> 40 1 0.000 0.997 1.00 0.00
#> 41 1 0.000 0.997 1.00 0.00
#> 42 1 0.000 0.997 1.00 0.00
#> 43 2 0.000 0.980 0.00 1.00
#> 44 1 0.000 0.997 1.00 0.00
#> 45 1 0.000 0.997 1.00 0.00
#> 46 1 0.000 0.997 1.00 0.00
#> 47 1 0.000 0.997 1.00 0.00
#> 48 1 0.000 0.997 1.00 0.00
#> 49 2 0.000 0.980 0.00 1.00
#> 50 2 0.000 0.980 0.00 1.00
#> 51 2 0.000 0.980 0.00 1.00
#> 52 2 0.000 0.980 0.00 1.00
#> 53 1 0.242 0.958 0.96 0.04
#> 54 1 0.000 0.997 1.00 0.00
#> 55 1 0.000 0.997 1.00 0.00
#> 56 1 0.000 0.997 1.00 0.00
#> 57 1 0.327 0.936 0.94 0.06
#> 58 1 0.141 0.978 0.98 0.02
#> 59 2 0.000 0.980 0.00 1.00
#> 60 2 0.760 0.728 0.22 0.78
#> 61 2 0.000 0.980 0.00 1.00
#> 62 2 0.000 0.980 0.00 1.00
#> 63 2 0.795 0.695 0.24 0.76
#> 64 1 0.000 0.997 1.00 0.00
#> 65 1 0.000 0.997 1.00 0.00
#> 66 1 0.000 0.997 1.00 0.00
#> 67 1 0.000 0.997 1.00 0.00
#> 68 1 0.000 0.997 1.00 0.00
#> 69 2 0.000 0.980 0.00 1.00
#> 70 1 0.000 0.997 1.00 0.00
#> 71 1 0.000 0.997 1.00 0.00
#> 72 1 0.000 0.997 1.00 0.00
#> 73 1 0.000 0.997 1.00 0.00
#> 74 2 0.000 0.980 0.00 1.00
#> 75 1 0.000 0.997 1.00 0.00
#> 76 2 0.000 0.980 0.00 1.00
#> 77 1 0.000 0.997 1.00 0.00
#> 78 1 0.000 0.997 1.00 0.00
#> 79 1 0.000 0.997 1.00 0.00
#> 80 2 0.000 0.980 0.00 1.00
#> 81 1 0.000 0.997 1.00 0.00
#> 82 1 0.000 0.997 1.00 0.00
#> 83 2 0.000 0.980 0.00 1.00
#> 84 1 0.000 0.997 1.00 0.00
#> 85 1 0.000 0.997 1.00 0.00
#> 86 1 0.000 0.997 1.00 0.00
#> 87 1 0.000 0.997 1.00 0.00
#> 88 1 0.000 0.997 1.00 0.00
#> 89 2 0.000 0.980 0.00 1.00
#> 90 2 0.000 0.980 0.00 1.00
#> 91 1 0.000 0.997 1.00 0.00
#> 92 2 0.000 0.980 0.00 1.00
#> 93 1 0.000 0.997 1.00 0.00
#> 94 1 0.000 0.997 1.00 0.00
#> 95 2 0.000 0.980 0.00 1.00
#> 96 2 0.000 0.980 0.00 1.00
#> 97 2 0.000 0.980 0.00 1.00
#> 98 1 0.000 0.997 1.00 0.00
#> 99 1 0.000 0.997 1.00 0.00
#> 100 1 0.000 0.997 1.00 0.00
#> 101 1 0.000 0.997 1.00 0.00
#> 102 1 0.000 0.997 1.00 0.00
#> 103 1 0.000 0.997 1.00 0.00
#> 104 1 0.000 0.997 1.00 0.00
#> 105 2 0.141 0.964 0.02 0.98
#> 106 1 0.000 0.997 1.00 0.00
#> 107 1 0.000 0.997 1.00 0.00
#> 108 1 0.000 0.997 1.00 0.00
#> 109 1 0.000 0.997 1.00 0.00
#> 110 1 0.000 0.997 1.00 0.00
#> 111 2 0.855 0.626 0.28 0.72
#> 112 2 0.000 0.980 0.00 1.00
#> 113 1 0.000 0.997 1.00 0.00
#> 114 1 0.000 0.997 1.00 0.00
#> 115 1 0.000 0.997 1.00 0.00
#> 116 1 0.000 0.997 1.00 0.00
#> 117 1 0.242 0.958 0.96 0.04
#> 118 1 0.000 0.997 1.00 0.00
#> 119 1 0.000 0.997 1.00 0.00
#> 120 2 0.141 0.964 0.02 0.98
#> 121 1 0.000 0.997 1.00 0.00
#> 122 1 0.000 0.997 1.00 0.00
#> 123 1 0.000 0.997 1.00 0.00
#> 124 1 0.529 0.864 0.88 0.12
#> 125 2 0.000 0.980 0.00 1.00
#> 126 2 0.000 0.980 0.00 1.00
#> 127 2 0.000 0.980 0.00 1.00
#> 128 2 0.000 0.980 0.00 1.00
#> 129 1 0.000 0.997 1.00 0.00
#> 130 1 0.000 0.997 1.00 0.00
#> 131 2 0.000 0.980 0.00 1.00
#> 132 2 0.000 0.980 0.00 1.00
#> 133 2 0.000 0.980 0.00 1.00
#> 134 2 0.000 0.980 0.00 1.00
#> 135 2 0.000 0.980 0.00 1.00
#> 136 2 0.000 0.980 0.00 1.00
#> 137 2 0.000 0.980 0.00 1.00
#> 138 2 0.000 0.980 0.00 1.00
#> 139 2 0.000 0.980 0.00 1.00
#> 140 2 0.000 0.980 0.00 1.00
#> 141 2 0.000 0.980 0.00 1.00
#> 142 2 0.000 0.980 0.00 1.00
#> 143 2 0.000 0.980 0.00 1.00
#> 144 2 0.000 0.980 0.00 1.00
#> 145 2 0.000 0.980 0.00 1.00
#> 146 2 0.000 0.980 0.00 1.00
#> 147 2 0.000 0.980 0.00 1.00
#> 148 2 0.000 0.980 0.00 1.00
#> 149 2 0.000 0.980 0.00 1.00
#> 150 2 0.000 0.980 0.00 1.00
#> 151 2 0.000 0.980 0.00 1.00
#> 152 2 0.000 0.980 0.00 1.00
#> 153 2 0.000 0.980 0.00 1.00
#> 154 2 0.000 0.980 0.00 1.00
#> 155 2 0.000 0.980 0.00 1.00
#> 156 2 0.000 0.980 0.00 1.00
#> 157 1 0.000 0.997 1.00 0.00
#> 158 2 0.000 0.980 0.00 1.00
#> 159 2 0.000 0.980 0.00 1.00
#> 160 2 0.000 0.980 0.00 1.00
#> 161 1 0.000 0.997 1.00 0.00
#> 162 2 0.000 0.980 0.00 1.00
#> 163 2 0.000 0.980 0.00 1.00
#> 164 2 0.000 0.980 0.00 1.00
#> 165 2 0.000 0.980 0.00 1.00
#> 166 2 0.529 0.861 0.12 0.88
#> 167 1 0.141 0.978 0.98 0.02
#> 168 1 0.000 0.997 1.00 0.00
#> 169 2 0.000 0.980 0.00 1.00
#> 170 2 0.000 0.980 0.00 1.00
#> 171 2 0.000 0.980 0.00 1.00
#> 172 1 0.000 0.997 1.00 0.00
#> 173 1 0.000 0.997 1.00 0.00
#> 174 1 0.000 0.997 1.00 0.00
#> 175 2 0.000 0.980 0.00 1.00
#> 176 2 0.000 0.980 0.00 1.00
#> 177 2 0.000 0.980 0.00 1.00
#> 178 1 0.000 0.997 1.00 0.00
#> 179 2 0.000 0.980 0.00 1.00
#> 180 1 0.000 0.997 1.00 0.00
#> 181 1 0.000 0.997 1.00 0.00
#> 182 1 0.000 0.997 1.00 0.00
#> 183 1 0.000 0.997 1.00 0.00
#> 184 1 0.000 0.997 1.00 0.00
#> 185 1 0.000 0.997 1.00 0.00
#> 186 1 0.000 0.997 1.00 0.00
#> 187 1 0.000 0.997 1.00 0.00
#> 188 1 0.000 0.997 1.00 0.00
#> 189 1 0.000 0.997 1.00 0.00
#> 190 1 0.000 0.997 1.00 0.00
#> 191 1 0.000 0.997 1.00 0.00
#> 192 1 0.000 0.997 1.00 0.00
#> 193 1 0.000 0.997 1.00 0.00
#> 194 1 0.000 0.997 1.00 0.00
#> 195 1 0.000 0.997 1.00 0.00
#> 196 2 0.795 0.697 0.24 0.76
#> 197 1 0.000 0.997 1.00 0.00
#> 198 1 0.000 0.997 1.00 0.00
#> 199 1 0.000 0.997 1.00 0.00
#> 200 1 0.000 0.997 1.00 0.00
#> 201 2 0.141 0.964 0.02 0.98
#> 202 1 0.000 0.997 1.00 0.00
#> 203 1 0.000 0.997 1.00 0.00
#> 204 1 0.000 0.997 1.00 0.00
#> 205 1 0.000 0.997 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.982 0.00 0.00 1.00
#> 2 3 0.0000 0.982 0.00 0.00 1.00
#> 3 3 0.0000 0.982 0.00 0.00 1.00
#> 4 3 0.0000 0.982 0.00 0.00 1.00
#> 5 3 0.0000 0.982 0.00 0.00 1.00
#> 6 3 0.0000 0.982 0.00 0.00 1.00
#> 7 3 0.0000 0.982 0.00 0.00 1.00
#> 8 3 0.0000 0.982 0.00 0.00 1.00
#> 9 3 0.0000 0.982 0.00 0.00 1.00
#> 10 3 0.0000 0.982 0.00 0.00 1.00
#> 11 3 0.0000 0.982 0.00 0.00 1.00
#> 12 3 0.0000 0.982 0.00 0.00 1.00
#> 13 3 0.0000 0.982 0.00 0.00 1.00
#> 14 3 0.0000 0.982 0.00 0.00 1.00
#> 15 3 0.0000 0.982 0.00 0.00 1.00
#> 16 3 0.0000 0.982 0.00 0.00 1.00
#> 17 3 0.0000 0.982 0.00 0.00 1.00
#> 18 3 0.0000 0.982 0.00 0.00 1.00
#> 19 3 0.0000 0.982 0.00 0.00 1.00
#> 20 2 0.0000 0.973 0.00 1.00 0.00
#> 21 2 0.0000 0.973 0.00 1.00 0.00
#> 22 2 0.0000 0.973 0.00 1.00 0.00
#> 23 2 0.0000 0.973 0.00 1.00 0.00
#> 24 1 0.0000 0.985 1.00 0.00 0.00
#> 25 1 0.0000 0.985 1.00 0.00 0.00
#> 26 1 0.0000 0.985 1.00 0.00 0.00
#> 27 1 0.0000 0.985 1.00 0.00 0.00
#> 28 1 0.0892 0.967 0.98 0.00 0.02
#> 29 3 0.0892 0.965 0.02 0.00 0.98
#> 30 2 0.0000 0.973 0.00 1.00 0.00
#> 31 2 0.0000 0.973 0.00 1.00 0.00
#> 32 1 0.0000 0.985 1.00 0.00 0.00
#> 33 1 0.2066 0.927 0.94 0.00 0.06
#> 34 2 0.4002 0.786 0.16 0.84 0.00
#> 35 1 0.0000 0.985 1.00 0.00 0.00
#> 36 2 0.6280 0.166 0.46 0.54 0.00
#> 37 3 0.5706 0.528 0.00 0.32 0.68
#> 38 2 0.0000 0.973 0.00 1.00 0.00
#> 39 2 0.0000 0.973 0.00 1.00 0.00
#> 40 1 0.0000 0.985 1.00 0.00 0.00
#> 41 1 0.0000 0.985 1.00 0.00 0.00
#> 42 2 0.5706 0.543 0.32 0.68 0.00
#> 43 2 0.0000 0.973 0.00 1.00 0.00
#> 44 2 0.0892 0.952 0.02 0.98 0.00
#> 45 3 0.0000 0.982 0.00 0.00 1.00
#> 46 1 0.0000 0.985 1.00 0.00 0.00
#> 47 3 0.3686 0.833 0.14 0.00 0.86
#> 48 1 0.0000 0.985 1.00 0.00 0.00
#> 49 2 0.0000 0.973 0.00 1.00 0.00
#> 50 2 0.0000 0.973 0.00 1.00 0.00
#> 51 2 0.0000 0.973 0.00 1.00 0.00
#> 52 2 0.0000 0.973 0.00 1.00 0.00
#> 53 2 0.0000 0.973 0.00 1.00 0.00
#> 54 1 0.0000 0.985 1.00 0.00 0.00
#> 55 1 0.0000 0.985 1.00 0.00 0.00
#> 56 1 0.0000 0.985 1.00 0.00 0.00
#> 57 2 0.0000 0.973 0.00 1.00 0.00
#> 58 2 0.0000 0.973 0.00 1.00 0.00
#> 59 2 0.0000 0.973 0.00 1.00 0.00
#> 60 2 0.0000 0.973 0.00 1.00 0.00
#> 61 2 0.0000 0.973 0.00 1.00 0.00
#> 62 3 0.0000 0.982 0.00 0.00 1.00
#> 63 2 0.0000 0.973 0.00 1.00 0.00
#> 64 1 0.2537 0.896 0.92 0.08 0.00
#> 65 2 0.3340 0.838 0.12 0.88 0.00
#> 66 1 0.0000 0.985 1.00 0.00 0.00
#> 67 1 0.0000 0.985 1.00 0.00 0.00
#> 68 1 0.0000 0.985 1.00 0.00 0.00
#> 69 2 0.0000 0.973 0.00 1.00 0.00
#> 70 1 0.0000 0.985 1.00 0.00 0.00
#> 71 1 0.0000 0.985 1.00 0.00 0.00
#> 72 1 0.0000 0.985 1.00 0.00 0.00
#> 73 1 0.0000 0.985 1.00 0.00 0.00
#> 74 2 0.0000 0.973 0.00 1.00 0.00
#> 75 1 0.0000 0.985 1.00 0.00 0.00
#> 76 3 0.0000 0.982 0.00 0.00 1.00
#> 77 1 0.0000 0.985 1.00 0.00 0.00
#> 78 1 0.0000 0.985 1.00 0.00 0.00
#> 79 1 0.0000 0.985 1.00 0.00 0.00
#> 80 2 0.0000 0.973 0.00 1.00 0.00
#> 81 1 0.0000 0.985 1.00 0.00 0.00
#> 82 1 0.0000 0.985 1.00 0.00 0.00
#> 83 2 0.0000 0.973 0.00 1.00 0.00
#> 84 1 0.0000 0.985 1.00 0.00 0.00
#> 85 1 0.3340 0.844 0.88 0.12 0.00
#> 86 2 0.3686 0.814 0.14 0.86 0.00
#> 87 1 0.0000 0.985 1.00 0.00 0.00
#> 88 1 0.2066 0.920 0.94 0.06 0.00
#> 89 2 0.0000 0.973 0.00 1.00 0.00
#> 90 2 0.0000 0.973 0.00 1.00 0.00
#> 91 1 0.0000 0.985 1.00 0.00 0.00
#> 92 2 0.0000 0.973 0.00 1.00 0.00
#> 93 1 0.0000 0.985 1.00 0.00 0.00
#> 94 1 0.0000 0.985 1.00 0.00 0.00
#> 95 2 0.0000 0.973 0.00 1.00 0.00
#> 96 2 0.0000 0.973 0.00 1.00 0.00
#> 97 2 0.0000 0.973 0.00 1.00 0.00
#> 98 1 0.0000 0.985 1.00 0.00 0.00
#> 99 1 0.0000 0.985 1.00 0.00 0.00
#> 100 1 0.0000 0.985 1.00 0.00 0.00
#> 101 1 0.0000 0.985 1.00 0.00 0.00
#> 102 3 0.2537 0.906 0.08 0.00 0.92
#> 103 1 0.0000 0.985 1.00 0.00 0.00
#> 104 1 0.0000 0.985 1.00 0.00 0.00
#> 105 3 0.0000 0.982 0.00 0.00 1.00
#> 106 1 0.0000 0.985 1.00 0.00 0.00
#> 107 1 0.0000 0.985 1.00 0.00 0.00
#> 108 1 0.0000 0.985 1.00 0.00 0.00
#> 109 1 0.0000 0.985 1.00 0.00 0.00
#> 110 1 0.0000 0.985 1.00 0.00 0.00
#> 111 2 0.0000 0.973 0.00 1.00 0.00
#> 112 2 0.0000 0.973 0.00 1.00 0.00
#> 113 1 0.0000 0.985 1.00 0.00 0.00
#> 114 1 0.0000 0.985 1.00 0.00 0.00
#> 115 1 0.6280 0.126 0.54 0.46 0.00
#> 116 1 0.0000 0.985 1.00 0.00 0.00
#> 117 2 0.0000 0.973 0.00 1.00 0.00
#> 118 1 0.2066 0.920 0.94 0.06 0.00
#> 119 1 0.0000 0.985 1.00 0.00 0.00
#> 120 3 0.0000 0.982 0.00 0.00 1.00
#> 121 1 0.0000 0.985 1.00 0.00 0.00
#> 122 2 0.5216 0.646 0.26 0.74 0.00
#> 123 1 0.0000 0.985 1.00 0.00 0.00
#> 124 2 0.2066 0.909 0.06 0.94 0.00
#> 125 2 0.0000 0.973 0.00 1.00 0.00
#> 126 2 0.0000 0.973 0.00 1.00 0.00
#> 127 2 0.0000 0.973 0.00 1.00 0.00
#> 128 2 0.0000 0.973 0.00 1.00 0.00
#> 129 1 0.0000 0.985 1.00 0.00 0.00
#> 130 1 0.0000 0.985 1.00 0.00 0.00
#> 131 2 0.0000 0.973 0.00 1.00 0.00
#> 132 2 0.0000 0.973 0.00 1.00 0.00
#> 133 2 0.0000 0.973 0.00 1.00 0.00
#> 134 2 0.0000 0.973 0.00 1.00 0.00
#> 135 2 0.0000 0.973 0.00 1.00 0.00
#> 136 2 0.0000 0.973 0.00 1.00 0.00
#> 137 2 0.0000 0.973 0.00 1.00 0.00
#> 138 2 0.0000 0.973 0.00 1.00 0.00
#> 139 2 0.0000 0.973 0.00 1.00 0.00
#> 140 2 0.0000 0.973 0.00 1.00 0.00
#> 141 2 0.0000 0.973 0.00 1.00 0.00
#> 142 3 0.0892 0.964 0.00 0.02 0.98
#> 143 2 0.0000 0.973 0.00 1.00 0.00
#> 144 3 0.0000 0.982 0.00 0.00 1.00
#> 145 2 0.0000 0.973 0.00 1.00 0.00
#> 146 2 0.0000 0.973 0.00 1.00 0.00
#> 147 2 0.0000 0.973 0.00 1.00 0.00
#> 148 2 0.0000 0.973 0.00 1.00 0.00
#> 149 2 0.0000 0.973 0.00 1.00 0.00
#> 150 2 0.0000 0.973 0.00 1.00 0.00
#> 151 2 0.0000 0.973 0.00 1.00 0.00
#> 152 2 0.0000 0.973 0.00 1.00 0.00
#> 153 2 0.0000 0.973 0.00 1.00 0.00
#> 154 3 0.0000 0.982 0.00 0.00 1.00
#> 155 2 0.0000 0.973 0.00 1.00 0.00
#> 156 2 0.0000 0.973 0.00 1.00 0.00
#> 157 2 0.5835 0.499 0.34 0.66 0.00
#> 158 2 0.0000 0.973 0.00 1.00 0.00
#> 159 2 0.0000 0.973 0.00 1.00 0.00
#> 160 2 0.0000 0.973 0.00 1.00 0.00
#> 161 1 0.0000 0.985 1.00 0.00 0.00
#> 162 2 0.0000 0.973 0.00 1.00 0.00
#> 163 2 0.0000 0.973 0.00 1.00 0.00
#> 164 2 0.0000 0.973 0.00 1.00 0.00
#> 165 2 0.0000 0.973 0.00 1.00 0.00
#> 166 2 0.0000 0.973 0.00 1.00 0.00
#> 167 2 0.0000 0.973 0.00 1.00 0.00
#> 168 1 0.4796 0.701 0.78 0.22 0.00
#> 169 2 0.0000 0.973 0.00 1.00 0.00
#> 170 2 0.0000 0.973 0.00 1.00 0.00
#> 171 2 0.0000 0.973 0.00 1.00 0.00
#> 172 1 0.0000 0.985 1.00 0.00 0.00
#> 173 1 0.0000 0.985 1.00 0.00 0.00
#> 174 1 0.0000 0.985 1.00 0.00 0.00
#> 175 2 0.0000 0.973 0.00 1.00 0.00
#> 176 2 0.0000 0.973 0.00 1.00 0.00
#> 177 2 0.0000 0.973 0.00 1.00 0.00
#> 178 1 0.0000 0.985 1.00 0.00 0.00
#> 179 2 0.0000 0.973 0.00 1.00 0.00
#> 180 1 0.0892 0.964 0.98 0.02 0.00
#> 181 1 0.0000 0.985 1.00 0.00 0.00
#> 182 1 0.0000 0.985 1.00 0.00 0.00
#> 183 1 0.0000 0.985 1.00 0.00 0.00
#> 184 1 0.0000 0.985 1.00 0.00 0.00
#> 185 1 0.0000 0.985 1.00 0.00 0.00
#> 186 1 0.0000 0.985 1.00 0.00 0.00
#> 187 1 0.0000 0.985 1.00 0.00 0.00
#> 188 1 0.0000 0.985 1.00 0.00 0.00
#> 189 1 0.0000 0.985 1.00 0.00 0.00
#> 190 1 0.0000 0.985 1.00 0.00 0.00
#> 191 1 0.0000 0.985 1.00 0.00 0.00
#> 192 1 0.0000 0.985 1.00 0.00 0.00
#> 193 1 0.0000 0.985 1.00 0.00 0.00
#> 194 1 0.0000 0.985 1.00 0.00 0.00
#> 195 1 0.0000 0.985 1.00 0.00 0.00
#> 196 3 0.0000 0.982 0.00 0.00 1.00
#> 197 1 0.0000 0.985 1.00 0.00 0.00
#> 198 1 0.0000 0.985 1.00 0.00 0.00
#> 199 1 0.0000 0.985 1.00 0.00 0.00
#> 200 1 0.0000 0.985 1.00 0.00 0.00
#> 201 3 0.0000 0.982 0.00 0.00 1.00
#> 202 1 0.0000 0.985 1.00 0.00 0.00
#> 203 1 0.0000 0.985 1.00 0.00 0.00
#> 204 1 0.0000 0.985 1.00 0.00 0.00
#> 205 1 0.0000 0.985 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 2 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 3 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 4 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 5 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 6 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 7 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 8 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 9 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 10 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 11 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 12 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 13 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 14 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 15 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 16 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 17 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 18 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 19 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 20 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 21 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 22 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 23 2 0.3801 0.7804 0.00 0.78 0.00 0.22
#> 24 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 25 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 26 1 0.0707 0.8799 0.98 0.00 0.00 0.02
#> 27 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 28 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 29 1 0.7525 0.3144 0.58 0.08 0.06 0.28
#> 30 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 31 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 32 1 0.3610 0.6421 0.80 0.00 0.00 0.20
#> 33 1 0.1637 0.8358 0.94 0.00 0.06 0.00
#> 34 2 0.7485 0.2193 0.38 0.44 0.00 0.18
#> 35 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 36 4 0.4581 0.5343 0.12 0.08 0.00 0.80
#> 37 2 0.6617 0.6627 0.00 0.60 0.12 0.28
#> 38 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 39 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 40 1 0.2011 0.8403 0.92 0.00 0.00 0.08
#> 41 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 42 4 0.6933 0.4870 0.14 0.30 0.00 0.56
#> 43 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 44 4 0.4277 0.5527 0.00 0.28 0.00 0.72
#> 45 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 46 4 0.5661 0.6425 0.22 0.08 0.00 0.70
#> 47 3 0.4292 0.7627 0.08 0.00 0.82 0.10
#> 48 4 0.4713 0.5833 0.36 0.00 0.00 0.64
#> 49 2 0.3801 0.7804 0.00 0.78 0.00 0.22
#> 50 2 0.3975 0.7750 0.00 0.76 0.00 0.24
#> 51 2 0.3801 0.7804 0.00 0.78 0.00 0.22
#> 52 2 0.3172 0.7903 0.00 0.84 0.00 0.16
#> 53 4 0.5570 -0.3137 0.02 0.44 0.00 0.54
#> 54 4 0.4522 0.6093 0.32 0.00 0.00 0.68
#> 55 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 56 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 57 2 0.7357 0.5082 0.18 0.50 0.00 0.32
#> 58 4 0.4522 0.4898 0.00 0.32 0.00 0.68
#> 59 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 60 4 0.4948 0.2845 0.00 0.44 0.00 0.56
#> 61 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 62 3 0.0707 0.9516 0.00 0.02 0.98 0.00
#> 63 2 0.4994 0.1299 0.00 0.52 0.00 0.48
#> 64 4 0.7135 0.5815 0.24 0.20 0.00 0.56
#> 65 4 0.5271 0.4745 0.02 0.34 0.00 0.64
#> 66 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 67 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 68 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 69 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 70 4 0.5594 0.6455 0.10 0.18 0.00 0.72
#> 71 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 72 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 73 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 74 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 75 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 76 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 77 1 0.2011 0.8212 0.92 0.00 0.00 0.08
#> 78 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 79 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 80 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 81 4 0.5000 0.2984 0.50 0.00 0.00 0.50
#> 82 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 83 2 0.3610 0.7532 0.00 0.80 0.00 0.20
#> 84 1 0.1211 0.8570 0.96 0.00 0.00 0.04
#> 85 4 0.6611 0.1452 0.46 0.08 0.00 0.46
#> 86 4 0.4277 0.5527 0.00 0.28 0.00 0.72
#> 87 4 0.5327 0.6433 0.22 0.06 0.00 0.72
#> 88 1 0.3975 0.6189 0.76 0.00 0.00 0.24
#> 89 4 0.4855 0.3706 0.00 0.40 0.00 0.60
#> 90 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 91 1 0.4406 0.3466 0.70 0.00 0.00 0.30
#> 92 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 93 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 94 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 95 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 96 2 0.6370 0.6691 0.10 0.62 0.00 0.28
#> 97 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 98 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 99 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 100 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 101 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 102 3 0.5147 0.6534 0.06 0.00 0.74 0.20
#> 103 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 104 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 105 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 106 1 0.4713 0.1856 0.64 0.00 0.00 0.36
#> 107 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 108 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 109 4 0.5657 0.6474 0.12 0.16 0.00 0.72
#> 110 1 0.4406 0.4232 0.70 0.00 0.00 0.30
#> 111 2 0.5767 0.7129 0.06 0.66 0.00 0.28
#> 112 2 0.4522 0.4157 0.00 0.68 0.00 0.32
#> 113 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 114 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 115 1 0.7707 0.0639 0.44 0.24 0.00 0.32
#> 116 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 117 2 0.5256 0.5240 0.04 0.70 0.00 0.26
#> 118 4 0.3247 0.5342 0.06 0.06 0.00 0.88
#> 119 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 120 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 121 1 0.0707 0.8799 0.98 0.00 0.00 0.02
#> 122 4 0.5661 0.5439 0.08 0.22 0.00 0.70
#> 123 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 124 4 0.4277 0.5527 0.00 0.28 0.00 0.72
#> 125 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 126 2 0.3975 0.7759 0.00 0.76 0.00 0.24
#> 127 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 128 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 129 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 130 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 131 2 0.3172 0.7918 0.00 0.84 0.00 0.16
#> 132 2 0.3610 0.7850 0.00 0.80 0.00 0.20
#> 133 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 134 2 0.0707 0.7785 0.00 0.98 0.00 0.02
#> 135 2 0.0000 0.7815 0.00 1.00 0.00 0.00
#> 136 2 0.3400 0.7881 0.00 0.82 0.00 0.18
#> 137 2 0.3400 0.7881 0.00 0.82 0.00 0.18
#> 138 2 0.1637 0.7883 0.00 0.94 0.00 0.06
#> 139 2 0.0707 0.7785 0.00 0.98 0.00 0.02
#> 140 2 0.2647 0.7914 0.00 0.88 0.00 0.12
#> 141 2 0.4134 0.7678 0.00 0.74 0.00 0.26
#> 142 3 0.3400 0.7696 0.00 0.18 0.82 0.00
#> 143 2 0.3400 0.7881 0.00 0.82 0.00 0.18
#> 144 3 0.3972 0.8078 0.00 0.08 0.84 0.08
#> 145 2 0.2647 0.7914 0.00 0.88 0.00 0.12
#> 146 2 0.0707 0.7785 0.00 0.98 0.00 0.02
#> 147 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 148 2 0.3801 0.7804 0.00 0.78 0.00 0.22
#> 149 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 150 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 151 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 152 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 153 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 154 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 155 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 156 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 157 4 0.5486 0.6206 0.08 0.20 0.00 0.72
#> 158 2 0.3400 0.7896 0.00 0.82 0.00 0.18
#> 159 2 0.2647 0.7625 0.00 0.88 0.00 0.12
#> 160 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 161 1 0.4939 0.5689 0.74 0.04 0.00 0.22
#> 162 2 0.4134 0.7679 0.00 0.74 0.00 0.26
#> 163 2 0.3172 0.7903 0.00 0.84 0.00 0.16
#> 164 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 165 2 0.5767 0.7130 0.06 0.66 0.00 0.28
#> 166 2 0.4936 0.7464 0.02 0.70 0.00 0.28
#> 167 4 0.5000 -0.5383 0.00 0.50 0.00 0.50
#> 168 4 0.5956 0.5652 0.22 0.10 0.00 0.68
#> 169 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 170 2 0.4134 0.7678 0.00 0.74 0.00 0.26
#> 171 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 172 4 0.4713 0.5834 0.36 0.00 0.00 0.64
#> 173 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 174 4 0.4948 0.4096 0.44 0.00 0.00 0.56
#> 175 2 0.3400 0.7896 0.00 0.82 0.00 0.18
#> 176 2 0.4277 0.7599 0.00 0.72 0.00 0.28
#> 177 2 0.2345 0.7595 0.00 0.90 0.00 0.10
#> 178 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 179 2 0.3400 0.6742 0.00 0.82 0.00 0.18
#> 180 4 0.5327 0.6174 0.06 0.22 0.00 0.72
#> 181 4 0.4977 0.4217 0.46 0.00 0.00 0.54
#> 182 4 0.4522 0.6091 0.32 0.00 0.00 0.68
#> 183 4 0.4994 0.3693 0.48 0.00 0.00 0.52
#> 184 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 185 4 0.4790 0.5656 0.38 0.00 0.00 0.62
#> 186 1 0.1211 0.8619 0.96 0.00 0.00 0.04
#> 187 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 188 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 189 4 0.4977 0.4217 0.46 0.00 0.00 0.54
#> 190 4 0.4907 0.5054 0.42 0.00 0.00 0.58
#> 191 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 192 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 193 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 194 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 195 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 196 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 197 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 198 4 0.4855 0.5384 0.40 0.00 0.00 0.60
#> 199 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 200 1 0.2011 0.8403 0.92 0.00 0.00 0.08
#> 201 3 0.0000 0.9708 0.00 0.00 1.00 0.00
#> 202 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 203 1 0.2345 0.8230 0.90 0.00 0.00 0.10
#> 204 1 0.0000 0.8947 1.00 0.00 0.00 0.00
#> 205 1 0.0000 0.8947 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 204 8.37e-04 2
#> ATC:skmeans 202 2.62e-19 3
#> ATC:skmeans 184 2.27e-17 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["022"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9717 rows and 433 columns.
#> Top rows (972) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.973 0.989 0.4236 0.577 0.577
#> 3 3 0.989 0.953 0.981 0.5380 0.718 0.534
#> 4 4 0.942 0.914 0.965 0.0927 0.897 0.722
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.982 0.00 1.00
#> 2 2 0.000 0.982 0.00 1.00
#> 3 2 0.000 0.982 0.00 1.00
#> 4 2 0.000 0.982 0.00 1.00
#> 5 2 0.000 0.982 0.00 1.00
#> 6 2 0.000 0.982 0.00 1.00
#> 7 2 0.000 0.982 0.00 1.00
#> 8 2 0.000 0.982 0.00 1.00
#> 9 2 0.000 0.982 0.00 1.00
#> 10 2 0.000 0.982 0.00 1.00
#> 11 2 0.000 0.982 0.00 1.00
#> 12 2 0.000 0.982 0.00 1.00
#> 13 2 0.000 0.982 0.00 1.00
#> 14 2 0.000 0.982 0.00 1.00
#> 15 2 0.000 0.982 0.00 1.00
#> 16 2 0.000 0.982 0.00 1.00
#> 17 2 0.000 0.982 0.00 1.00
#> 18 2 0.000 0.982 0.00 1.00
#> 19 2 0.000 0.982 0.00 1.00
#> 20 2 0.000 0.982 0.00 1.00
#> 21 2 0.000 0.982 0.00 1.00
#> 22 2 0.000 0.982 0.00 1.00
#> 23 2 0.000 0.982 0.00 1.00
#> 24 2 0.000 0.982 0.00 1.00
#> 25 2 0.000 0.982 0.00 1.00
#> 26 2 0.000 0.982 0.00 1.00
#> 27 2 0.000 0.982 0.00 1.00
#> 28 2 0.000 0.982 0.00 1.00
#> 29 2 0.000 0.982 0.00 1.00
#> 30 2 0.000 0.982 0.00 1.00
#> 31 2 0.000 0.982 0.00 1.00
#> 32 2 0.000 0.982 0.00 1.00
#> 33 2 0.000 0.982 0.00 1.00
#> 34 2 0.000 0.982 0.00 1.00
#> 35 2 0.000 0.982 0.00 1.00
#> 36 2 0.000 0.982 0.00 1.00
#> 37 2 0.000 0.982 0.00 1.00
#> 38 2 0.000 0.982 0.00 1.00
#> 39 2 0.000 0.982 0.00 1.00
#> 40 2 0.000 0.982 0.00 1.00
#> 41 2 0.000 0.982 0.00 1.00
#> 42 2 0.000 0.982 0.00 1.00
#> 43 2 0.000 0.982 0.00 1.00
#> 44 2 0.000 0.982 0.00 1.00
#> 45 2 0.000 0.982 0.00 1.00
#> 46 2 0.000 0.982 0.00 1.00
#> 47 2 0.000 0.982 0.00 1.00
#> 48 2 0.000 0.982 0.00 1.00
#> 49 2 0.000 0.982 0.00 1.00
#> 50 2 0.000 0.982 0.00 1.00
#> 51 2 0.000 0.982 0.00 1.00
#> 52 2 0.000 0.982 0.00 1.00
#> 53 2 0.000 0.982 0.00 1.00
#> 54 2 0.000 0.982 0.00 1.00
#> 55 2 0.000 0.982 0.00 1.00
#> 56 2 0.000 0.982 0.00 1.00
#> 57 2 0.000 0.982 0.00 1.00
#> 58 2 0.000 0.982 0.00 1.00
#> 59 2 0.000 0.982 0.00 1.00
#> 60 2 0.000 0.982 0.00 1.00
#> 61 2 0.000 0.982 0.00 1.00
#> 62 2 0.000 0.982 0.00 1.00
#> 63 2 0.000 0.982 0.00 1.00
#> 64 2 0.000 0.982 0.00 1.00
#> 65 2 0.000 0.982 0.00 1.00
#> 66 2 0.000 0.982 0.00 1.00
#> 67 2 0.000 0.982 0.00 1.00
#> 68 2 0.000 0.982 0.00 1.00
#> 69 2 0.000 0.982 0.00 1.00
#> 70 2 0.000 0.982 0.00 1.00
#> 71 2 0.000 0.982 0.00 1.00
#> 72 2 0.000 0.982 0.00 1.00
#> 73 1 0.000 0.992 1.00 0.00
#> 74 1 0.000 0.992 1.00 0.00
#> 75 1 0.000 0.992 1.00 0.00
#> 76 2 0.000 0.982 0.00 1.00
#> 77 1 0.000 0.992 1.00 0.00
#> 78 1 0.000 0.992 1.00 0.00
#> 79 1 0.000 0.992 1.00 0.00
#> 80 1 0.000 0.992 1.00 0.00
#> 81 1 0.000 0.992 1.00 0.00
#> 82 2 0.000 0.982 0.00 1.00
#> 83 1 0.000 0.992 1.00 0.00
#> 84 1 0.000 0.992 1.00 0.00
#> 85 2 0.000 0.982 0.00 1.00
#> 86 1 0.000 0.992 1.00 0.00
#> 87 2 0.000 0.982 0.00 1.00
#> 88 1 0.000 0.992 1.00 0.00
#> 89 1 0.000 0.992 1.00 0.00
#> 90 1 0.000 0.992 1.00 0.00
#> 91 1 0.000 0.992 1.00 0.00
#> 92 2 0.000 0.982 0.00 1.00
#> 93 2 0.990 0.228 0.44 0.56
#> 94 1 0.000 0.992 1.00 0.00
#> 95 1 0.000 0.992 1.00 0.00
#> 96 1 0.000 0.992 1.00 0.00
#> 97 2 0.000 0.982 0.00 1.00
#> 98 2 0.000 0.982 0.00 1.00
#> 99 2 0.000 0.982 0.00 1.00
#> 100 2 0.000 0.982 0.00 1.00
#> 101 2 0.000 0.982 0.00 1.00
#> 102 1 0.000 0.992 1.00 0.00
#> 103 1 0.000 0.992 1.00 0.00
#> 104 1 0.000 0.992 1.00 0.00
#> 105 1 0.958 0.379 0.62 0.38
#> 106 1 0.000 0.992 1.00 0.00
#> 107 1 0.000 0.992 1.00 0.00
#> 108 1 0.000 0.992 1.00 0.00
#> 109 1 0.000 0.992 1.00 0.00
#> 110 2 0.904 0.537 0.32 0.68
#> 111 1 0.000 0.992 1.00 0.00
#> 112 2 0.000 0.982 0.00 1.00
#> 113 1 0.000 0.992 1.00 0.00
#> 114 1 0.760 0.715 0.78 0.22
#> 115 1 0.000 0.992 1.00 0.00
#> 116 2 0.000 0.982 0.00 1.00
#> 117 2 0.000 0.982 0.00 1.00
#> 118 1 0.000 0.992 1.00 0.00
#> 119 2 0.000 0.982 0.00 1.00
#> 120 1 0.000 0.992 1.00 0.00
#> 121 1 0.000 0.992 1.00 0.00
#> 122 1 0.000 0.992 1.00 0.00
#> 123 2 0.000 0.982 0.00 1.00
#> 124 1 0.000 0.992 1.00 0.00
#> 125 1 0.000 0.992 1.00 0.00
#> 126 1 0.000 0.992 1.00 0.00
#> 127 2 0.000 0.982 0.00 1.00
#> 128 1 0.000 0.992 1.00 0.00
#> 129 1 0.000 0.992 1.00 0.00
#> 130 2 0.000 0.982 0.00 1.00
#> 131 1 0.000 0.992 1.00 0.00
#> 132 2 0.000 0.982 0.00 1.00
#> 133 2 0.000 0.982 0.00 1.00
#> 134 1 0.000 0.992 1.00 0.00
#> 135 1 0.000 0.992 1.00 0.00
#> 136 1 0.000 0.992 1.00 0.00
#> 137 1 0.000 0.992 1.00 0.00
#> 138 1 0.000 0.992 1.00 0.00
#> 139 1 0.000 0.992 1.00 0.00
#> 140 1 0.000 0.992 1.00 0.00
#> 141 1 0.000 0.992 1.00 0.00
#> 142 1 0.000 0.992 1.00 0.00
#> 143 1 0.000 0.992 1.00 0.00
#> 144 1 0.000 0.992 1.00 0.00
#> 145 1 0.000 0.992 1.00 0.00
#> 146 1 0.000 0.992 1.00 0.00
#> 147 1 0.000 0.992 1.00 0.00
#> 148 2 0.000 0.982 0.00 1.00
#> 149 2 0.000 0.982 0.00 1.00
#> 150 1 0.000 0.992 1.00 0.00
#> 151 1 0.000 0.992 1.00 0.00
#> 152 2 0.000 0.982 0.00 1.00
#> 153 2 0.327 0.923 0.06 0.94
#> 154 1 0.000 0.992 1.00 0.00
#> 155 1 0.000 0.992 1.00 0.00
#> 156 1 0.000 0.992 1.00 0.00
#> 157 2 0.000 0.982 0.00 1.00
#> 158 1 0.000 0.992 1.00 0.00
#> 159 1 0.000 0.992 1.00 0.00
#> 160 1 0.000 0.992 1.00 0.00
#> 161 2 0.000 0.982 0.00 1.00
#> 162 1 0.827 0.647 0.74 0.26
#> 163 1 0.000 0.992 1.00 0.00
#> 164 1 0.000 0.992 1.00 0.00
#> 165 2 0.000 0.982 0.00 1.00
#> 166 1 0.000 0.992 1.00 0.00
#> 167 2 0.000 0.982 0.00 1.00
#> 168 1 0.000 0.992 1.00 0.00
#> 169 1 0.000 0.992 1.00 0.00
#> 170 1 0.000 0.992 1.00 0.00
#> 171 1 0.000 0.992 1.00 0.00
#> 172 1 0.000 0.992 1.00 0.00
#> 173 1 0.904 0.524 0.68 0.32
#> 174 1 0.000 0.992 1.00 0.00
#> 175 2 0.000 0.982 0.00 1.00
#> 176 1 0.000 0.992 1.00 0.00
#> 177 1 0.000 0.992 1.00 0.00
#> 178 1 0.000 0.992 1.00 0.00
#> 179 1 0.000 0.992 1.00 0.00
#> 180 1 0.000 0.992 1.00 0.00
#> 181 1 0.000 0.992 1.00 0.00
#> 182 1 0.000 0.992 1.00 0.00
#> 183 1 0.000 0.992 1.00 0.00
#> 184 1 0.000 0.992 1.00 0.00
#> 185 1 0.000 0.992 1.00 0.00
#> 186 2 0.000 0.982 0.00 1.00
#> 187 1 0.000 0.992 1.00 0.00
#> 188 1 0.000 0.992 1.00 0.00
#> 189 1 0.000 0.992 1.00 0.00
#> 190 1 0.000 0.992 1.00 0.00
#> 191 1 0.000 0.992 1.00 0.00
#> 192 1 0.000 0.992 1.00 0.00
#> 193 1 0.000 0.992 1.00 0.00
#> 194 1 0.000 0.992 1.00 0.00
#> 195 1 0.000 0.992 1.00 0.00
#> 196 1 0.000 0.992 1.00 0.00
#> 197 1 0.000 0.992 1.00 0.00
#> 198 2 0.000 0.982 0.00 1.00
#> 199 1 0.000 0.992 1.00 0.00
#> 200 1 0.000 0.992 1.00 0.00
#> 201 1 0.000 0.992 1.00 0.00
#> 202 1 0.000 0.992 1.00 0.00
#> 203 1 0.000 0.992 1.00 0.00
#> 204 1 0.000 0.992 1.00 0.00
#> 205 1 0.000 0.992 1.00 0.00
#> 206 1 0.000 0.992 1.00 0.00
#> 207 1 0.000 0.992 1.00 0.00
#> 208 1 0.000 0.992 1.00 0.00
#> 209 2 0.000 0.982 0.00 1.00
#> 210 1 0.000 0.992 1.00 0.00
#> 211 1 0.000 0.992 1.00 0.00
#> 212 1 0.760 0.716 0.78 0.22
#> 213 1 0.000 0.992 1.00 0.00
#> 214 1 0.000 0.992 1.00 0.00
#> 215 2 0.990 0.227 0.44 0.56
#> 216 1 0.000 0.992 1.00 0.00
#> 217 1 0.000 0.992 1.00 0.00
#> 218 2 0.000 0.982 0.00 1.00
#> 219 2 0.242 0.944 0.04 0.96
#> 220 1 0.000 0.992 1.00 0.00
#> 221 1 0.000 0.992 1.00 0.00
#> 222 1 0.000 0.992 1.00 0.00
#> 223 1 0.000 0.992 1.00 0.00
#> 224 1 0.000 0.992 1.00 0.00
#> 225 1 0.000 0.992 1.00 0.00
#> 226 1 0.958 0.380 0.62 0.38
#> 227 1 0.000 0.992 1.00 0.00
#> 228 1 0.000 0.992 1.00 0.00
#> 229 1 0.000 0.992 1.00 0.00
#> 230 2 0.795 0.686 0.24 0.76
#> 231 1 0.000 0.992 1.00 0.00
#> 232 2 0.000 0.982 0.00 1.00
#> 233 1 0.000 0.992 1.00 0.00
#> 234 1 0.000 0.992 1.00 0.00
#> 235 2 0.000 0.982 0.00 1.00
#> 236 1 0.000 0.992 1.00 0.00
#> 237 2 0.000 0.982 0.00 1.00
#> 238 1 0.000 0.992 1.00 0.00
#> 239 1 0.000 0.992 1.00 0.00
#> 240 1 0.000 0.992 1.00 0.00
#> 241 1 0.000 0.992 1.00 0.00
#> 242 1 0.000 0.992 1.00 0.00
#> 243 1 0.000 0.992 1.00 0.00
#> 244 1 0.000 0.992 1.00 0.00
#> 245 1 0.000 0.992 1.00 0.00
#> 246 1 0.000 0.992 1.00 0.00
#> 247 1 0.000 0.992 1.00 0.00
#> 248 2 0.000 0.982 0.00 1.00
#> 249 1 0.000 0.992 1.00 0.00
#> 250 1 0.000 0.992 1.00 0.00
#> 251 1 0.000 0.992 1.00 0.00
#> 252 1 0.000 0.992 1.00 0.00
#> 253 1 0.000 0.992 1.00 0.00
#> 254 1 0.000 0.992 1.00 0.00
#> 255 1 0.000 0.992 1.00 0.00
#> 256 1 0.000 0.992 1.00 0.00
#> 257 1 0.000 0.992 1.00 0.00
#> 258 1 0.000 0.992 1.00 0.00
#> 259 1 0.000 0.992 1.00 0.00
#> 260 1 0.000 0.992 1.00 0.00
#> 261 1 0.000 0.992 1.00 0.00
#> 262 1 0.000 0.992 1.00 0.00
#> 263 1 0.000 0.992 1.00 0.00
#> 264 1 0.000 0.992 1.00 0.00
#> 265 1 0.000 0.992 1.00 0.00
#> 266 1 0.000 0.992 1.00 0.00
#> 267 1 0.000 0.992 1.00 0.00
#> 268 1 0.000 0.992 1.00 0.00
#> 269 1 0.000 0.992 1.00 0.00
#> 270 1 0.000 0.992 1.00 0.00
#> 271 2 0.000 0.982 0.00 1.00
#> 272 1 0.000 0.992 1.00 0.00
#> 273 1 0.000 0.992 1.00 0.00
#> 274 1 0.000 0.992 1.00 0.00
#> 275 1 0.000 0.992 1.00 0.00
#> 276 1 0.000 0.992 1.00 0.00
#> 277 1 0.000 0.992 1.00 0.00
#> 278 1 0.000 0.992 1.00 0.00
#> 279 1 0.000 0.992 1.00 0.00
#> 280 1 0.000 0.992 1.00 0.00
#> 281 1 0.000 0.992 1.00 0.00
#> 282 2 0.000 0.982 0.00 1.00
#> 283 1 0.000 0.992 1.00 0.00
#> 284 1 0.000 0.992 1.00 0.00
#> 285 1 0.000 0.992 1.00 0.00
#> 286 1 0.000 0.992 1.00 0.00
#> 287 1 0.000 0.992 1.00 0.00
#> 288 1 0.000 0.992 1.00 0.00
#> 289 1 0.000 0.992 1.00 0.00
#> 290 1 0.000 0.992 1.00 0.00
#> 291 1 0.000 0.992 1.00 0.00
#> 292 1 0.000 0.992 1.00 0.00
#> 293 1 0.000 0.992 1.00 0.00
#> 294 2 0.000 0.982 0.00 1.00
#> 295 1 0.000 0.992 1.00 0.00
#> 296 1 0.000 0.992 1.00 0.00
#> 297 2 0.995 0.161 0.46 0.54
#> 298 2 0.000 0.982 0.00 1.00
#> 299 1 0.000 0.992 1.00 0.00
#> 300 1 0.000 0.992 1.00 0.00
#> 301 1 0.000 0.992 1.00 0.00
#> 302 1 0.000 0.992 1.00 0.00
#> 303 1 0.000 0.992 1.00 0.00
#> 304 1 0.000 0.992 1.00 0.00
#> 305 1 0.000 0.992 1.00 0.00
#> 306 1 0.000 0.992 1.00 0.00
#> 307 1 0.000 0.992 1.00 0.00
#> 308 1 0.000 0.992 1.00 0.00
#> 309 1 0.000 0.992 1.00 0.00
#> 310 1 0.000 0.992 1.00 0.00
#> 311 1 0.000 0.992 1.00 0.00
#> 312 1 0.000 0.992 1.00 0.00
#> 313 1 0.000 0.992 1.00 0.00
#> 314 1 0.000 0.992 1.00 0.00
#> 315 1 0.402 0.907 0.92 0.08
#> 316 1 0.000 0.992 1.00 0.00
#> 317 2 0.000 0.982 0.00 1.00
#> 318 1 0.000 0.992 1.00 0.00
#> 319 1 0.000 0.992 1.00 0.00
#> 320 1 0.000 0.992 1.00 0.00
#> 321 1 0.000 0.992 1.00 0.00
#> 322 1 0.000 0.992 1.00 0.00
#> 323 2 0.881 0.577 0.30 0.70
#> 324 1 0.000 0.992 1.00 0.00
#> 325 1 0.000 0.992 1.00 0.00
#> 326 1 0.000 0.992 1.00 0.00
#> 327 1 0.000 0.992 1.00 0.00
#> 328 1 0.000 0.992 1.00 0.00
#> 329 1 0.000 0.992 1.00 0.00
#> 330 1 0.000 0.992 1.00 0.00
#> 331 1 0.000 0.992 1.00 0.00
#> 332 1 0.000 0.992 1.00 0.00
#> 333 1 0.000 0.992 1.00 0.00
#> 334 1 0.000 0.992 1.00 0.00
#> 335 1 0.000 0.992 1.00 0.00
#> 336 1 0.000 0.992 1.00 0.00
#> 337 1 0.000 0.992 1.00 0.00
#> 338 1 0.000 0.992 1.00 0.00
#> 339 1 0.000 0.992 1.00 0.00
#> 340 2 0.000 0.982 0.00 1.00
#> 341 1 0.000 0.992 1.00 0.00
#> 342 1 0.000 0.992 1.00 0.00
#> 343 1 0.000 0.992 1.00 0.00
#> 344 1 0.000 0.992 1.00 0.00
#> 345 1 0.000 0.992 1.00 0.00
#> 346 1 0.000 0.992 1.00 0.00
#> 347 1 0.000 0.992 1.00 0.00
#> 348 1 0.000 0.992 1.00 0.00
#> 349 1 0.000 0.992 1.00 0.00
#> 350 1 0.000 0.992 1.00 0.00
#> 351 1 0.000 0.992 1.00 0.00
#> 352 1 0.000 0.992 1.00 0.00
#> 353 1 0.000 0.992 1.00 0.00
#> 354 1 0.000 0.992 1.00 0.00
#> 355 1 0.000 0.992 1.00 0.00
#> 356 1 0.000 0.992 1.00 0.00
#> 357 1 0.000 0.992 1.00 0.00
#> 358 1 0.000 0.992 1.00 0.00
#> 359 1 0.000 0.992 1.00 0.00
#> 360 1 0.000 0.992 1.00 0.00
#> 361 1 0.000 0.992 1.00 0.00
#> 362 1 0.000 0.992 1.00 0.00
#> 363 1 0.000 0.992 1.00 0.00
#> 364 1 0.000 0.992 1.00 0.00
#> 365 1 0.000 0.992 1.00 0.00
#> 366 1 0.000 0.992 1.00 0.00
#> 367 1 0.000 0.992 1.00 0.00
#> 368 1 0.000 0.992 1.00 0.00
#> 369 2 0.000 0.982 0.00 1.00
#> 370 1 0.000 0.992 1.00 0.00
#> 371 1 0.000 0.992 1.00 0.00
#> 372 1 0.795 0.682 0.76 0.24
#> 373 1 0.795 0.681 0.76 0.24
#> 374 2 0.000 0.982 0.00 1.00
#> 375 1 0.000 0.992 1.00 0.00
#> 376 1 0.000 0.992 1.00 0.00
#> 377 1 0.000 0.992 1.00 0.00
#> 378 1 0.000 0.992 1.00 0.00
#> 379 1 0.000 0.992 1.00 0.00
#> 380 1 0.000 0.992 1.00 0.00
#> 381 1 0.000 0.992 1.00 0.00
#> 382 1 0.000 0.992 1.00 0.00
#> 383 1 0.000 0.992 1.00 0.00
#> 384 1 0.000 0.992 1.00 0.00
#> 385 1 0.000 0.992 1.00 0.00
#> 386 1 0.000 0.992 1.00 0.00
#> 387 1 0.000 0.992 1.00 0.00
#> 388 1 0.000 0.992 1.00 0.00
#> 389 1 0.000 0.992 1.00 0.00
#> 390 1 0.000 0.992 1.00 0.00
#> 391 1 0.000 0.992 1.00 0.00
#> 392 2 0.000 0.982 0.00 1.00
#> 393 1 0.000 0.992 1.00 0.00
#> 394 1 0.000 0.992 1.00 0.00
#> 395 1 0.000 0.992 1.00 0.00
#> 396 1 0.000 0.992 1.00 0.00
#> 397 1 0.000 0.992 1.00 0.00
#> 398 1 0.000 0.992 1.00 0.00
#> 399 1 0.000 0.992 1.00 0.00
#> 400 1 0.000 0.992 1.00 0.00
#> 401 1 0.000 0.992 1.00 0.00
#> 402 1 0.000 0.992 1.00 0.00
#> 403 1 0.000 0.992 1.00 0.00
#> 404 1 0.000 0.992 1.00 0.00
#> 405 1 0.000 0.992 1.00 0.00
#> 406 1 0.000 0.992 1.00 0.00
#> 407 1 0.000 0.992 1.00 0.00
#> 408 2 0.000 0.982 0.00 1.00
#> 409 2 0.000 0.982 0.00 1.00
#> 410 2 0.000 0.982 0.00 1.00
#> 411 2 0.000 0.982 0.00 1.00
#> 412 2 0.000 0.982 0.00 1.00
#> 413 2 0.000 0.982 0.00 1.00
#> 414 2 0.000 0.982 0.00 1.00
#> 415 1 0.000 0.992 1.00 0.00
#> 416 1 0.000 0.992 1.00 0.00
#> 417 1 0.000 0.992 1.00 0.00
#> 418 1 0.000 0.992 1.00 0.00
#> 419 1 0.000 0.992 1.00 0.00
#> 420 1 0.000 0.992 1.00 0.00
#> 421 1 0.000 0.992 1.00 0.00
#> 422 1 0.000 0.992 1.00 0.00
#> 423 1 0.000 0.992 1.00 0.00
#> 424 1 0.000 0.992 1.00 0.00
#> 425 1 0.000 0.992 1.00 0.00
#> 426 1 0.000 0.992 1.00 0.00
#> 427 1 0.000 0.992 1.00 0.00
#> 428 1 0.000 0.992 1.00 0.00
#> 429 1 0.000 0.992 1.00 0.00
#> 430 1 0.000 0.992 1.00 0.00
#> 431 1 0.000 0.992 1.00 0.00
#> 432 1 0.000 0.992 1.00 0.00
#> 433 1 0.000 0.992 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 2 0.0000 0.995 0.00 1.00 0.00
#> 2 2 0.0000 0.995 0.00 1.00 0.00
#> 3 2 0.0000 0.995 0.00 1.00 0.00
#> 4 2 0.0000 0.995 0.00 1.00 0.00
#> 5 2 0.0000 0.995 0.00 1.00 0.00
#> 6 2 0.0000 0.995 0.00 1.00 0.00
#> 7 2 0.0000 0.995 0.00 1.00 0.00
#> 8 2 0.0000 0.995 0.00 1.00 0.00
#> 9 2 0.0000 0.995 0.00 1.00 0.00
#> 10 2 0.0000 0.995 0.00 1.00 0.00
#> 11 2 0.0000 0.995 0.00 1.00 0.00
#> 12 2 0.0000 0.995 0.00 1.00 0.00
#> 13 2 0.0000 0.995 0.00 1.00 0.00
#> 14 2 0.0000 0.995 0.00 1.00 0.00
#> 15 2 0.0000 0.995 0.00 1.00 0.00
#> 16 2 0.0000 0.995 0.00 1.00 0.00
#> 17 2 0.0000 0.995 0.00 1.00 0.00
#> 18 2 0.0000 0.995 0.00 1.00 0.00
#> 19 2 0.0000 0.995 0.00 1.00 0.00
#> 20 2 0.0000 0.995 0.00 1.00 0.00
#> 21 2 0.0000 0.995 0.00 1.00 0.00
#> 22 2 0.0000 0.995 0.00 1.00 0.00
#> 23 2 0.0000 0.995 0.00 1.00 0.00
#> 24 2 0.0000 0.995 0.00 1.00 0.00
#> 25 2 0.0000 0.995 0.00 1.00 0.00
#> 26 2 0.0000 0.995 0.00 1.00 0.00
#> 27 2 0.0000 0.995 0.00 1.00 0.00
#> 28 2 0.0000 0.995 0.00 1.00 0.00
#> 29 2 0.0000 0.995 0.00 1.00 0.00
#> 30 2 0.0000 0.995 0.00 1.00 0.00
#> 31 2 0.0000 0.995 0.00 1.00 0.00
#> 32 2 0.0000 0.995 0.00 1.00 0.00
#> 33 2 0.0000 0.995 0.00 1.00 0.00
#> 34 2 0.0000 0.995 0.00 1.00 0.00
#> 35 2 0.0000 0.995 0.00 1.00 0.00
#> 36 2 0.0000 0.995 0.00 1.00 0.00
#> 37 2 0.0000 0.995 0.00 1.00 0.00
#> 38 2 0.0000 0.995 0.00 1.00 0.00
#> 39 2 0.0000 0.995 0.00 1.00 0.00
#> 40 2 0.0000 0.995 0.00 1.00 0.00
#> 41 2 0.0000 0.995 0.00 1.00 0.00
#> 42 2 0.0000 0.995 0.00 1.00 0.00
#> 43 2 0.0000 0.995 0.00 1.00 0.00
#> 44 2 0.0000 0.995 0.00 1.00 0.00
#> 45 2 0.0000 0.995 0.00 1.00 0.00
#> 46 2 0.0000 0.995 0.00 1.00 0.00
#> 47 2 0.0000 0.995 0.00 1.00 0.00
#> 48 2 0.0000 0.995 0.00 1.00 0.00
#> 49 2 0.0000 0.995 0.00 1.00 0.00
#> 50 2 0.0000 0.995 0.00 1.00 0.00
#> 51 2 0.0000 0.995 0.00 1.00 0.00
#> 52 2 0.0000 0.995 0.00 1.00 0.00
#> 53 2 0.0000 0.995 0.00 1.00 0.00
#> 54 2 0.0000 0.995 0.00 1.00 0.00
#> 55 2 0.0000 0.995 0.00 1.00 0.00
#> 56 2 0.0000 0.995 0.00 1.00 0.00
#> 57 2 0.0000 0.995 0.00 1.00 0.00
#> 58 2 0.0000 0.995 0.00 1.00 0.00
#> 59 2 0.0000 0.995 0.00 1.00 0.00
#> 60 2 0.0000 0.995 0.00 1.00 0.00
#> 61 2 0.0000 0.995 0.00 1.00 0.00
#> 62 2 0.0000 0.995 0.00 1.00 0.00
#> 63 2 0.0000 0.995 0.00 1.00 0.00
#> 64 2 0.0000 0.995 0.00 1.00 0.00
#> 65 2 0.0000 0.995 0.00 1.00 0.00
#> 66 2 0.0000 0.995 0.00 1.00 0.00
#> 67 2 0.0000 0.995 0.00 1.00 0.00
#> 68 2 0.0000 0.995 0.00 1.00 0.00
#> 69 2 0.0000 0.995 0.00 1.00 0.00
#> 70 2 0.0000 0.995 0.00 1.00 0.00
#> 71 2 0.0000 0.995 0.00 1.00 0.00
#> 72 2 0.0000 0.995 0.00 1.00 0.00
#> 73 1 0.0000 0.978 1.00 0.00 0.00
#> 74 3 0.0000 0.971 0.00 0.00 1.00
#> 75 1 0.0000 0.978 1.00 0.00 0.00
#> 76 2 0.0000 0.995 0.00 1.00 0.00
#> 77 1 0.0000 0.978 1.00 0.00 0.00
#> 78 3 0.0000 0.971 0.00 0.00 1.00
#> 79 1 0.0000 0.978 1.00 0.00 0.00
#> 80 1 0.0000 0.978 1.00 0.00 0.00
#> 81 1 0.0000 0.978 1.00 0.00 0.00
#> 82 3 0.0000 0.971 0.00 0.00 1.00
#> 83 3 0.0000 0.971 0.00 0.00 1.00
#> 84 1 0.0000 0.978 1.00 0.00 0.00
#> 85 2 0.0000 0.995 0.00 1.00 0.00
#> 86 3 0.0000 0.971 0.00 0.00 1.00
#> 87 3 0.0000 0.971 0.00 0.00 1.00
#> 88 1 0.0000 0.978 1.00 0.00 0.00
#> 89 1 0.0000 0.978 1.00 0.00 0.00
#> 90 3 0.6244 0.220 0.44 0.00 0.56
#> 91 1 0.0000 0.978 1.00 0.00 0.00
#> 92 2 0.0000 0.995 0.00 1.00 0.00
#> 93 3 0.0000 0.971 0.00 0.00 1.00
#> 94 1 0.0000 0.978 1.00 0.00 0.00
#> 95 1 0.0000 0.978 1.00 0.00 0.00
#> 96 1 0.0000 0.978 1.00 0.00 0.00
#> 97 3 0.0892 0.954 0.00 0.02 0.98
#> 98 3 0.5016 0.677 0.00 0.24 0.76
#> 99 3 0.0000 0.971 0.00 0.00 1.00
#> 100 2 0.1529 0.955 0.00 0.96 0.04
#> 101 2 0.0000 0.995 0.00 1.00 0.00
#> 102 1 0.0000 0.978 1.00 0.00 0.00
#> 103 1 0.0000 0.978 1.00 0.00 0.00
#> 104 1 0.0892 0.960 0.98 0.00 0.02
#> 105 3 0.0000 0.971 0.00 0.00 1.00
#> 106 1 0.0000 0.978 1.00 0.00 0.00
#> 107 1 0.0000 0.978 1.00 0.00 0.00
#> 108 3 0.3340 0.854 0.12 0.00 0.88
#> 109 3 0.0000 0.971 0.00 0.00 1.00
#> 110 3 0.0000 0.971 0.00 0.00 1.00
#> 111 1 0.0000 0.978 1.00 0.00 0.00
#> 112 3 0.0000 0.971 0.00 0.00 1.00
#> 113 3 0.0892 0.954 0.02 0.00 0.98
#> 114 3 0.0000 0.971 0.00 0.00 1.00
#> 115 1 0.0000 0.978 1.00 0.00 0.00
#> 116 3 0.0000 0.971 0.00 0.00 1.00
#> 117 2 0.0000 0.995 0.00 1.00 0.00
#> 118 1 0.0000 0.978 1.00 0.00 0.00
#> 119 2 0.0000 0.995 0.00 1.00 0.00
#> 120 1 0.0000 0.978 1.00 0.00 0.00
#> 121 3 0.0000 0.971 0.00 0.00 1.00
#> 122 3 0.0000 0.971 0.00 0.00 1.00
#> 123 2 0.0000 0.995 0.00 1.00 0.00
#> 124 1 0.3340 0.854 0.88 0.00 0.12
#> 125 1 0.0000 0.978 1.00 0.00 0.00
#> 126 3 0.0000 0.971 0.00 0.00 1.00
#> 127 2 0.0000 0.995 0.00 1.00 0.00
#> 128 3 0.0000 0.971 0.00 0.00 1.00
#> 129 1 0.0000 0.978 1.00 0.00 0.00
#> 130 2 0.0000 0.995 0.00 1.00 0.00
#> 131 3 0.0000 0.971 0.00 0.00 1.00
#> 132 3 0.0000 0.971 0.00 0.00 1.00
#> 133 2 0.0000 0.995 0.00 1.00 0.00
#> 134 3 0.0000 0.971 0.00 0.00 1.00
#> 135 1 0.0000 0.978 1.00 0.00 0.00
#> 136 1 0.0000 0.978 1.00 0.00 0.00
#> 137 3 0.0892 0.954 0.02 0.00 0.98
#> 138 3 0.0000 0.971 0.00 0.00 1.00
#> 139 1 0.0000 0.978 1.00 0.00 0.00
#> 140 1 0.0000 0.978 1.00 0.00 0.00
#> 141 3 0.0000 0.971 0.00 0.00 1.00
#> 142 3 0.0000 0.971 0.00 0.00 1.00
#> 143 3 0.0000 0.971 0.00 0.00 1.00
#> 144 3 0.0000 0.971 0.00 0.00 1.00
#> 145 1 0.0000 0.978 1.00 0.00 0.00
#> 146 3 0.0000 0.971 0.00 0.00 1.00
#> 147 3 0.0000 0.971 0.00 0.00 1.00
#> 148 2 0.0000 0.995 0.00 1.00 0.00
#> 149 2 0.0000 0.995 0.00 1.00 0.00
#> 150 1 0.0000 0.978 1.00 0.00 0.00
#> 151 3 0.5397 0.617 0.28 0.00 0.72
#> 152 3 0.0000 0.971 0.00 0.00 1.00
#> 153 3 0.0000 0.971 0.00 0.00 1.00
#> 154 3 0.0000 0.971 0.00 0.00 1.00
#> 155 3 0.0000 0.971 0.00 0.00 1.00
#> 156 3 0.2066 0.917 0.06 0.00 0.94
#> 157 3 0.0000 0.971 0.00 0.00 1.00
#> 158 3 0.0000 0.971 0.00 0.00 1.00
#> 159 3 0.0000 0.971 0.00 0.00 1.00
#> 160 1 0.0000 0.978 1.00 0.00 0.00
#> 161 2 0.2066 0.930 0.06 0.94 0.00
#> 162 3 0.0000 0.971 0.00 0.00 1.00
#> 163 1 0.0000 0.978 1.00 0.00 0.00
#> 164 1 0.0000 0.978 1.00 0.00 0.00
#> 165 2 0.0000 0.995 0.00 1.00 0.00
#> 166 3 0.1529 0.937 0.04 0.00 0.96
#> 167 3 0.3340 0.850 0.00 0.12 0.88
#> 168 3 0.0000 0.971 0.00 0.00 1.00
#> 169 3 0.0000 0.971 0.00 0.00 1.00
#> 170 3 0.0000 0.971 0.00 0.00 1.00
#> 171 3 0.0000 0.971 0.00 0.00 1.00
#> 172 1 0.0000 0.978 1.00 0.00 0.00
#> 173 3 0.0000 0.971 0.00 0.00 1.00
#> 174 1 0.0000 0.978 1.00 0.00 0.00
#> 175 3 0.0000 0.971 0.00 0.00 1.00
#> 176 3 0.0000 0.971 0.00 0.00 1.00
#> 177 1 0.0000 0.978 1.00 0.00 0.00
#> 178 3 0.0000 0.971 0.00 0.00 1.00
#> 179 3 0.0000 0.971 0.00 0.00 1.00
#> 180 3 0.0000 0.971 0.00 0.00 1.00
#> 181 1 0.0000 0.978 1.00 0.00 0.00
#> 182 1 0.0000 0.978 1.00 0.00 0.00
#> 183 1 0.0000 0.978 1.00 0.00 0.00
#> 184 1 0.0000 0.978 1.00 0.00 0.00
#> 185 3 0.2537 0.896 0.08 0.00 0.92
#> 186 2 0.0000 0.995 0.00 1.00 0.00
#> 187 3 0.0000 0.971 0.00 0.00 1.00
#> 188 1 0.0000 0.978 1.00 0.00 0.00
#> 189 1 0.0000 0.978 1.00 0.00 0.00
#> 190 1 0.0000 0.978 1.00 0.00 0.00
#> 191 1 0.0000 0.978 1.00 0.00 0.00
#> 192 1 0.5560 0.570 0.70 0.00 0.30
#> 193 1 0.0000 0.978 1.00 0.00 0.00
#> 194 1 0.0000 0.978 1.00 0.00 0.00
#> 195 1 0.0000 0.978 1.00 0.00 0.00
#> 196 1 0.0000 0.978 1.00 0.00 0.00
#> 197 3 0.0000 0.971 0.00 0.00 1.00
#> 198 2 0.0000 0.995 0.00 1.00 0.00
#> 199 1 0.0000 0.978 1.00 0.00 0.00
#> 200 3 0.0000 0.971 0.00 0.00 1.00
#> 201 3 0.0000 0.971 0.00 0.00 1.00
#> 202 1 0.2066 0.921 0.94 0.00 0.06
#> 203 3 0.0000 0.971 0.00 0.00 1.00
#> 204 3 0.0000 0.971 0.00 0.00 1.00
#> 205 3 0.0000 0.971 0.00 0.00 1.00
#> 206 3 0.0000 0.971 0.00 0.00 1.00
#> 207 3 0.0000 0.971 0.00 0.00 1.00
#> 208 1 0.0000 0.978 1.00 0.00 0.00
#> 209 2 0.0000 0.995 0.00 1.00 0.00
#> 210 1 0.0000 0.978 1.00 0.00 0.00
#> 211 1 0.0000 0.978 1.00 0.00 0.00
#> 212 3 0.0000 0.971 0.00 0.00 1.00
#> 213 1 0.0000 0.978 1.00 0.00 0.00
#> 214 3 0.0000 0.971 0.00 0.00 1.00
#> 215 3 0.0000 0.971 0.00 0.00 1.00
#> 216 3 0.0000 0.971 0.00 0.00 1.00
#> 217 1 0.0000 0.978 1.00 0.00 0.00
#> 218 2 0.0000 0.995 0.00 1.00 0.00
#> 219 2 0.5560 0.568 0.30 0.70 0.00
#> 220 1 0.0000 0.978 1.00 0.00 0.00
#> 221 1 0.0000 0.978 1.00 0.00 0.00
#> 222 1 0.0000 0.978 1.00 0.00 0.00
#> 223 1 0.0000 0.978 1.00 0.00 0.00
#> 224 1 0.0000 0.978 1.00 0.00 0.00
#> 225 1 0.0000 0.978 1.00 0.00 0.00
#> 226 3 0.0000 0.971 0.00 0.00 1.00
#> 227 1 0.0000 0.978 1.00 0.00 0.00
#> 228 3 0.0000 0.971 0.00 0.00 1.00
#> 229 3 0.0892 0.954 0.02 0.00 0.98
#> 230 3 0.0000 0.971 0.00 0.00 1.00
#> 231 3 0.2066 0.917 0.06 0.00 0.94
#> 232 2 0.0000 0.995 0.00 1.00 0.00
#> 233 3 0.2066 0.917 0.06 0.00 0.94
#> 234 1 0.0000 0.978 1.00 0.00 0.00
#> 235 2 0.0000 0.995 0.00 1.00 0.00
#> 236 1 0.5835 0.485 0.66 0.00 0.34
#> 237 2 0.0000 0.995 0.00 1.00 0.00
#> 238 1 0.0000 0.978 1.00 0.00 0.00
#> 239 1 0.0000 0.978 1.00 0.00 0.00
#> 240 1 0.0000 0.978 1.00 0.00 0.00
#> 241 1 0.0000 0.978 1.00 0.00 0.00
#> 242 1 0.0000 0.978 1.00 0.00 0.00
#> 243 1 0.0000 0.978 1.00 0.00 0.00
#> 244 1 0.0000 0.978 1.00 0.00 0.00
#> 245 1 0.0000 0.978 1.00 0.00 0.00
#> 246 1 0.0000 0.978 1.00 0.00 0.00
#> 247 3 0.0000 0.971 0.00 0.00 1.00
#> 248 2 0.0000 0.995 0.00 1.00 0.00
#> 249 3 0.0892 0.954 0.02 0.00 0.98
#> 250 1 0.0000 0.978 1.00 0.00 0.00
#> 251 1 0.0000 0.978 1.00 0.00 0.00
#> 252 1 0.0000 0.978 1.00 0.00 0.00
#> 253 3 0.5216 0.653 0.26 0.00 0.74
#> 254 1 0.0000 0.978 1.00 0.00 0.00
#> 255 1 0.0000 0.978 1.00 0.00 0.00
#> 256 1 0.0000 0.978 1.00 0.00 0.00
#> 257 1 0.0000 0.978 1.00 0.00 0.00
#> 258 1 0.0000 0.978 1.00 0.00 0.00
#> 259 1 0.0000 0.978 1.00 0.00 0.00
#> 260 3 0.6280 0.153 0.46 0.00 0.54
#> 261 1 0.0000 0.978 1.00 0.00 0.00
#> 262 1 0.0000 0.978 1.00 0.00 0.00
#> 263 3 0.0000 0.971 0.00 0.00 1.00
#> 264 1 0.0000 0.978 1.00 0.00 0.00
#> 265 1 0.0000 0.978 1.00 0.00 0.00
#> 266 3 0.5397 0.616 0.28 0.00 0.72
#> 267 3 0.0000 0.971 0.00 0.00 1.00
#> 268 3 0.1529 0.937 0.04 0.00 0.96
#> 269 1 0.0000 0.978 1.00 0.00 0.00
#> 270 1 0.0000 0.978 1.00 0.00 0.00
#> 271 2 0.0000 0.995 0.00 1.00 0.00
#> 272 3 0.0000 0.971 0.00 0.00 1.00
#> 273 3 0.0000 0.971 0.00 0.00 1.00
#> 274 1 0.0000 0.978 1.00 0.00 0.00
#> 275 1 0.0000 0.978 1.00 0.00 0.00
#> 276 1 0.0000 0.978 1.00 0.00 0.00
#> 277 1 0.0000 0.978 1.00 0.00 0.00
#> 278 1 0.3686 0.829 0.86 0.00 0.14
#> 279 1 0.0000 0.978 1.00 0.00 0.00
#> 280 3 0.0000 0.971 0.00 0.00 1.00
#> 281 3 0.0000 0.971 0.00 0.00 1.00
#> 282 3 0.0000 0.971 0.00 0.00 1.00
#> 283 3 0.0000 0.971 0.00 0.00 1.00
#> 284 1 0.0000 0.978 1.00 0.00 0.00
#> 285 1 0.0000 0.978 1.00 0.00 0.00
#> 286 3 0.0000 0.971 0.00 0.00 1.00
#> 287 1 0.0000 0.978 1.00 0.00 0.00
#> 288 3 0.0000 0.971 0.00 0.00 1.00
#> 289 3 0.0000 0.971 0.00 0.00 1.00
#> 290 3 0.0892 0.954 0.02 0.00 0.98
#> 291 3 0.0000 0.971 0.00 0.00 1.00
#> 292 1 0.6244 0.214 0.56 0.00 0.44
#> 293 1 0.0000 0.978 1.00 0.00 0.00
#> 294 3 0.0000 0.971 0.00 0.00 1.00
#> 295 1 0.0000 0.978 1.00 0.00 0.00
#> 296 1 0.0000 0.978 1.00 0.00 0.00
#> 297 3 0.0000 0.971 0.00 0.00 1.00
#> 298 2 0.1529 0.955 0.00 0.96 0.04
#> 299 1 0.6244 0.215 0.56 0.00 0.44
#> 300 1 0.0000 0.978 1.00 0.00 0.00
#> 301 3 0.0000 0.971 0.00 0.00 1.00
#> 302 1 0.0000 0.978 1.00 0.00 0.00
#> 303 3 0.0000 0.971 0.00 0.00 1.00
#> 304 3 0.0000 0.971 0.00 0.00 1.00
#> 305 1 0.0000 0.978 1.00 0.00 0.00
#> 306 3 0.0000 0.971 0.00 0.00 1.00
#> 307 1 0.0000 0.978 1.00 0.00 0.00
#> 308 3 0.0000 0.971 0.00 0.00 1.00
#> 309 3 0.0000 0.971 0.00 0.00 1.00
#> 310 1 0.0000 0.978 1.00 0.00 0.00
#> 311 1 0.0000 0.978 1.00 0.00 0.00
#> 312 1 0.0000 0.978 1.00 0.00 0.00
#> 313 3 0.0000 0.971 0.00 0.00 1.00
#> 314 3 0.0000 0.971 0.00 0.00 1.00
#> 315 1 0.0892 0.959 0.98 0.02 0.00
#> 316 3 0.6280 0.150 0.46 0.00 0.54
#> 317 2 0.0000 0.995 0.00 1.00 0.00
#> 318 1 0.0000 0.978 1.00 0.00 0.00
#> 319 1 0.3340 0.853 0.88 0.00 0.12
#> 320 1 0.0000 0.978 1.00 0.00 0.00
#> 321 1 0.0000 0.978 1.00 0.00 0.00
#> 322 3 0.0000 0.971 0.00 0.00 1.00
#> 323 3 0.0000 0.971 0.00 0.00 1.00
#> 324 1 0.6045 0.388 0.62 0.00 0.38
#> 325 3 0.4291 0.781 0.18 0.00 0.82
#> 326 3 0.0000 0.971 0.00 0.00 1.00
#> 327 3 0.0000 0.971 0.00 0.00 1.00
#> 328 3 0.0000 0.971 0.00 0.00 1.00
#> 329 3 0.0000 0.971 0.00 0.00 1.00
#> 330 3 0.0000 0.971 0.00 0.00 1.00
#> 331 1 0.0000 0.978 1.00 0.00 0.00
#> 332 3 0.0000 0.971 0.00 0.00 1.00
#> 333 3 0.0000 0.971 0.00 0.00 1.00
#> 334 3 0.0000 0.971 0.00 0.00 1.00
#> 335 1 0.0000 0.978 1.00 0.00 0.00
#> 336 3 0.0000 0.971 0.00 0.00 1.00
#> 337 1 0.0000 0.978 1.00 0.00 0.00
#> 338 1 0.6244 0.213 0.56 0.00 0.44
#> 339 1 0.0000 0.978 1.00 0.00 0.00
#> 340 2 0.0892 0.974 0.02 0.98 0.00
#> 341 1 0.0000 0.978 1.00 0.00 0.00
#> 342 1 0.4002 0.802 0.84 0.00 0.16
#> 343 3 0.0000 0.971 0.00 0.00 1.00
#> 344 1 0.0000 0.978 1.00 0.00 0.00
#> 345 1 0.0000 0.978 1.00 0.00 0.00
#> 346 1 0.0000 0.978 1.00 0.00 0.00
#> 347 1 0.0000 0.978 1.00 0.00 0.00
#> 348 1 0.0000 0.978 1.00 0.00 0.00
#> 349 1 0.0000 0.978 1.00 0.00 0.00
#> 350 1 0.0000 0.978 1.00 0.00 0.00
#> 351 1 0.0000 0.978 1.00 0.00 0.00
#> 352 1 0.0000 0.978 1.00 0.00 0.00
#> 353 3 0.0000 0.971 0.00 0.00 1.00
#> 354 1 0.0000 0.978 1.00 0.00 0.00
#> 355 1 0.0000 0.978 1.00 0.00 0.00
#> 356 3 0.0000 0.971 0.00 0.00 1.00
#> 357 1 0.0000 0.978 1.00 0.00 0.00
#> 358 1 0.0000 0.978 1.00 0.00 0.00
#> 359 1 0.0000 0.978 1.00 0.00 0.00
#> 360 1 0.0000 0.978 1.00 0.00 0.00
#> 361 1 0.0000 0.978 1.00 0.00 0.00
#> 362 3 0.0000 0.971 0.00 0.00 1.00
#> 363 1 0.0000 0.978 1.00 0.00 0.00
#> 364 1 0.0000 0.978 1.00 0.00 0.00
#> 365 3 0.0000 0.971 0.00 0.00 1.00
#> 366 3 0.0000 0.971 0.00 0.00 1.00
#> 367 1 0.0000 0.978 1.00 0.00 0.00
#> 368 1 0.0000 0.978 1.00 0.00 0.00
#> 369 2 0.0000 0.995 0.00 1.00 0.00
#> 370 1 0.0000 0.978 1.00 0.00 0.00
#> 371 1 0.2959 0.876 0.90 0.00 0.10
#> 372 3 0.0000 0.971 0.00 0.00 1.00
#> 373 1 0.0000 0.978 1.00 0.00 0.00
#> 374 2 0.0000 0.995 0.00 1.00 0.00
#> 375 3 0.0000 0.971 0.00 0.00 1.00
#> 376 3 0.0000 0.971 0.00 0.00 1.00
#> 377 3 0.2066 0.917 0.06 0.00 0.94
#> 378 1 0.0000 0.978 1.00 0.00 0.00
#> 379 1 0.0000 0.978 1.00 0.00 0.00
#> 380 1 0.0000 0.978 1.00 0.00 0.00
#> 381 1 0.0000 0.978 1.00 0.00 0.00
#> 382 1 0.0000 0.978 1.00 0.00 0.00
#> 383 1 0.0000 0.978 1.00 0.00 0.00
#> 384 3 0.0000 0.971 0.00 0.00 1.00
#> 385 1 0.0892 0.960 0.98 0.00 0.02
#> 386 1 0.0892 0.960 0.98 0.00 0.02
#> 387 1 0.0000 0.978 1.00 0.00 0.00
#> 388 1 0.0000 0.978 1.00 0.00 0.00
#> 389 1 0.0000 0.978 1.00 0.00 0.00
#> 390 1 0.6302 0.066 0.52 0.00 0.48
#> 391 1 0.0000 0.978 1.00 0.00 0.00
#> 392 2 0.0000 0.995 0.00 1.00 0.00
#> 393 1 0.0000 0.978 1.00 0.00 0.00
#> 394 1 0.0000 0.978 1.00 0.00 0.00
#> 395 1 0.0000 0.978 1.00 0.00 0.00
#> 396 1 0.0000 0.978 1.00 0.00 0.00
#> 397 1 0.0000 0.978 1.00 0.00 0.00
#> 398 1 0.0000 0.978 1.00 0.00 0.00
#> 399 1 0.0000 0.978 1.00 0.00 0.00
#> 400 1 0.0000 0.978 1.00 0.00 0.00
#> 401 1 0.0000 0.978 1.00 0.00 0.00
#> 402 1 0.0000 0.978 1.00 0.00 0.00
#> 403 1 0.0000 0.978 1.00 0.00 0.00
#> 404 1 0.0000 0.978 1.00 0.00 0.00
#> 405 1 0.0000 0.978 1.00 0.00 0.00
#> 406 1 0.0892 0.960 0.98 0.00 0.02
#> 407 1 0.0000 0.978 1.00 0.00 0.00
#> 408 2 0.0000 0.995 0.00 1.00 0.00
#> 409 2 0.0000 0.995 0.00 1.00 0.00
#> 410 2 0.0000 0.995 0.00 1.00 0.00
#> 411 2 0.0000 0.995 0.00 1.00 0.00
#> 412 2 0.0000 0.995 0.00 1.00 0.00
#> 413 3 0.0000 0.971 0.00 0.00 1.00
#> 414 2 0.0000 0.995 0.00 1.00 0.00
#> 415 1 0.0000 0.978 1.00 0.00 0.00
#> 416 1 0.0000 0.978 1.00 0.00 0.00
#> 417 1 0.0000 0.978 1.00 0.00 0.00
#> 418 1 0.0000 0.978 1.00 0.00 0.00
#> 419 1 0.0000 0.978 1.00 0.00 0.00
#> 420 1 0.0000 0.978 1.00 0.00 0.00
#> 421 1 0.0000 0.978 1.00 0.00 0.00
#> 422 3 0.0000 0.971 0.00 0.00 1.00
#> 423 1 0.0000 0.978 1.00 0.00 0.00
#> 424 1 0.0000 0.978 1.00 0.00 0.00
#> 425 3 0.0000 0.971 0.00 0.00 1.00
#> 426 3 0.0000 0.971 0.00 0.00 1.00
#> 427 1 0.0000 0.978 1.00 0.00 0.00
#> 428 1 0.6244 0.212 0.56 0.00 0.44
#> 429 3 0.0000 0.971 0.00 0.00 1.00
#> 430 3 0.4002 0.807 0.16 0.00 0.84
#> 431 1 0.0000 0.978 1.00 0.00 0.00
#> 432 1 0.0000 0.978 1.00 0.00 0.00
#> 433 3 0.0000 0.971 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 2 2 0.2345 0.8857 0.00 0.90 0.00 0.10
#> 3 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 4 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 5 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 6 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 7 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 8 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 9 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 10 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 11 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 12 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 13 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 14 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 15 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 16 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 17 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 18 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 19 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 20 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 21 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 22 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 23 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 24 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 25 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 26 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 27 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 28 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 29 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 30 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 31 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 32 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 33 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 34 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 35 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 36 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 37 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 38 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 39 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 40 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 41 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 42 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 43 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 44 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 45 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 46 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 47 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 48 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 49 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 50 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 51 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 52 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 53 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 54 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 55 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 56 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 57 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 58 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 59 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 60 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 61 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 62 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 63 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 64 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 65 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 66 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 67 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 68 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 69 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 70 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 71 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 72 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 73 4 0.1637 0.8673 0.06 0.00 0.00 0.94
#> 74 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 75 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 76 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 77 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 78 3 0.1211 0.9039 0.00 0.00 0.96 0.04
#> 79 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 80 1 0.4522 0.5010 0.68 0.00 0.00 0.32
#> 81 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 82 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 83 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 84 4 0.4522 0.5486 0.32 0.00 0.00 0.68
#> 85 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 86 3 0.4977 0.1648 0.00 0.00 0.54 0.46
#> 87 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 88 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 89 4 0.4994 0.1223 0.48 0.00 0.00 0.52
#> 90 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 91 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 92 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 93 4 0.1211 0.8768 0.00 0.00 0.04 0.96
#> 94 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 95 1 0.0707 0.9576 0.98 0.00 0.00 0.02
#> 96 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 97 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 98 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 99 4 0.3610 0.7048 0.00 0.00 0.20 0.80
#> 100 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 101 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 102 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 103 4 0.0707 0.8925 0.02 0.00 0.00 0.98
#> 104 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 105 4 0.0707 0.8906 0.00 0.00 0.02 0.98
#> 106 4 0.0707 0.8925 0.02 0.00 0.00 0.98
#> 107 1 0.4134 0.6255 0.74 0.00 0.00 0.26
#> 108 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 109 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 110 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 111 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 112 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 113 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 114 4 0.0707 0.8902 0.00 0.00 0.02 0.98
#> 115 4 0.4790 0.4185 0.38 0.00 0.00 0.62
#> 116 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 117 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 118 4 0.0707 0.8925 0.02 0.00 0.00 0.98
#> 119 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 120 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 121 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 122 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 123 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 124 1 0.4406 0.5531 0.70 0.00 0.30 0.00
#> 125 1 0.3172 0.7798 0.84 0.00 0.16 0.00
#> 126 3 0.1211 0.9044 0.00 0.00 0.96 0.04
#> 127 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 128 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 129 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 130 2 0.0707 0.9748 0.00 0.98 0.00 0.02
#> 131 3 0.1211 0.9044 0.00 0.00 0.96 0.04
#> 132 4 0.5000 -0.0450 0.00 0.00 0.50 0.50
#> 133 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 134 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 135 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 136 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 137 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 138 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 139 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 140 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 141 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 142 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 143 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 144 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 145 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 146 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 147 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 148 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 149 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 150 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 151 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 152 4 0.3610 0.7052 0.00 0.00 0.20 0.80
#> 153 3 0.4948 0.2284 0.00 0.00 0.56 0.44
#> 154 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 155 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 156 3 0.1211 0.8934 0.04 0.00 0.96 0.00
#> 157 3 0.3400 0.7587 0.00 0.00 0.82 0.18
#> 158 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 159 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 160 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 161 2 0.1637 0.9102 0.06 0.94 0.00 0.00
#> 162 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 163 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 164 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 165 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 166 3 0.3335 0.7884 0.12 0.00 0.86 0.02
#> 167 3 0.3400 0.7278 0.00 0.18 0.82 0.00
#> 168 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 169 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 170 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 171 4 0.4713 0.4068 0.00 0.00 0.36 0.64
#> 172 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 173 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 174 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 175 3 0.3400 0.7612 0.00 0.00 0.82 0.18
#> 176 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 177 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 178 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 179 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 180 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 181 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 182 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 183 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 184 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 185 3 0.4079 0.7466 0.02 0.00 0.80 0.18
#> 186 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 187 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 188 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 189 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 190 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 191 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 192 3 0.4855 0.3687 0.40 0.00 0.60 0.00
#> 193 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 194 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 195 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 196 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 197 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 198 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 199 4 0.1211 0.8815 0.04 0.00 0.00 0.96
#> 200 3 0.3975 0.6728 0.00 0.00 0.76 0.24
#> 201 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 202 3 0.5000 0.0309 0.50 0.00 0.50 0.00
#> 203 4 0.2647 0.8058 0.00 0.00 0.12 0.88
#> 204 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 205 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 206 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 207 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 208 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 209 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 210 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 211 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 212 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 213 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 214 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 215 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 216 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 217 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 218 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 219 4 0.4553 0.7225 0.04 0.18 0.00 0.78
#> 220 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 221 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 222 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 223 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 224 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 225 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 226 3 0.4907 0.2944 0.00 0.00 0.58 0.42
#> 227 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 228 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 229 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 230 3 0.4406 0.5673 0.00 0.00 0.70 0.30
#> 231 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 232 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 233 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 234 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 235 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 236 3 0.4977 0.1797 0.46 0.00 0.54 0.00
#> 237 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 238 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 239 1 0.2011 0.8938 0.92 0.00 0.00 0.08
#> 240 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 241 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 242 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 243 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 244 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 245 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 246 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 247 3 0.1211 0.9044 0.00 0.00 0.96 0.04
#> 248 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 249 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 250 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 251 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 252 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 253 3 0.1211 0.8930 0.04 0.00 0.96 0.00
#> 254 1 0.0707 0.9564 0.98 0.00 0.02 0.00
#> 255 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 256 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 257 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 258 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 259 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 260 1 0.6336 -0.0310 0.48 0.00 0.46 0.06
#> 261 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 262 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 263 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 264 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 265 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 266 3 0.1637 0.8718 0.06 0.00 0.94 0.00
#> 267 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 268 3 0.0707 0.9114 0.02 0.00 0.98 0.00
#> 269 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 270 1 0.1211 0.9353 0.96 0.00 0.04 0.00
#> 271 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 272 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 273 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 274 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 275 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 276 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 277 1 0.0707 0.9564 0.98 0.00 0.02 0.00
#> 278 1 0.4855 0.3101 0.60 0.00 0.40 0.00
#> 279 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 280 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 281 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 282 3 0.3853 0.7677 0.00 0.02 0.82 0.16
#> 283 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 284 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 285 1 0.0707 0.9557 0.98 0.00 0.02 0.00
#> 286 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 287 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 288 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 289 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 290 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 291 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 292 3 0.3610 0.6902 0.20 0.00 0.80 0.00
#> 293 1 0.4790 0.3520 0.62 0.00 0.00 0.38
#> 294 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 295 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 296 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 297 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 298 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 299 3 0.4406 0.5333 0.30 0.00 0.70 0.00
#> 300 1 0.2011 0.8878 0.92 0.00 0.08 0.00
#> 301 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 302 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 303 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 304 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 305 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 306 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 307 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 308 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 309 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 310 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 311 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 312 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 313 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 314 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 315 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 316 3 0.3400 0.7199 0.18 0.00 0.82 0.00
#> 317 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 318 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 319 1 0.4624 0.4692 0.66 0.00 0.34 0.00
#> 320 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 321 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 322 3 0.0707 0.9156 0.00 0.00 0.98 0.02
#> 323 3 0.2011 0.8710 0.00 0.00 0.92 0.08
#> 324 3 0.4134 0.5974 0.26 0.00 0.74 0.00
#> 325 3 0.2921 0.7761 0.14 0.00 0.86 0.00
#> 326 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 327 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 328 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 329 3 0.1211 0.9044 0.00 0.00 0.96 0.04
#> 330 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 331 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 332 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 333 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 334 3 0.0707 0.9157 0.00 0.00 0.98 0.02
#> 335 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 336 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 337 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 338 3 0.4406 0.5356 0.30 0.00 0.70 0.00
#> 339 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 340 4 0.3610 0.7051 0.00 0.20 0.00 0.80
#> 341 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 342 3 0.4948 0.2445 0.44 0.00 0.56 0.00
#> 343 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 344 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 345 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 346 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 347 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 348 1 0.1637 0.9124 0.94 0.00 0.06 0.00
#> 349 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 350 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 351 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 352 1 0.1211 0.9337 0.96 0.00 0.04 0.00
#> 353 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 354 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 355 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 356 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 357 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 358 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 359 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 360 4 0.2647 0.8192 0.12 0.00 0.00 0.88
#> 361 1 0.0707 0.9564 0.98 0.00 0.02 0.00
#> 362 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 363 4 0.4713 0.4681 0.36 0.00 0.00 0.64
#> 364 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 365 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 366 3 0.1211 0.9044 0.00 0.00 0.96 0.04
#> 367 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 368 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 369 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 370 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 371 4 0.2335 0.8638 0.06 0.00 0.02 0.92
#> 372 4 0.0000 0.9011 0.00 0.00 0.00 1.00
#> 373 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 374 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 375 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 376 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 377 3 0.0707 0.9116 0.02 0.00 0.98 0.00
#> 378 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 379 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 380 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 381 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 382 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 383 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 384 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 385 1 0.1211 0.9337 0.96 0.00 0.04 0.00
#> 386 4 0.4088 0.7860 0.14 0.00 0.04 0.82
#> 387 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 388 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 389 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 390 3 0.2921 0.7770 0.14 0.00 0.86 0.00
#> 391 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 392 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 393 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 394 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 395 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 396 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 397 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 398 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 399 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 400 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 401 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 402 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 403 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 404 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 405 1 0.0707 0.9564 0.98 0.00 0.02 0.00
#> 406 1 0.3801 0.6888 0.78 0.00 0.22 0.00
#> 407 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 408 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 409 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 410 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 411 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 412 2 0.0000 0.9948 0.00 1.00 0.00 0.00
#> 413 3 0.0707 0.9166 0.00 0.00 0.98 0.02
#> 414 2 0.4406 0.5684 0.00 0.70 0.00 0.30
#> 415 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 416 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 417 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 418 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 419 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 420 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 421 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 422 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 423 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 424 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 425 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 426 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 427 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 428 3 0.2921 0.7766 0.14 0.00 0.86 0.00
#> 429 3 0.0000 0.9265 0.00 0.00 1.00 0.00
#> 430 3 0.1211 0.8934 0.04 0.00 0.96 0.00
#> 431 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 432 1 0.0000 0.9763 1.00 0.00 0.00 0.00
#> 433 3 0.0000 0.9265 0.00 0.00 1.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 428 5.86e-43 2
#> ATC:skmeans 423 2.43e-47 3
#> ATC:skmeans 417 4.84e-88 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node022. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0222"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7471 rows and 109 columns.
#> Top rows (747) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.978 0.991 0.499 0.501 0.501
#> 3 3 0.829 0.840 0.926 0.315 0.804 0.623
#> 4 4 0.682 0.753 0.855 0.126 0.850 0.600
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.988 0.00 1.00
#> 2 1 0.000 0.994 1.00 0.00
#> 3 1 0.000 0.994 1.00 0.00
#> 4 1 0.000 0.994 1.00 0.00
#> 5 1 0.000 0.994 1.00 0.00
#> 6 1 0.000 0.994 1.00 0.00
#> 7 1 0.000 0.994 1.00 0.00
#> 8 2 0.000 0.988 0.00 1.00
#> 9 1 0.000 0.994 1.00 0.00
#> 10 2 0.402 0.907 0.08 0.92
#> 11 2 0.000 0.988 0.00 1.00
#> 12 2 0.000 0.988 0.00 1.00
#> 13 2 0.000 0.988 0.00 1.00
#> 14 2 0.000 0.988 0.00 1.00
#> 15 2 0.000 0.988 0.00 1.00
#> 16 2 0.000 0.988 0.00 1.00
#> 17 2 0.000 0.988 0.00 1.00
#> 18 2 0.000 0.988 0.00 1.00
#> 19 2 0.000 0.988 0.00 1.00
#> 20 2 0.000 0.988 0.00 1.00
#> 21 2 0.000 0.988 0.00 1.00
#> 22 2 0.000 0.988 0.00 1.00
#> 23 2 0.000 0.988 0.00 1.00
#> 24 2 0.000 0.988 0.00 1.00
#> 25 2 0.000 0.988 0.00 1.00
#> 26 2 0.000 0.988 0.00 1.00
#> 27 2 0.000 0.988 0.00 1.00
#> 28 2 0.000 0.988 0.00 1.00
#> 29 2 0.000 0.988 0.00 1.00
#> 30 2 0.000 0.988 0.00 1.00
#> 31 2 0.000 0.988 0.00 1.00
#> 32 1 0.000 0.994 1.00 0.00
#> 33 2 0.000 0.988 0.00 1.00
#> 34 2 0.000 0.988 0.00 1.00
#> 35 2 0.000 0.988 0.00 1.00
#> 36 2 0.000 0.988 0.00 1.00
#> 37 2 0.000 0.988 0.00 1.00
#> 38 1 0.943 0.426 0.64 0.36
#> 39 2 0.000 0.988 0.00 1.00
#> 40 2 0.000 0.988 0.00 1.00
#> 41 2 0.000 0.988 0.00 1.00
#> 42 1 0.000 0.994 1.00 0.00
#> 43 1 0.000 0.994 1.00 0.00
#> 44 1 0.000 0.994 1.00 0.00
#> 45 2 0.000 0.988 0.00 1.00
#> 46 2 0.000 0.988 0.00 1.00
#> 47 2 0.000 0.988 0.00 1.00
#> 48 2 0.000 0.988 0.00 1.00
#> 49 2 0.000 0.988 0.00 1.00
#> 50 2 0.000 0.988 0.00 1.00
#> 51 2 0.958 0.387 0.38 0.62
#> 52 2 0.000 0.988 0.00 1.00
#> 53 1 0.000 0.994 1.00 0.00
#> 54 1 0.000 0.994 1.00 0.00
#> 55 1 0.000 0.994 1.00 0.00
#> 56 2 0.000 0.988 0.00 1.00
#> 57 2 0.000 0.988 0.00 1.00
#> 58 2 0.000 0.988 0.00 1.00
#> 59 2 0.000 0.988 0.00 1.00
#> 60 2 0.529 0.861 0.12 0.88
#> 61 1 0.000 0.994 1.00 0.00
#> 62 1 0.000 0.994 1.00 0.00
#> 63 1 0.000 0.994 1.00 0.00
#> 64 1 0.000 0.994 1.00 0.00
#> 65 1 0.000 0.994 1.00 0.00
#> 66 1 0.000 0.994 1.00 0.00
#> 67 1 0.000 0.994 1.00 0.00
#> 68 2 0.000 0.988 0.00 1.00
#> 69 1 0.000 0.994 1.00 0.00
#> 70 1 0.000 0.994 1.00 0.00
#> 71 1 0.000 0.994 1.00 0.00
#> 72 1 0.000 0.994 1.00 0.00
#> 73 1 0.000 0.994 1.00 0.00
#> 74 1 0.000 0.994 1.00 0.00
#> 75 2 0.000 0.988 0.00 1.00
#> 76 1 0.000 0.994 1.00 0.00
#> 77 1 0.000 0.994 1.00 0.00
#> 78 1 0.000 0.994 1.00 0.00
#> 79 1 0.000 0.994 1.00 0.00
#> 80 1 0.000 0.994 1.00 0.00
#> 81 1 0.000 0.994 1.00 0.00
#> 82 1 0.000 0.994 1.00 0.00
#> 83 1 0.000 0.994 1.00 0.00
#> 84 1 0.000 0.994 1.00 0.00
#> 85 1 0.000 0.994 1.00 0.00
#> 86 1 0.000 0.994 1.00 0.00
#> 87 1 0.000 0.994 1.00 0.00
#> 88 2 0.000 0.988 0.00 1.00
#> 89 1 0.000 0.994 1.00 0.00
#> 90 1 0.000 0.994 1.00 0.00
#> 91 1 0.000 0.994 1.00 0.00
#> 92 1 0.000 0.994 1.00 0.00
#> 93 1 0.000 0.994 1.00 0.00
#> 94 1 0.000 0.994 1.00 0.00
#> 95 1 0.000 0.994 1.00 0.00
#> 96 1 0.000 0.994 1.00 0.00
#> 97 1 0.000 0.994 1.00 0.00
#> 98 1 0.000 0.994 1.00 0.00
#> 99 1 0.000 0.994 1.00 0.00
#> 100 1 0.000 0.994 1.00 0.00
#> 101 1 0.000 0.994 1.00 0.00
#> 102 2 0.000 0.988 0.00 1.00
#> 103 1 0.000 0.994 1.00 0.00
#> 104 1 0.000 0.994 1.00 0.00
#> 105 1 0.000 0.994 1.00 0.00
#> 106 1 0.000 0.994 1.00 0.00
#> 107 1 0.000 0.994 1.00 0.00
#> 108 1 0.000 0.994 1.00 0.00
#> 109 1 0.000 0.994 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 2 0.0892 0.9537 0.00 0.98 0.02
#> 2 1 0.0000 0.9048 1.00 0.00 0.00
#> 3 1 0.5706 0.5335 0.68 0.00 0.32
#> 4 1 0.0000 0.9048 1.00 0.00 0.00
#> 5 3 0.6126 0.3259 0.40 0.00 0.60
#> 6 1 0.0892 0.8946 0.98 0.00 0.02
#> 7 1 0.0000 0.9048 1.00 0.00 0.00
#> 8 2 0.0892 0.9537 0.00 0.98 0.02
#> 9 1 0.0000 0.9048 1.00 0.00 0.00
#> 10 3 0.2066 0.8185 0.00 0.06 0.94
#> 11 3 0.6309 -0.1441 0.00 0.50 0.50
#> 12 2 0.0892 0.9537 0.00 0.98 0.02
#> 13 2 0.0892 0.9537 0.00 0.98 0.02
#> 14 2 0.0892 0.9537 0.00 0.98 0.02
#> 15 2 0.0892 0.9537 0.00 0.98 0.02
#> 16 2 0.2414 0.9246 0.04 0.94 0.02
#> 17 2 0.6244 0.2494 0.00 0.56 0.44
#> 18 2 0.0892 0.9537 0.00 0.98 0.02
#> 19 2 0.0892 0.9537 0.00 0.98 0.02
#> 20 2 0.0892 0.9537 0.00 0.98 0.02
#> 21 2 0.0892 0.9537 0.00 0.98 0.02
#> 22 2 0.0000 0.9579 0.00 1.00 0.00
#> 23 2 0.0892 0.9537 0.00 0.98 0.02
#> 24 2 0.4551 0.8146 0.14 0.84 0.02
#> 25 2 0.0892 0.9537 0.00 0.98 0.02
#> 26 2 0.2947 0.9047 0.06 0.92 0.02
#> 27 2 0.0892 0.9537 0.00 0.98 0.02
#> 28 2 0.0000 0.9579 0.00 1.00 0.00
#> 29 2 0.0000 0.9579 0.00 1.00 0.00
#> 30 2 0.0000 0.9579 0.00 1.00 0.00
#> 31 2 0.0000 0.9579 0.00 1.00 0.00
#> 32 3 0.4555 0.7024 0.20 0.00 0.80
#> 33 2 0.0000 0.9579 0.00 1.00 0.00
#> 34 2 0.0000 0.9579 0.00 1.00 0.00
#> 35 2 0.0000 0.9579 0.00 1.00 0.00
#> 36 2 0.0000 0.9579 0.00 1.00 0.00
#> 37 2 0.0000 0.9579 0.00 1.00 0.00
#> 38 1 0.1529 0.8747 0.96 0.04 0.00
#> 39 2 0.0000 0.9579 0.00 1.00 0.00
#> 40 2 0.0000 0.9579 0.00 1.00 0.00
#> 41 2 0.0000 0.9579 0.00 1.00 0.00
#> 42 1 0.0000 0.9048 1.00 0.00 0.00
#> 43 1 0.1529 0.8835 0.96 0.00 0.04
#> 44 1 0.0000 0.9048 1.00 0.00 0.00
#> 45 2 0.0000 0.9579 0.00 1.00 0.00
#> 46 2 0.0000 0.9579 0.00 1.00 0.00
#> 47 2 0.0000 0.9579 0.00 1.00 0.00
#> 48 2 0.0000 0.9579 0.00 1.00 0.00
#> 49 2 0.0000 0.9579 0.00 1.00 0.00
#> 50 2 0.0000 0.9579 0.00 1.00 0.00
#> 51 1 0.7277 0.5208 0.66 0.28 0.06
#> 52 2 0.2066 0.9132 0.00 0.94 0.06
#> 53 3 0.0892 0.8728 0.02 0.00 0.98
#> 54 1 0.0000 0.9048 1.00 0.00 0.00
#> 55 1 0.1529 0.8835 0.96 0.00 0.04
#> 56 2 0.0000 0.9579 0.00 1.00 0.00
#> 57 2 0.0000 0.9579 0.00 1.00 0.00
#> 58 2 0.0000 0.9579 0.00 1.00 0.00
#> 59 2 0.0000 0.9579 0.00 1.00 0.00
#> 60 1 0.2537 0.8370 0.92 0.08 0.00
#> 61 1 0.0000 0.9048 1.00 0.00 0.00
#> 62 1 0.0892 0.8945 0.98 0.00 0.02
#> 63 1 0.0892 0.8945 0.98 0.00 0.02
#> 64 1 0.0000 0.9048 1.00 0.00 0.00
#> 65 1 0.0000 0.9048 1.00 0.00 0.00
#> 66 3 0.1529 0.8698 0.04 0.00 0.96
#> 67 1 0.5397 0.6438 0.72 0.00 0.28
#> 68 2 0.5016 0.6835 0.00 0.76 0.24
#> 69 3 0.0892 0.8728 0.02 0.00 0.98
#> 70 1 0.0000 0.9048 1.00 0.00 0.00
#> 71 3 0.2066 0.8756 0.06 0.00 0.94
#> 72 3 0.2066 0.8756 0.06 0.00 0.94
#> 73 1 0.0000 0.9048 1.00 0.00 0.00
#> 74 3 0.2959 0.8509 0.10 0.00 0.90
#> 75 2 0.0000 0.9579 0.00 1.00 0.00
#> 76 3 0.2066 0.8756 0.06 0.00 0.94
#> 77 3 0.2066 0.8756 0.06 0.00 0.94
#> 78 3 0.1529 0.8766 0.04 0.00 0.96
#> 79 3 0.1529 0.8766 0.04 0.00 0.96
#> 80 3 0.0000 0.8602 0.00 0.00 1.00
#> 81 3 0.2537 0.8662 0.08 0.00 0.92
#> 82 3 0.1529 0.8766 0.04 0.00 0.96
#> 83 3 0.0892 0.8728 0.02 0.00 0.98
#> 84 1 0.4002 0.7959 0.84 0.00 0.16
#> 85 1 0.6192 0.3200 0.58 0.00 0.42
#> 86 1 0.0000 0.9048 1.00 0.00 0.00
#> 87 1 0.0000 0.9048 1.00 0.00 0.00
#> 88 2 0.6244 0.2332 0.00 0.56 0.44
#> 89 1 0.3686 0.8023 0.86 0.00 0.14
#> 90 1 0.0000 0.9048 1.00 0.00 0.00
#> 91 3 0.6192 0.2766 0.42 0.00 0.58
#> 92 1 0.5397 0.6134 0.72 0.00 0.28
#> 93 3 0.0892 0.8728 0.02 0.00 0.98
#> 94 3 0.6302 -0.0181 0.48 0.00 0.52
#> 95 3 0.2537 0.8663 0.08 0.00 0.92
#> 96 3 0.0892 0.8728 0.02 0.00 0.98
#> 97 1 0.5397 0.6391 0.72 0.00 0.28
#> 98 3 0.2959 0.8519 0.10 0.00 0.90
#> 99 1 0.5560 0.5815 0.70 0.00 0.30
#> 100 1 0.5560 0.5752 0.70 0.00 0.30
#> 101 1 0.0000 0.9048 1.00 0.00 0.00
#> 102 2 0.2959 0.8858 0.00 0.90 0.10
#> 103 1 0.0000 0.9048 1.00 0.00 0.00
#> 104 1 0.0000 0.9048 1.00 0.00 0.00
#> 105 1 0.0000 0.9048 1.00 0.00 0.00
#> 106 1 0.0000 0.9048 1.00 0.00 0.00
#> 107 1 0.0000 0.9048 1.00 0.00 0.00
#> 108 1 0.3340 0.8326 0.88 0.00 0.12
#> 109 3 0.2066 0.8756 0.06 0.00 0.94
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 4 0.0000 0.8099 0.00 0.00 0.00 1.00
#> 2 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 3 1 0.6808 0.4022 0.56 0.00 0.12 0.32
#> 4 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 5 1 0.7806 0.0626 0.42 0.02 0.14 0.42
#> 6 1 0.2335 0.8372 0.92 0.06 0.00 0.02
#> 7 1 0.1211 0.8457 0.96 0.00 0.00 0.04
#> 8 4 0.1637 0.8022 0.00 0.06 0.00 0.94
#> 9 1 0.0707 0.8539 0.98 0.00 0.00 0.02
#> 10 4 0.5106 0.5395 0.00 0.04 0.24 0.72
#> 11 4 0.5077 0.6851 0.00 0.08 0.16 0.76
#> 12 4 0.3975 0.6321 0.00 0.24 0.00 0.76
#> 13 4 0.2345 0.8303 0.00 0.10 0.00 0.90
#> 14 4 0.2345 0.8303 0.00 0.10 0.00 0.90
#> 15 4 0.2345 0.8303 0.00 0.10 0.00 0.90
#> 16 4 0.1411 0.8169 0.02 0.02 0.00 0.96
#> 17 4 0.2345 0.7400 0.00 0.00 0.10 0.90
#> 18 4 0.2345 0.8303 0.00 0.10 0.00 0.90
#> 19 4 0.2345 0.8303 0.00 0.10 0.00 0.90
#> 20 4 0.2011 0.8300 0.00 0.08 0.00 0.92
#> 21 4 0.2647 0.8152 0.00 0.12 0.00 0.88
#> 22 4 0.4977 0.1001 0.00 0.46 0.00 0.54
#> 23 4 0.2345 0.8303 0.00 0.10 0.00 0.90
#> 24 4 0.1913 0.7792 0.04 0.02 0.00 0.94
#> 25 4 0.2011 0.8300 0.00 0.08 0.00 0.92
#> 26 4 0.1411 0.7933 0.02 0.02 0.00 0.96
#> 27 4 0.2345 0.8303 0.00 0.10 0.00 0.90
#> 28 4 0.4977 0.1054 0.00 0.46 0.00 0.54
#> 29 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 30 4 0.4977 0.0471 0.00 0.46 0.00 0.54
#> 31 2 0.4134 0.7483 0.00 0.74 0.00 0.26
#> 32 1 0.8778 0.0480 0.42 0.10 0.36 0.12
#> 33 2 0.3172 0.8822 0.00 0.84 0.00 0.16
#> 34 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 35 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 36 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 37 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 38 1 0.4949 0.6723 0.76 0.18 0.00 0.06
#> 39 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 40 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 41 2 0.3400 0.8434 0.00 0.82 0.00 0.18
#> 42 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 43 1 0.3247 0.8053 0.88 0.06 0.06 0.00
#> 44 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 45 2 0.3400 0.8744 0.00 0.82 0.00 0.18
#> 46 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 47 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 48 2 0.2647 0.8910 0.00 0.88 0.00 0.12
#> 49 2 0.2647 0.8910 0.00 0.88 0.00 0.12
#> 50 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 51 2 0.2830 0.6987 0.04 0.90 0.06 0.00
#> 52 2 0.1637 0.7385 0.00 0.94 0.06 0.00
#> 53 3 0.3037 0.8188 0.02 0.10 0.88 0.00
#> 54 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 55 1 0.4731 0.7351 0.78 0.16 0.06 0.00
#> 56 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 57 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 58 2 0.2647 0.8910 0.00 0.88 0.00 0.12
#> 59 2 0.2921 0.8996 0.00 0.86 0.00 0.14
#> 60 2 0.6831 0.1067 0.42 0.48 0.00 0.10
#> 61 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 62 1 0.1411 0.8493 0.96 0.02 0.02 0.00
#> 63 1 0.3611 0.7918 0.86 0.08 0.06 0.00
#> 64 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 65 1 0.0707 0.8556 0.98 0.02 0.00 0.00
#> 66 3 0.3335 0.8106 0.02 0.12 0.86 0.00
#> 67 1 0.6110 0.5835 0.66 0.10 0.24 0.00
#> 68 2 0.2335 0.7479 0.00 0.92 0.06 0.02
#> 69 3 0.2647 0.8120 0.00 0.12 0.88 0.00
#> 70 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 71 3 0.1637 0.8654 0.06 0.00 0.94 0.00
#> 72 3 0.1637 0.8654 0.06 0.00 0.94 0.00
#> 73 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 74 3 0.1637 0.8654 0.06 0.00 0.94 0.00
#> 75 2 0.4277 0.7249 0.00 0.72 0.00 0.28
#> 76 3 0.1637 0.8654 0.06 0.00 0.94 0.00
#> 77 3 0.1637 0.8654 0.06 0.00 0.94 0.00
#> 78 3 0.1211 0.8626 0.04 0.00 0.96 0.00
#> 79 3 0.0707 0.8560 0.02 0.00 0.98 0.00
#> 80 3 0.6537 0.2857 0.02 0.04 0.54 0.40
#> 81 3 0.2011 0.8559 0.08 0.00 0.92 0.00
#> 82 3 0.1637 0.8654 0.06 0.00 0.94 0.00
#> 83 3 0.1637 0.8334 0.00 0.06 0.94 0.00
#> 84 1 0.4949 0.6910 0.76 0.06 0.18 0.00
#> 85 3 0.6649 0.3285 0.34 0.10 0.56 0.00
#> 86 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 87 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 88 2 0.2335 0.7479 0.00 0.92 0.06 0.02
#> 89 1 0.3975 0.6636 0.76 0.00 0.24 0.00
#> 90 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 91 3 0.4948 0.2060 0.44 0.00 0.56 0.00
#> 92 1 0.4624 0.4736 0.66 0.00 0.34 0.00
#> 93 3 0.0000 0.8460 0.00 0.00 1.00 0.00
#> 94 3 0.6976 0.4933 0.24 0.18 0.58 0.00
#> 95 3 0.2647 0.8281 0.12 0.00 0.88 0.00
#> 96 3 0.2345 0.8206 0.00 0.10 0.90 0.00
#> 97 1 0.4581 0.7471 0.80 0.08 0.12 0.00
#> 98 3 0.2706 0.8569 0.08 0.02 0.90 0.00
#> 99 1 0.4134 0.6253 0.74 0.00 0.26 0.00
#> 100 1 0.4790 0.3704 0.62 0.00 0.38 0.00
#> 101 1 0.2345 0.8099 0.90 0.00 0.10 0.00
#> 102 4 0.4610 0.7683 0.00 0.10 0.10 0.80
#> 103 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 104 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 105 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 106 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 107 1 0.0000 0.8616 1.00 0.00 0.00 0.00
#> 108 1 0.3725 0.7949 0.86 0.02 0.10 0.02
#> 109 3 0.1637 0.8654 0.06 0.00 0.94 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 107 4.90e-07 2
#> ATC:skmeans 102 1.37e-07 3
#> ATC:skmeans 96 1.70e-06 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["023"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 8383 rows and 141 columns.
#> Top rows (838) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.973 0.989 0.503 0.498 0.498
#> 3 3 0.947 0.919 0.968 0.307 0.749 0.539
#> 4 4 1.000 0.969 0.988 0.107 0.875 0.661
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.000 0.987 1.00 0.00
#> 2 1 0.000 0.987 1.00 0.00
#> 3 1 0.000 0.987 1.00 0.00
#> 4 1 0.000 0.987 1.00 0.00
#> 5 2 0.141 0.971 0.02 0.98
#> 6 2 0.327 0.932 0.06 0.94
#> 7 1 0.000 0.987 1.00 0.00
#> 8 1 0.000 0.987 1.00 0.00
#> 9 1 0.469 0.887 0.90 0.10
#> 10 1 0.000 0.987 1.00 0.00
#> 11 2 0.469 0.888 0.10 0.90
#> 12 2 0.000 0.990 0.00 1.00
#> 13 1 0.000 0.987 1.00 0.00
#> 14 1 0.000 0.987 1.00 0.00
#> 15 2 0.000 0.990 0.00 1.00
#> 16 2 0.000 0.990 0.00 1.00
#> 17 1 0.000 0.987 1.00 0.00
#> 18 2 0.000 0.990 0.00 1.00
#> 19 2 0.000 0.990 0.00 1.00
#> 20 1 0.000 0.987 1.00 0.00
#> 21 2 0.000 0.990 0.00 1.00
#> 22 2 0.000 0.990 0.00 1.00
#> 23 2 0.000 0.990 0.00 1.00
#> 24 2 0.000 0.990 0.00 1.00
#> 25 2 0.000 0.990 0.00 1.00
#> 26 1 0.000 0.987 1.00 0.00
#> 27 1 0.958 0.381 0.62 0.38
#> 28 2 0.000 0.990 0.00 1.00
#> 29 1 0.584 0.843 0.86 0.14
#> 30 2 0.000 0.990 0.00 1.00
#> 31 2 0.000 0.990 0.00 1.00
#> 32 2 0.000 0.990 0.00 1.00
#> 33 2 0.000 0.990 0.00 1.00
#> 34 2 0.000 0.990 0.00 1.00
#> 35 2 0.000 0.990 0.00 1.00
#> 36 2 0.000 0.990 0.00 1.00
#> 37 2 0.000 0.990 0.00 1.00
#> 38 1 0.000 0.987 1.00 0.00
#> 39 2 0.000 0.990 0.00 1.00
#> 40 2 0.000 0.990 0.00 1.00
#> 41 1 0.000 0.987 1.00 0.00
#> 42 2 0.000 0.990 0.00 1.00
#> 43 2 0.000 0.990 0.00 1.00
#> 44 2 0.000 0.990 0.00 1.00
#> 45 2 0.000 0.990 0.00 1.00
#> 46 2 0.000 0.990 0.00 1.00
#> 47 2 0.000 0.990 0.00 1.00
#> 48 1 0.000 0.987 1.00 0.00
#> 49 2 0.000 0.990 0.00 1.00
#> 50 1 0.000 0.987 1.00 0.00
#> 51 1 0.000 0.987 1.00 0.00
#> 52 1 0.000 0.987 1.00 0.00
#> 53 2 0.000 0.990 0.00 1.00
#> 54 2 0.000 0.990 0.00 1.00
#> 55 2 0.000 0.990 0.00 1.00
#> 56 1 0.000 0.987 1.00 0.00
#> 57 1 0.000 0.987 1.00 0.00
#> 58 1 0.000 0.987 1.00 0.00
#> 59 1 0.141 0.971 0.98 0.02
#> 60 2 0.000 0.990 0.00 1.00
#> 61 2 0.000 0.990 0.00 1.00
#> 62 1 0.000 0.987 1.00 0.00
#> 63 2 0.000 0.990 0.00 1.00
#> 64 2 0.000 0.990 0.00 1.00
#> 65 1 0.000 0.987 1.00 0.00
#> 66 2 0.000 0.990 0.00 1.00
#> 67 1 0.000 0.987 1.00 0.00
#> 68 1 0.141 0.971 0.98 0.02
#> 69 1 0.000 0.987 1.00 0.00
#> 70 1 0.000 0.987 1.00 0.00
#> 71 2 0.000 0.990 0.00 1.00
#> 72 2 0.000 0.990 0.00 1.00
#> 73 2 0.000 0.990 0.00 1.00
#> 74 2 0.000 0.990 0.00 1.00
#> 75 2 0.000 0.990 0.00 1.00
#> 76 1 0.000 0.987 1.00 0.00
#> 77 2 0.000 0.990 0.00 1.00
#> 78 1 0.141 0.971 0.98 0.02
#> 79 1 0.000 0.987 1.00 0.00
#> 80 2 0.000 0.990 0.00 1.00
#> 81 2 0.000 0.990 0.00 1.00
#> 82 1 0.000 0.987 1.00 0.00
#> 83 1 0.000 0.987 1.00 0.00
#> 84 1 0.000 0.987 1.00 0.00
#> 85 1 0.000 0.987 1.00 0.00
#> 86 1 0.000 0.987 1.00 0.00
#> 87 1 0.000 0.987 1.00 0.00
#> 88 1 0.000 0.987 1.00 0.00
#> 89 2 0.000 0.990 0.00 1.00
#> 90 2 0.327 0.932 0.06 0.94
#> 91 1 0.584 0.842 0.86 0.14
#> 92 2 0.000 0.990 0.00 1.00
#> 93 1 0.000 0.987 1.00 0.00
#> 94 1 0.000 0.987 1.00 0.00
#> 95 1 0.000 0.987 1.00 0.00
#> 96 1 0.000 0.987 1.00 0.00
#> 97 1 0.000 0.987 1.00 0.00
#> 98 1 0.000 0.987 1.00 0.00
#> 99 2 0.000 0.990 0.00 1.00
#> 100 2 0.000 0.990 0.00 1.00
#> 101 1 0.000 0.987 1.00 0.00
#> 102 2 0.000 0.990 0.00 1.00
#> 103 1 0.000 0.987 1.00 0.00
#> 104 2 0.000 0.990 0.00 1.00
#> 105 2 0.000 0.990 0.00 1.00
#> 106 1 0.000 0.987 1.00 0.00
#> 107 2 0.000 0.990 0.00 1.00
#> 108 2 0.000 0.990 0.00 1.00
#> 109 1 0.327 0.934 0.94 0.06
#> 110 1 0.000 0.987 1.00 0.00
#> 111 1 0.000 0.987 1.00 0.00
#> 112 1 0.000 0.987 1.00 0.00
#> 113 1 0.000 0.987 1.00 0.00
#> 114 2 0.000 0.990 0.00 1.00
#> 115 2 0.000 0.990 0.00 1.00
#> 116 1 0.000 0.987 1.00 0.00
#> 117 1 0.000 0.987 1.00 0.00
#> 118 1 0.000 0.987 1.00 0.00
#> 119 2 0.000 0.990 0.00 1.00
#> 120 2 0.000 0.990 0.00 1.00
#> 121 1 0.000 0.987 1.00 0.00
#> 122 1 0.000 0.987 1.00 0.00
#> 123 1 0.000 0.987 1.00 0.00
#> 124 1 0.000 0.987 1.00 0.00
#> 125 1 0.000 0.987 1.00 0.00
#> 126 2 0.000 0.990 0.00 1.00
#> 127 1 0.000 0.987 1.00 0.00
#> 128 2 0.000 0.990 0.00 1.00
#> 129 1 0.242 0.953 0.96 0.04
#> 130 1 0.000 0.987 1.00 0.00
#> 131 1 0.000 0.987 1.00 0.00
#> 132 2 0.000 0.990 0.00 1.00
#> 133 2 0.000 0.990 0.00 1.00
#> 134 1 0.000 0.987 1.00 0.00
#> 135 1 0.000 0.987 1.00 0.00
#> 136 2 0.000 0.990 0.00 1.00
#> 137 1 0.000 0.987 1.00 0.00
#> 138 1 0.000 0.987 1.00 0.00
#> 139 1 0.000 0.987 1.00 0.00
#> 140 2 0.990 0.212 0.44 0.56
#> 141 1 0.000 0.987 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 1 0.0000 0.9786 1.00 0.00 0.00
#> 2 1 0.1529 0.9463 0.96 0.00 0.04
#> 3 3 0.0000 0.9505 0.00 0.00 1.00
#> 4 1 0.1529 0.9463 0.96 0.00 0.04
#> 5 3 0.0000 0.9505 0.00 0.00 1.00
#> 6 3 0.0000 0.9505 0.00 0.00 1.00
#> 7 1 0.3686 0.8350 0.86 0.00 0.14
#> 8 3 0.0000 0.9505 0.00 0.00 1.00
#> 9 3 0.0000 0.9505 0.00 0.00 1.00
#> 10 1 0.5560 0.5735 0.70 0.00 0.30
#> 11 3 0.0000 0.9505 0.00 0.00 1.00
#> 12 3 0.0000 0.9505 0.00 0.00 1.00
#> 13 3 0.6045 0.3781 0.38 0.00 0.62
#> 14 1 0.2066 0.9274 0.94 0.00 0.06
#> 15 3 0.0000 0.9505 0.00 0.00 1.00
#> 16 3 0.0000 0.9505 0.00 0.00 1.00
#> 17 3 0.6192 0.2647 0.42 0.00 0.58
#> 18 3 0.0000 0.9505 0.00 0.00 1.00
#> 19 3 0.0000 0.9505 0.00 0.00 1.00
#> 20 1 0.0000 0.9786 1.00 0.00 0.00
#> 21 3 0.1529 0.9287 0.00 0.04 0.96
#> 22 3 0.1529 0.9287 0.00 0.04 0.96
#> 23 3 0.1529 0.9287 0.00 0.04 0.96
#> 24 3 0.1529 0.9287 0.00 0.04 0.96
#> 25 3 0.0000 0.9505 0.00 0.00 1.00
#> 26 1 0.0000 0.9786 1.00 0.00 0.00
#> 27 3 0.2537 0.8814 0.08 0.00 0.92
#> 28 3 0.1529 0.9287 0.00 0.04 0.96
#> 29 3 0.2066 0.9026 0.06 0.00 0.94
#> 30 3 0.1529 0.9287 0.00 0.04 0.96
#> 31 3 0.0000 0.9505 0.00 0.00 1.00
#> 32 3 0.0000 0.9505 0.00 0.00 1.00
#> 33 3 0.0000 0.9505 0.00 0.00 1.00
#> 34 3 0.0000 0.9505 0.00 0.00 1.00
#> 35 3 0.0000 0.9505 0.00 0.00 1.00
#> 36 3 0.0000 0.9505 0.00 0.00 1.00
#> 37 3 0.0000 0.9505 0.00 0.00 1.00
#> 38 1 0.0000 0.9786 1.00 0.00 0.00
#> 39 3 0.0000 0.9505 0.00 0.00 1.00
#> 40 3 0.0000 0.9505 0.00 0.00 1.00
#> 41 1 0.0000 0.9786 1.00 0.00 0.00
#> 42 3 0.0000 0.9505 0.00 0.00 1.00
#> 43 3 0.0000 0.9505 0.00 0.00 1.00
#> 44 3 0.0000 0.9505 0.00 0.00 1.00
#> 45 3 0.0000 0.9505 0.00 0.00 1.00
#> 46 2 0.0000 0.9554 0.00 1.00 0.00
#> 47 2 0.0000 0.9554 0.00 1.00 0.00
#> 48 1 0.0000 0.9786 1.00 0.00 0.00
#> 49 2 0.0000 0.9554 0.00 1.00 0.00
#> 50 1 0.0000 0.9786 1.00 0.00 0.00
#> 51 1 0.0000 0.9786 1.00 0.00 0.00
#> 52 1 0.0000 0.9786 1.00 0.00 0.00
#> 53 2 0.0000 0.9554 0.00 1.00 0.00
#> 54 2 0.0000 0.9554 0.00 1.00 0.00
#> 55 2 0.0000 0.9554 0.00 1.00 0.00
#> 56 1 0.0000 0.9786 1.00 0.00 0.00
#> 57 1 0.0000 0.9786 1.00 0.00 0.00
#> 58 1 0.0000 0.9786 1.00 0.00 0.00
#> 59 2 0.3340 0.8357 0.12 0.88 0.00
#> 60 3 0.1529 0.9287 0.00 0.04 0.96
#> 61 2 0.0000 0.9554 0.00 1.00 0.00
#> 62 1 0.0000 0.9786 1.00 0.00 0.00
#> 63 2 0.0000 0.9554 0.00 1.00 0.00
#> 64 2 0.2066 0.9008 0.00 0.94 0.06
#> 65 1 0.0000 0.9786 1.00 0.00 0.00
#> 66 2 0.0000 0.9554 0.00 1.00 0.00
#> 67 1 0.0000 0.9786 1.00 0.00 0.00
#> 68 1 0.1529 0.9449 0.96 0.04 0.00
#> 69 1 0.0892 0.9627 0.98 0.02 0.00
#> 70 1 0.4555 0.7460 0.80 0.20 0.00
#> 71 2 0.0000 0.9554 0.00 1.00 0.00
#> 72 3 0.1529 0.9287 0.00 0.04 0.96
#> 73 2 0.0000 0.9554 0.00 1.00 0.00
#> 74 3 0.0000 0.9505 0.00 0.00 1.00
#> 75 2 0.0000 0.9554 0.00 1.00 0.00
#> 76 1 0.0000 0.9786 1.00 0.00 0.00
#> 77 2 0.0000 0.9554 0.00 1.00 0.00
#> 78 2 0.0000 0.9554 0.00 1.00 0.00
#> 79 1 0.0000 0.9786 1.00 0.00 0.00
#> 80 2 0.0000 0.9554 0.00 1.00 0.00
#> 81 2 0.0000 0.9554 0.00 1.00 0.00
#> 82 1 0.0000 0.9786 1.00 0.00 0.00
#> 83 1 0.0000 0.9786 1.00 0.00 0.00
#> 84 1 0.0000 0.9786 1.00 0.00 0.00
#> 85 1 0.0000 0.9786 1.00 0.00 0.00
#> 86 1 0.0000 0.9786 1.00 0.00 0.00
#> 87 1 0.0000 0.9786 1.00 0.00 0.00
#> 88 1 0.0000 0.9786 1.00 0.00 0.00
#> 89 2 0.0000 0.9554 0.00 1.00 0.00
#> 90 2 0.4796 0.7045 0.00 0.78 0.22
#> 91 2 0.0000 0.9554 0.00 1.00 0.00
#> 92 2 0.0000 0.9554 0.00 1.00 0.00
#> 93 1 0.0000 0.9786 1.00 0.00 0.00
#> 94 1 0.5706 0.5190 0.68 0.32 0.00
#> 95 1 0.0000 0.9786 1.00 0.00 0.00
#> 96 1 0.1529 0.9450 0.96 0.00 0.04
#> 97 1 0.0000 0.9786 1.00 0.00 0.00
#> 98 1 0.0000 0.9786 1.00 0.00 0.00
#> 99 2 0.0000 0.9554 0.00 1.00 0.00
#> 100 2 0.0000 0.9554 0.00 1.00 0.00
#> 101 1 0.0000 0.9786 1.00 0.00 0.00
#> 102 2 0.0000 0.9554 0.00 1.00 0.00
#> 103 2 0.5216 0.6452 0.26 0.74 0.00
#> 104 2 0.0000 0.9554 0.00 1.00 0.00
#> 105 2 0.0000 0.9554 0.00 1.00 0.00
#> 106 1 0.0000 0.9786 1.00 0.00 0.00
#> 107 3 0.0000 0.9505 0.00 0.00 1.00
#> 108 2 0.0000 0.9554 0.00 1.00 0.00
#> 109 2 0.0000 0.9554 0.00 1.00 0.00
#> 110 1 0.0000 0.9786 1.00 0.00 0.00
#> 111 1 0.0000 0.9786 1.00 0.00 0.00
#> 112 1 0.0000 0.9786 1.00 0.00 0.00
#> 113 1 0.0000 0.9786 1.00 0.00 0.00
#> 114 2 0.6302 0.0630 0.00 0.52 0.48
#> 115 2 0.0000 0.9554 0.00 1.00 0.00
#> 116 1 0.0000 0.9786 1.00 0.00 0.00
#> 117 1 0.0000 0.9786 1.00 0.00 0.00
#> 118 1 0.0000 0.9786 1.00 0.00 0.00
#> 119 2 0.0000 0.9554 0.00 1.00 0.00
#> 120 2 0.0000 0.9554 0.00 1.00 0.00
#> 121 1 0.0000 0.9786 1.00 0.00 0.00
#> 122 2 0.6280 0.1497 0.46 0.54 0.00
#> 123 1 0.0000 0.9786 1.00 0.00 0.00
#> 124 1 0.0000 0.9786 1.00 0.00 0.00
#> 125 1 0.0000 0.9786 1.00 0.00 0.00
#> 126 2 0.0000 0.9554 0.00 1.00 0.00
#> 127 1 0.0000 0.9786 1.00 0.00 0.00
#> 128 2 0.0000 0.9554 0.00 1.00 0.00
#> 129 2 0.0000 0.9554 0.00 1.00 0.00
#> 130 1 0.0892 0.9625 0.98 0.02 0.00
#> 131 1 0.0000 0.9786 1.00 0.00 0.00
#> 132 3 0.6302 0.0766 0.00 0.48 0.52
#> 133 2 0.0000 0.9554 0.00 1.00 0.00
#> 134 1 0.0000 0.9786 1.00 0.00 0.00
#> 135 1 0.0000 0.9786 1.00 0.00 0.00
#> 136 2 0.0000 0.9554 0.00 1.00 0.00
#> 137 1 0.0000 0.9786 1.00 0.00 0.00
#> 138 1 0.0000 0.9786 1.00 0.00 0.00
#> 139 1 0.0000 0.9786 1.00 0.00 0.00
#> 140 2 0.0892 0.9368 0.02 0.98 0.00
#> 141 1 0.0000 0.9786 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 2 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 3 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 4 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 5 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 6 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 7 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 8 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 9 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 10 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 11 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 12 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 13 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 14 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 15 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 16 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 17 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 18 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 19 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 20 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 21 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 22 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 23 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 24 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 25 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 26 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 27 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 28 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 29 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 30 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 31 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 32 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 33 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 34 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 35 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 36 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 37 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 38 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 39 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 40 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 41 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 42 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 43 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 44 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 45 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 46 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 47 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 48 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 49 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 50 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 51 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 52 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 53 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 54 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 55 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 56 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 57 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 58 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 59 2 0.3172 0.7852 0.16 0.84 0.00 0.00
#> 60 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 61 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 62 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 63 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 64 3 0.0707 0.9589 0.00 0.02 0.98 0.00
#> 65 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 66 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 67 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 68 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 69 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 70 1 0.1637 0.9221 0.94 0.06 0.00 0.00
#> 71 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 72 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 73 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 74 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 75 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 76 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 77 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 78 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 79 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 80 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 81 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 82 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 83 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 84 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 85 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 86 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 87 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 88 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 89 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 90 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 91 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 92 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 93 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 94 1 0.1211 0.9427 0.96 0.04 0.00 0.00
#> 95 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 96 1 0.1637 0.9228 0.94 0.00 0.06 0.00
#> 97 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 98 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 99 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 100 2 0.0707 0.9735 0.00 0.98 0.02 0.00
#> 101 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 102 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 103 1 0.4994 0.0885 0.52 0.48 0.00 0.00
#> 104 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 105 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 106 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 107 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 108 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 109 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 110 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 111 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 112 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 113 4 0.0707 0.9750 0.02 0.00 0.00 0.98
#> 114 3 0.6605 0.0930 0.00 0.44 0.48 0.08
#> 115 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 116 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 117 1 0.0707 0.9636 0.98 0.00 0.00 0.02
#> 118 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 119 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 120 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 121 4 0.0000 0.9988 0.00 0.00 0.00 1.00
#> 122 1 0.4134 0.6507 0.74 0.26 0.00 0.00
#> 123 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 124 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 125 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 126 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 127 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 128 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 129 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 130 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 131 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 132 3 0.0000 0.9797 0.00 0.00 1.00 0.00
#> 133 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 134 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 135 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 136 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 137 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 138 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 139 1 0.0000 0.9812 1.00 0.00 0.00 0.00
#> 140 2 0.0000 0.9937 0.00 1.00 0.00 0.00
#> 141 1 0.0000 0.9812 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 139 5.26e-03 2
#> ATC:skmeans 136 2.51e-14 3
#> ATC:skmeans 139 5.84e-31 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013 , Node014 , Node021 , Node022 , Node023 , Node031 , Node032 , Node033 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["03"]
A summary of res and all the functions that can be applied to it:
res
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 10389 rows and 500 columns, randomly sampled from 648 columns.
#> Top rows (964) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.966 0.986 0.482 0.517 0.517
#> 3 3 1.000 0.955 0.972 0.232 0.846 0.715
#> 4 4 0.973 0.943 0.973 0.228 0.781 0.512
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
get_classes(res, k = 2)
#> class p
#> 1 2 0.000
#> 2 2 0.000
#> 3 2 0.000
#> 4 1 0.000
#> 5 2 0.000
#> 6 2 0.000
#> 7 2 0.000
#> 8 2 0.000
#> 9 2 0.000
#> 10 1 0.000
#> 11 2 0.000
#> 12 2 0.000
#> 13 2 0.000
#> 14 2 0.000
#> 15 1 0.000
#> 16 1 0.000
#> 17 1 0.000
#> 18 1 0.000
#> 19 1 0.000
#> 20 1 1.000
#> 21 1 0.000
#> 22 1 0.000
#> 23 2 0.000
#> 24 1 0.000
#> 25 2 0.000
#> 26 2 0.000
#> 27 1 0.000
#> 28 2 0.000
#> 29 2 0.751
#> 30 1 1.000
#> 31 1 0.000
#> 32 2 0.502
#> 33 2 0.253
#> 34 2 0.000
#> 35 1 0.000
#> 36 2 0.000
#> 37 2 0.000
#> 38 2 0.253
#> 39 2 0.000
#> 40 1 0.249
#> 41 1 0.000
#> 42 1 1.000
#> 43 2 1.000
#> 44 1 0.000
#> 45 2 0.751
#> 46 2 0.502
#> 47 2 1.000
#> 48 1 0.747
#> 49 1 0.000
#> 50 1 0.249
#> 51 1 0.000
#> 52 2 1.000
#> 53 1 0.000
#> 54 1 0.000
#> 55 1 0.000
#> 56 1 0.000
#> 57 1 0.000
#> 58 1 0.000
#> 59 1 0.000
#> 60 1 0.000
#> 61 1 0.498
#> 62 1 0.000
#> 63 2 1.000
#> 64 1 0.000
#> 65 2 1.000
#> 66 1 0.000
#> 67 1 0.000
#> 68 2 0.000
#> 69 1 0.000
#> 70 1 0.000
#> 71 1 0.000
#> 72 2 0.751
#> 73 1 0.000
#> 74 1 0.000
#> 75 1 0.000
#> 76 1 0.000
#> 77 1 0.000
#> 78 1 0.000
#> 79 1 0.000
#> 80 1 0.000
#> 81 1 0.000
#> 82 1 0.000
#> 83 2 0.502
#> 84 1 1.000
#> 85 2 0.253
#> 86 1 0.000
#> 87 1 0.000
#> 88 1 0.000
#> 89 1 0.000
#> 90 1 0.000
#> 91 2 0.751
#> 92 1 0.000
#> 93 1 1.000
#> 94 1 0.000
#> 95 1 0.000
#> 96 1 0.000
#> 97 1 0.000
#> 98 1 0.000
#> 99 1 0.000
#> 100 1 0.000
#> 101 1 0.000
#> 102 1 1.000
#> 103 1 0.000
#> 104 1 0.000
#> 105 1 0.000
#> 106 1 1.000
#> 107 1 0.000
#> 108 1 0.000
#> 109 1 0.000
#> 110 1 0.000
#> 111 1 0.000
#> 112 1 0.000
#> 113 1 0.000
#> 114 1 0.000
#> 115 1 0.000
#> 116 1 0.000
#> 117 1 0.000
#> 118 1 0.000
#> 119 1 0.000
#> 120 1 0.000
#> 121 1 0.498
#> 122 1 0.000
#> 123 1 0.000
#> 124 1 1.000
#> 125 1 0.000
#> 126 1 1.000
#> 127 1 0.000
#> 128 1 1.000
#> 129 1 0.000
#> 130 1 0.000
#> 131 1 1.000
#> 132 1 0.000
#> 133 1 0.000
#> 134 1 0.000
#> 135 2 0.502
#> 136 1 1.000
#> 137 1 0.000
#> 138 1 0.000
#> 139 1 0.000
#> 140 1 0.000
#> 141 1 0.000
#> 142 1 0.498
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 1 0.000
#> 147 1 0.000
#> 148 1 0.000
#> 149 1 0.000
#> 150 1 0.000
#> 151 1 0.000
#> 152 1 0.249
#> 153 1 0.000
#> 154 1 0.000
#> 155 1 0.000
#> 156 1 0.000
#> 157 1 0.000
#> 158 1 0.000
#> 159 1 0.000
#> 160 1 0.000
#> 161 1 0.000
#> 162 1 0.000
#> 163 1 0.000
#> 164 1 0.000
#> 165 1 1.000
#> 166 1 0.000
#> 167 1 0.000
#> 168 1 0.000
#> 169 1 0.000
#> 170 1 0.000
#> 171 1 0.000
#> 172 1 0.000
#> 173 1 1.000
#> 174 1 0.502
#> 175 1 0.751
#> 176 1 0.000
#> 177 1 0.000
#> 178 1 0.000
#> 179 1 0.000
#> 180 1 0.000
#> 181 1 0.000
#> 182 1 0.000
#> 183 1 0.000
#> 184 1 0.000
#> 185 1 0.000
#> 186 1 0.000
#> 187 1 0.000
#> 188 1 0.000
#> 189 1 0.000
#> 190 1 1.000
#> 191 1 0.000
#> 192 1 0.000
#> 193 1 0.249
#> 194 1 0.000
#> 195 1 0.000
#> 196 1 0.000
#> 197 1 0.000
#> 198 1 0.000
#> 199 1 0.000
#> 200 1 0.000
#> 201 1 0.000
#> 202 1 0.000
#> 203 1 0.000
#> 204 1 0.000
#> 205 1 0.000
#> 206 1 0.000
#> 207 1 0.000
#> 208 1 0.000
#> 209 1 0.000
#> 210 1 0.000
#> 211 1 0.000
#> 212 2 0.000
#> 213 1 1.000
#> 214 1 0.000
#> 215 1 1.000
#> 216 1 0.000
#> 217 1 0.000
#> 218 1 0.000
#> 219 1 0.000
#> 220 1 0.000
#> 221 1 0.000
#> 222 1 0.000
#> 223 1 0.000
#> 224 1 0.751
#> 225 1 0.000
#> 226 1 0.000
#> 227 1 0.000
#> 228 1 0.000
#> 229 1 0.000
#> 230 1 0.000
#> 231 1 0.000
#> 232 1 0.000
#> 233 1 0.000
#> 234 1 0.000
#> 235 1 0.000
#> 236 1 0.000
#> 237 1 0.000
#> 238 1 0.000
#> 239 1 0.000
#> 240 1 0.000
#> 241 1 0.000
#> 242 1 0.000
#> 243 1 0.000
#> 244 1 0.000
#> 245 1 0.000
#> 246 1 0.000
#> 247 1 0.000
#> 248 1 0.000
#> 249 1 0.000
#> 250 1 0.000
#> 251 1 0.000
#> 252 1 0.000
#> 253 1 0.000
#> 254 1 0.000
#> 255 1 0.000
#> 256 1 0.000
#> 257 1 0.000
#> 258 1 0.000
#> 259 1 0.000
#> 260 1 0.000
#> 261 1 0.000
#> 262 1 0.000
#> 263 1 0.000
#> 264 1 0.000
#> 265 1 0.000
#> 266 1 0.000
#> 267 1 0.000
#> 268 1 0.000
#> 269 1 0.000
#> 270 1 0.000
#> 271 1 0.000
#> 272 1 0.000
#> 273 1 0.000
#> 274 1 0.000
#> 275 1 0.000
#> 276 1 0.000
#> 277 1 0.000
#> 278 1 0.000
#> 279 1 0.000
#> 280 1 0.000
#> 281 1 0.000
#> 282 1 0.000
#> 283 1 0.000
#> 284 1 0.000
#> 285 1 0.000
#> 286 1 0.000
#> 287 1 0.000
#> 288 1 0.751
#> 289 2 0.000
#> 290 2 0.000
#> 291 1 0.000
#> 292 2 0.000
#> 293 1 0.000
#> 294 1 0.000
#> 295 1 0.502
#> 296 1 1.000
#> 297 1 0.000
#> 298 1 0.000
#> 299 1 0.000
#> 300 2 0.000
#> 301 1 0.000
#> 302 1 0.000
#> 303 1 0.000
#> 304 1 0.000
#> 305 1 0.000
#> 306 1 1.000
#> 307 1 0.000
#> 308 1 0.000
#> 309 1 0.000
#> 310 1 0.000
#> 311 1 0.000
#> 312 1 0.000
#> 313 1 0.000
#> 314 1 0.000
#> 315 1 0.000
#> 316 1 0.000
#> 317 1 0.000
#> 318 1 0.000
#> 319 1 0.000
#> 320 1 0.000
#> 321 1 0.000
#> 322 1 0.000
#> 323 1 0.000
#> 324 1 0.000
#> 325 1 0.000
#> 326 1 0.000
#> 327 1 0.000
#> 328 1 0.000
#> 329 1 0.000
#> 330 1 0.000
#> 331 1 0.000
#> 332 1 0.000
#> 333 1 0.000
#> 334 1 0.000
#> 335 1 0.000
#> 336 1 0.000
#> 337 1 0.000
#> 338 1 0.000
#> 339 1 0.249
#> 340 1 0.000
#> 341 1 0.000
#> 342 1 0.000
#> 343 1 0.000
#> 344 1 0.000
#> 345 1 0.000
#> 346 1 0.000
#> 347 1 0.000
#> 348 1 0.000
#> 349 1 0.000
#> 350 1 0.000
#> 351 1 0.000
#> 352 1 0.000
#> 353 1 0.000
#> 354 2 0.751
#> 355 2 1.000
#> 356 1 0.000
#> 357 1 0.000
#> 358 1 1.000
#> 359 1 0.000
#> 360 1 0.000
#> 361 1 0.000
#> 362 1 0.000
#> 363 1 0.000
#> 364 1 0.000
#> 365 1 0.000
#> 366 1 0.000
#> 367 1 0.000
#> 368 1 0.000
#> 369 1 0.000
#> 370 1 0.000
#> 371 1 0.000
#> 372 1 0.000
#> 373 2 1.000
#> 374 1 1.000
#> 375 1 0.000
#> 376 1 0.000
#> 377 1 0.000
#> 378 1 0.000
#> 379 1 0.000
#> 380 1 0.000
#> 381 1 0.000
#> 382 1 0.000
#> 383 1 0.000
#> 384 1 0.000
#> 385 1 0.000
#> 386 1 0.000
#> 387 1 0.000
#> 388 1 0.000
#> 389 1 0.000
#> 390 1 0.000
#> 391 2 0.000
#> 392 2 0.000
#> 393 2 0.000
#> 394 2 0.000
#> 395 2 0.000
#> 396 2 0.000
#> 397 2 1.000
#> 398 2 0.000
#> 399 2 0.000
#> 400 1 0.000
#> 401 1 0.498
#> 402 2 0.000
#> 403 1 0.000
#> 404 1 0.751
#> 405 1 0.000
#> 406 2 0.000
#> 407 2 0.000
#> 408 1 0.000
#> 409 1 0.000
#> 410 1 0.000
#> 411 2 1.000
#> 412 2 1.000
#> 413 2 0.000
#> 414 2 0.000
#> 415 1 0.000
#> 416 1 0.000
#> 417 2 0.000
#> 418 1 0.000
#> 419 2 0.000
#> 420 1 0.000
#> 421 1 0.000
#> 422 1 0.000
#> 423 1 0.000
#> 424 1 0.249
#> 425 1 0.000
#> 426 2 0.249
#> 427 2 0.000
#> 428 1 0.000
#> 429 2 0.000
#> 430 1 0.000
#> 431 1 0.000
#> 432 1 0.249
#> 433 2 0.000
#> 434 1 0.000
#> 435 1 0.000
#> 436 2 0.000
#> 437 1 0.000
#> 438 1 0.000
#> 439 1 0.000
#> 440 2 0.000
#> 441 2 0.000
#> 442 1 0.000
#> 443 1 0.249
#> 444 2 0.000
#> 445 1 1.000
#> 446 2 0.000
#> 447 1 0.000
#> 448 1 0.000
#> 449 2 0.000
#> 450 1 0.000
#> 451 2 0.000
#> 452 2 0.000
#> 453 2 0.000
#> 454 2 0.000
#> 455 2 0.000
#> 456 2 0.000
#> 457 1 0.000
#> 458 2 0.000
#> 459 2 0.000
#> 460 2 0.000
#> 461 1 0.000
#> 462 1 0.000
#> 463 1 0.000
#> 464 1 0.000
#> 465 1 0.000
#> 466 1 0.000
#> 467 1 0.249
#> 468 1 0.000
#> 469 1 0.000
#> 470 1 0.000
#> 471 1 0.000
#> 472 2 0.000
#> 473 2 0.000
#> 474 2 0.000
#> 475 1 0.000
#> 476 1 0.000
#> 477 1 0.000
#> 478 1 0.000
#> 479 1 0.000
#> 480 1 0.000
#> 481 2 0.000
#> 482 2 0.000
#> 483 2 0.000
#> 484 1 0.751
#> 485 2 0.000
#> 486 1 1.000
#> 487 2 0.000
#> 488 2 0.000
#> 489 2 0.000
#> 490 1 1.000
#> 491 2 0.000
#> 492 1 0.751
#> 493 2 0.000
#> 494 2 0.000
#> 495 1 0.000
#> 496 2 0.000
#> 497 2 0.000
#> 498 2 0.000
#> 499 2 0.000
#> 500 2 0.000
#> 501 2 0.000
#> 502 2 0.000
#> 503 2 0.253
#> 504 1 0.000
#> 505 2 0.000
#> 506 1 0.000
#> 507 2 0.000
#> 508 2 0.000
#> 509 1 0.000
#> 510 1 0.000
#> 511 1 0.000
#> 512 2 0.000
#> 513 1 0.000
#> 514 1 0.000
#> 515 1 0.000
#> 516 1 1.000
#> 517 1 0.000
#> 518 2 0.000
#> 519 1 0.000
#> 520 1 0.498
#> 521 2 0.000
#> 522 1 0.000
#> 523 2 0.000
#> 524 1 0.502
#> 525 1 0.000
#> 526 2 0.000
#> 527 2 0.000
#> 528 1 1.000
#> 529 1 0.000
#> 530 1 0.000
#> 531 1 0.000
#> 532 2 0.000
#> 533 2 0.000
#> 534 2 0.000
#> 535 2 0.000
#> 536 2 1.000
#> 537 1 0.000
#> 538 1 0.000
#> 539 1 0.000
#> 540 2 0.000
#> 541 2 1.000
#> 542 2 0.000
#> 543 2 0.000
#> 544 1 0.000
#> 545 2 0.000
#> 546 2 0.000
#> 547 1 0.000
#> 548 2 0.000
#> 549 2 0.000
#> 550 1 0.000
#> 551 2 0.000
#> 552 2 0.000
#> 553 2 0.000
#> 554 2 0.000
#> 555 1 1.000
#> 556 2 0.000
#> 557 1 0.000
#> 558 2 0.000
#> 559 2 0.000
#> 560 2 0.000
#> 561 2 0.000
#> 562 2 1.000
#> 563 2 1.000
#> 564 2 0.000
#> 565 2 0.000
#> 566 1 0.000
#> 567 1 0.000
#> 568 2 0.000
#> 569 1 1.000
#> 570 2 0.000
#> 571 2 0.000
#> 572 2 0.000
#> 573 2 0.000
#> 574 2 0.000
#> 575 2 0.000
#> 576 2 0.000
#> 577 2 0.000
#> 578 2 0.000
#> 579 1 0.000
#> 580 2 0.000
#> 581 2 0.000
#> 582 2 0.000
#> 583 2 0.000
#> 584 2 0.000
#> 585 2 0.000
#> 586 2 0.000
#> 587 2 0.000
#> 588 2 0.000
#> 589 2 0.000
#> 590 2 0.000
#> 591 2 0.000
#> 592 2 0.000
#> 593 2 0.000
#> 594 2 0.000
#> 595 2 0.000
#> 596 2 0.000
#> 597 2 0.000
#> 598 2 0.000
#> 599 2 0.000
#> 600 2 0.000
#> 601 2 0.000
#> 602 2 0.000
#> 603 2 0.000
#> 604 2 0.000
#> 605 2 0.000
#> 606 2 0.000
#> 607 2 0.000
#> 608 2 0.000
#> 609 2 0.000
#> 610 2 0.000
#> 611 2 0.000
#> 612 2 0.000
#> 613 2 0.000
#> 614 2 0.000
#> 615 2 0.000
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.000
#> 619 2 0.000
#> 620 2 0.000
#> 621 2 0.000
#> 622 2 0.000
#> 623 2 0.000
#> 624 2 0.000
#> 625 2 0.000
#> 626 2 0.000
#> 627 2 0.000
#> 628 2 0.000
#> 629 2 0.000
#> 630 2 0.000
#> 631 2 0.000
#> 632 2 0.000
#> 633 2 0.000
#> 634 2 0.000
#> 635 2 0.000
#> 636 2 0.000
#> 637 2 0.000
#> 638 2 0.000
#> 639 2 0.000
#> 640 2 0.000
#> 641 2 0.000
#> 642 2 0.000
#> 643 2 0.000
#> 644 2 0.000
#> 645 2 0.000
#> 646 2 0.000
#> 647 2 0.000
#> 648 2 0.000
get_classes(res, k = 3)
#> class p
#> 1 2 0.000
#> 2 2 0.000
#> 3 2 0.000
#> 4 1 0.747
#> 5 2 0.000
#> 6 2 0.000
#> 7 2 0.000
#> 8 2 0.000
#> 9 2 0.000
#> 10 1 0.000
#> 11 2 0.000
#> 12 2 0.000
#> 13 2 0.000
#> 14 2 0.000
#> 15 1 0.000
#> 16 1 0.000
#> 17 1 0.000
#> 18 1 0.000
#> 19 1 0.000
#> 20 2 1.000
#> 21 1 0.000
#> 22 1 0.498
#> 23 2 0.000
#> 24 1 0.000
#> 25 2 0.000
#> 26 2 0.000
#> 27 1 0.000
#> 28 2 0.000
#> 29 2 0.751
#> 30 2 0.751
#> 31 1 0.000
#> 32 2 0.249
#> 33 2 0.000
#> 34 2 0.000
#> 35 1 0.000
#> 36 2 0.000
#> 37 2 0.000
#> 38 2 0.000
#> 39 2 0.000
#> 40 1 0.000
#> 41 1 0.000
#> 42 1 0.000
#> 43 2 1.000
#> 44 1 0.000
#> 45 2 1.000
#> 46 2 1.000
#> 47 2 0.751
#> 48 1 0.249
#> 49 1 0.000
#> 50 2 1.000
#> 51 1 0.000
#> 52 2 0.000
#> 53 1 0.000
#> 54 1 0.000
#> 55 1 0.000
#> 56 1 0.000
#> 57 1 0.000
#> 58 1 0.000
#> 59 1 0.000
#> 60 1 0.000
#> 61 2 1.000
#> 62 1 0.751
#> 63 2 1.000
#> 64 1 0.000
#> 65 1 1.000
#> 66 1 1.000
#> 67 1 0.000
#> 68 2 0.000
#> 69 1 0.000
#> 70 1 0.000
#> 71 1 0.000
#> 72 2 1.000
#> 73 1 0.000
#> 74 1 0.000
#> 75 1 0.000
#> 76 1 0.000
#> 77 1 0.000
#> 78 1 0.000
#> 79 1 0.000
#> 80 1 0.000
#> 81 1 0.000
#> 82 1 0.000
#> 83 2 0.498
#> 84 1 0.000
#> 85 2 0.000
#> 86 1 0.000
#> 87 1 0.000
#> 88 1 0.000
#> 89 1 1.000
#> 90 1 0.000
#> 91 2 1.000
#> 92 1 0.000
#> 93 1 0.000
#> 94 1 0.000
#> 95 1 0.000
#> 96 1 0.000
#> 97 1 0.000
#> 98 1 0.000
#> 99 1 0.000
#> 100 1 0.000
#> 101 1 0.000
#> 102 1 0.000
#> 103 1 0.000
#> 104 1 0.000
#> 105 1 0.000
#> 106 1 0.000
#> 107 1 0.000
#> 108 1 0.498
#> 109 1 0.000
#> 110 1 0.000
#> 111 1 0.000
#> 112 1 0.000
#> 113 1 0.000
#> 114 1 0.000
#> 115 1 0.000
#> 116 1 0.000
#> 117 1 0.000
#> 118 1 0.000
#> 119 1 0.000
#> 120 1 0.000
#> 121 1 0.000
#> 122 1 0.000
#> 123 1 0.000
#> 124 1 0.000
#> 125 1 0.000
#> 126 1 0.000
#> 127 1 0.249
#> 128 1 0.000
#> 129 1 0.000
#> 130 1 0.000
#> 131 1 1.000
#> 132 1 0.000
#> 133 1 0.000
#> 134 1 0.000
#> 135 2 1.000
#> 136 1 0.000
#> 137 1 0.000
#> 138 1 0.000
#> 139 1 0.000
#> 140 1 0.000
#> 141 1 0.000
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 1 0.000
#> 147 1 0.000
#> 148 1 0.000
#> 149 1 0.000
#> 150 1 0.000
#> 151 1 0.000
#> 152 1 0.000
#> 153 1 0.000
#> 154 1 0.000
#> 155 1 0.000
#> 156 1 0.000
#> 157 1 0.000
#> 158 1 0.000
#> 159 1 0.000
#> 160 1 0.000
#> 161 1 0.000
#> 162 1 0.000
#> 163 1 0.000
#> 164 1 0.000
#> 165 1 0.000
#> 166 1 0.000
#> 167 1 0.000
#> 168 1 0.000
#> 169 1 0.000
#> 170 1 0.000
#> 171 1 0.000
#> 172 1 0.000
#> 173 1 1.000
#> 174 1 0.000
#> 175 1 0.000
#> 176 1 0.000
#> 177 1 0.000
#> 178 1 0.000
#> 179 1 0.000
#> 180 1 0.000
#> 181 1 0.000
#> 182 1 0.000
#> 183 1 0.000
#> 184 1 0.000
#> 185 1 0.000
#> 186 1 0.000
#> 187 1 0.000
#> 188 1 0.000
#> 189 1 0.000
#> 190 1 0.000
#> 191 1 0.000
#> 192 1 0.000
#> 193 1 0.000
#> 194 1 0.000
#> 195 1 0.000
#> 196 1 0.000
#> 197 1 0.000
#> 198 1 0.000
#> 199 1 0.000
#> 200 1 0.000
#> 201 1 0.000
#> 202 1 0.000
#> 203 1 0.000
#> 204 1 0.000
#> 205 1 0.000
#> 206 1 0.000
#> 207 1 0.000
#> 208 1 0.000
#> 209 1 0.000
#> 210 1 0.000
#> 211 1 0.000
#> 212 2 0.502
#> 213 1 0.000
#> 214 1 0.000
#> 215 1 0.249
#> 216 1 0.000
#> 217 1 0.000
#> 218 1 0.000
#> 219 1 0.000
#> 220 1 0.000
#> 221 1 0.000
#> 222 1 0.000
#> 223 1 0.000
#> 224 1 0.000
#> 225 1 0.000
#> 226 1 0.000
#> 227 1 0.000
#> 228 1 0.000
#> 229 1 0.000
#> 230 1 0.000
#> 231 1 0.000
#> 232 1 0.000
#> 233 1 0.000
#> 234 1 0.000
#> 235 1 0.000
#> 236 1 0.000
#> 237 1 0.000
#> 238 1 0.000
#> 239 1 0.000
#> 240 1 0.000
#> 241 1 0.000
#> 242 1 0.000
#> 243 1 0.000
#> 244 1 0.000
#> 245 1 0.000
#> 246 1 0.000
#> 247 1 0.000
#> 248 1 0.000
#> 249 1 0.000
#> 250 1 0.000
#> 251 1 0.000
#> 252 1 0.000
#> 253 1 0.000
#> 254 1 0.000
#> 255 1 0.000
#> 256 1 0.000
#> 257 1 0.000
#> 258 1 0.000
#> 259 1 0.000
#> 260 1 0.000
#> 261 1 0.000
#> 262 1 0.000
#> 263 1 0.000
#> 264 1 0.000
#> 265 1 0.000
#> 266 1 0.000
#> 267 1 0.000
#> 268 1 0.000
#> 269 1 0.000
#> 270 1 0.000
#> 271 1 0.000
#> 272 1 0.000
#> 273 1 0.000
#> 274 1 0.000
#> 275 1 0.000
#> 276 1 0.000
#> 277 1 0.000
#> 278 1 0.000
#> 279 1 0.000
#> 280 1 0.000
#> 281 1 0.000
#> 282 1 0.000
#> 283 1 0.000
#> 284 1 0.502
#> 285 1 0.000
#> 286 1 1.000
#> 287 1 0.000
#> 288 1 0.000
#> 289 2 0.000
#> 290 2 1.000
#> 291 1 0.000
#> 292 2 0.249
#> 293 1 0.000
#> 294 1 0.000
#> 295 1 0.000
#> 296 1 0.000
#> 297 1 0.000
#> 298 1 0.000
#> 299 1 0.000
#> 300 2 1.000
#> 301 1 0.000
#> 302 1 0.000
#> 303 1 0.000
#> 304 1 0.000
#> 305 1 0.000
#> 306 1 1.000
#> 307 1 0.000
#> 308 1 0.000
#> 309 1 0.000
#> 310 1 0.000
#> 311 1 0.000
#> 312 1 0.000
#> 313 1 0.000
#> 314 1 0.000
#> 315 1 0.000
#> 316 1 0.000
#> 317 1 0.000
#> 318 1 0.000
#> 319 1 0.000
#> 320 1 0.000
#> 321 1 0.000
#> 322 1 0.000
#> 323 1 0.000
#> 324 1 0.000
#> 325 1 0.000
#> 326 1 0.000
#> 327 1 0.000
#> 328 1 0.000
#> 329 1 0.000
#> 330 1 0.000
#> 331 1 0.000
#> 332 1 0.000
#> 333 1 0.000
#> 334 1 0.000
#> 335 1 0.000
#> 336 1 0.000
#> 337 1 0.000
#> 338 1 0.000
#> 339 1 0.000
#> 340 1 0.000
#> 341 1 0.000
#> 342 1 0.000
#> 343 1 0.000
#> 344 1 0.000
#> 345 1 0.000
#> 346 1 0.000
#> 347 1 0.000
#> 348 1 0.000
#> 349 1 0.000
#> 350 1 0.000
#> 351 1 0.000
#> 352 1 0.000
#> 353 1 0.000
#> 354 2 1.000
#> 355 2 0.249
#> 356 1 0.000
#> 357 1 0.000
#> 358 1 1.000
#> 359 1 0.000
#> 360 1 0.000
#> 361 1 0.000
#> 362 1 0.000
#> 363 1 0.249
#> 364 1 0.000
#> 365 1 0.000
#> 366 1 0.000
#> 367 1 0.000
#> 368 1 0.000
#> 369 1 0.000
#> 370 1 0.000
#> 371 1 0.000
#> 372 1 0.000
#> 373 2 1.000
#> 374 1 0.000
#> 375 1 0.000
#> 376 1 0.000
#> 377 1 1.000
#> 378 1 0.000
#> 379 1 0.000
#> 380 1 0.000
#> 381 1 0.000
#> 382 1 0.000
#> 383 1 0.000
#> 384 1 0.000
#> 385 1 0.000
#> 386 1 0.000
#> 387 1 0.000
#> 388 1 0.000
#> 389 1 0.000
#> 390 1 0.000
#> 391 2 0.000
#> 392 2 0.502
#> 393 2 0.000
#> 394 2 0.000
#> 395 2 0.253
#> 396 2 0.000
#> 397 1 1.000
#> 398 2 0.000
#> 399 2 0.000
#> 400 1 0.000
#> 401 1 0.253
#> 402 2 0.751
#> 403 1 0.000
#> 404 1 1.000
#> 405 1 0.000
#> 406 2 0.000
#> 407 2 0.000
#> 408 1 0.751
#> 409 1 0.000
#> 410 1 0.000
#> 411 1 1.000
#> 412 2 1.000
#> 413 2 0.000
#> 414 2 0.000
#> 415 1 0.000
#> 416 1 0.000
#> 417 2 0.000
#> 418 1 0.000
#> 419 2 0.000
#> 420 1 0.000
#> 421 1 0.000
#> 422 1 0.000
#> 423 1 0.000
#> 424 1 0.000
#> 425 1 0.000
#> 426 2 0.751
#> 427 2 0.000
#> 428 1 0.249
#> 429 2 0.000
#> 430 1 0.000
#> 431 1 0.000
#> 432 3 1.000
#> 433 2 0.000
#> 434 1 0.000
#> 435 1 0.000
#> 436 3 0.000
#> 437 3 0.000
#> 438 3 0.000
#> 439 3 0.000
#> 440 3 0.000
#> 441 3 0.000
#> 442 3 0.000
#> 443 3 0.000
#> 444 3 0.000
#> 445 3 0.000
#> 446 3 0.000
#> 447 3 0.000
#> 448 3 0.000
#> 449 3 0.000
#> 450 3 0.000
#> 451 3 0.000
#> 452 3 0.000
#> 453 3 0.000
#> 454 3 0.000
#> 455 3 0.000
#> 456 3 0.000
#> 457 3 0.000
#> 458 3 0.000
#> 459 2 0.000
#> 460 3 0.000
#> 461 1 1.000
#> 462 3 0.751
#> 463 1 0.000
#> 464 3 1.000
#> 465 1 0.751
#> 466 1 1.000
#> 467 3 1.000
#> 468 3 0.000
#> 469 1 1.000
#> 470 3 0.000
#> 471 1 1.000
#> 472 2 0.000
#> 473 2 0.000
#> 474 3 0.249
#> 475 3 1.000
#> 476 1 0.502
#> 477 1 0.000
#> 478 3 0.751
#> 479 3 0.000
#> 480 3 0.000
#> 481 3 0.000
#> 482 3 0.000
#> 483 3 0.000
#> 484 3 0.000
#> 485 3 0.000
#> 486 3 0.000
#> 487 3 0.000
#> 488 3 0.000
#> 489 3 0.000
#> 490 3 0.000
#> 491 3 0.000
#> 492 3 0.000
#> 493 3 0.000
#> 494 3 0.000
#> 495 3 0.000
#> 496 3 0.000
#> 497 3 0.000
#> 498 3 0.000
#> 499 3 0.000
#> 500 3 0.000
#> 501 2 0.000
#> 502 2 0.000
#> 503 2 0.000
#> 504 1 0.000
#> 505 2 0.000
#> 506 1 0.000
#> 507 2 0.000
#> 508 2 0.000
#> 509 1 0.000
#> 510 1 0.000
#> 511 1 0.000
#> 512 2 0.000
#> 513 1 0.000
#> 514 1 0.000
#> 515 1 0.000
#> 516 2 1.000
#> 517 1 0.000
#> 518 2 0.000
#> 519 1 0.000
#> 520 1 0.747
#> 521 2 0.000
#> 522 1 0.000
#> 523 2 0.000
#> 524 1 0.249
#> 525 1 0.000
#> 526 2 0.000
#> 527 2 0.000
#> 528 2 0.000
#> 529 1 0.000
#> 530 3 0.000
#> 531 1 0.000
#> 532 2 0.000
#> 533 2 0.000
#> 534 2 0.000
#> 535 2 0.000
#> 536 2 0.000
#> 537 1 0.000
#> 538 1 0.000
#> 539 1 0.000
#> 540 2 0.000
#> 541 1 0.498
#> 542 3 0.000
#> 543 2 0.000
#> 544 1 0.000
#> 545 2 0.000
#> 546 2 0.000
#> 547 1 0.000
#> 548 2 0.000
#> 549 2 0.000
#> 550 3 1.000
#> 551 2 0.751
#> 552 2 0.000
#> 553 2 0.000
#> 554 3 0.000
#> 555 1 1.000
#> 556 2 0.000
#> 557 1 0.000
#> 558 2 0.000
#> 559 2 0.000
#> 560 2 0.000
#> 561 2 0.000
#> 562 2 0.000
#> 563 2 0.000
#> 564 3 0.000
#> 565 2 0.000
#> 566 1 0.000
#> 567 1 0.000
#> 568 2 0.000
#> 569 1 0.000
#> 570 2 0.000
#> 571 2 0.000
#> 572 2 0.000
#> 573 3 0.000
#> 574 2 0.000
#> 575 2 0.000
#> 576 2 0.000
#> 577 2 0.000
#> 578 2 0.000
#> 579 1 0.000
#> 580 2 0.000
#> 581 2 0.000
#> 582 2 0.000
#> 583 2 0.000
#> 584 2 0.000
#> 585 2 0.000
#> 586 2 0.000
#> 587 2 0.000
#> 588 2 0.000
#> 589 2 0.000
#> 590 2 0.000
#> 591 2 0.000
#> 592 2 0.000
#> 593 2 0.000
#> 594 2 0.000
#> 595 2 0.000
#> 596 2 0.000
#> 597 2 0.000
#> 598 2 0.000
#> 599 2 0.000
#> 600 2 0.000
#> 601 2 0.000
#> 602 2 0.000
#> 603 2 0.000
#> 604 2 0.000
#> 605 2 0.000
#> 606 2 0.000
#> 607 2 0.000
#> 608 2 0.000
#> 609 2 0.000
#> 610 2 0.000
#> 611 2 0.000
#> 612 2 0.000
#> 613 2 0.000
#> 614 2 0.000
#> 615 2 0.000
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.000
#> 619 2 0.000
#> 620 2 0.000
#> 621 2 0.000
#> 622 2 0.000
#> 623 2 0.000
#> 624 2 0.000
#> 625 2 0.000
#> 626 2 0.000
#> 627 2 0.000
#> 628 2 0.000
#> 629 2 0.000
#> 630 2 0.000
#> 631 2 0.000
#> 632 2 0.000
#> 633 2 0.000
#> 634 2 0.000
#> 635 2 0.000
#> 636 2 0.000
#> 637 2 0.000
#> 638 2 0.000
#> 639 2 0.000
#> 640 2 0.000
#> 641 2 0.000
#> 642 2 0.000
#> 643 2 0.000
#> 644 2 0.000
#> 645 2 0.000
#> 646 2 0.000
#> 647 2 0.000
#> 648 2 0.000
get_classes(res, k = 4)
#> class p
#> 1 2 0.000
#> 2 2 0.000
#> 3 2 0.000
#> 4 4 0.000
#> 5 2 0.000
#> 6 2 0.000
#> 7 2 0.000
#> 8 2 0.000
#> 9 2 0.000
#> 10 1 1.000
#> 11 2 0.000
#> 12 4 1.000
#> 13 2 0.000
#> 14 2 0.000
#> 15 1 0.000
#> 16 4 1.000
#> 17 4 1.000
#> 18 4 1.000
#> 19 1 1.000
#> 20 4 1.000
#> 21 1 1.000
#> 22 4 1.000
#> 23 2 0.000
#> 24 4 1.000
#> 25 4 1.000
#> 26 2 0.000
#> 27 4 0.000
#> 28 4 0.751
#> 29 4 0.000
#> 30 4 1.000
#> 31 4 1.000
#> 32 4 0.751
#> 33 2 1.000
#> 34 2 0.000
#> 35 4 0.751
#> 36 2 0.249
#> 37 2 0.000
#> 38 4 0.253
#> 39 2 0.000
#> 40 4 1.000
#> 41 4 1.000
#> 42 4 1.000
#> 43 4 1.000
#> 44 1 1.000
#> 45 4 0.000
#> 46 4 0.000
#> 47 4 0.751
#> 48 4 1.000
#> 49 4 1.000
#> 50 4 0.751
#> 51 1 1.000
#> 52 4 1.000
#> 53 4 1.000
#> 54 4 1.000
#> 55 4 1.000
#> 56 1 1.000
#> 57 1 0.747
#> 58 1 1.000
#> 59 1 0.000
#> 60 4 1.000
#> 61 4 0.751
#> 62 4 0.502
#> 63 4 0.000
#> 64 4 1.000
#> 65 4 0.000
#> 66 4 0.000
#> 67 1 0.000
#> 68 4 1.000
#> 69 1 0.747
#> 70 1 0.000
#> 71 1 1.000
#> 72 4 0.000
#> 73 1 0.000
#> 74 1 0.000
#> 75 1 0.000
#> 76 1 0.000
#> 77 1 0.000
#> 78 1 0.249
#> 79 1 0.000
#> 80 1 1.000
#> 81 1 0.000
#> 82 1 0.000
#> 83 4 0.000
#> 84 4 1.000
#> 85 4 0.253
#> 86 1 1.000
#> 87 4 1.000
#> 88 1 0.000
#> 89 4 0.751
#> 90 1 1.000
#> 91 4 0.000
#> 92 1 0.000
#> 93 1 1.000
#> 94 1 1.000
#> 95 1 0.000
#> 96 1 0.498
#> 97 1 0.000
#> 98 1 1.000
#> 99 1 0.000
#> 100 1 0.000
#> 101 1 1.000
#> 102 4 1.000
#> 103 1 0.000
#> 104 1 1.000
#> 105 1 0.000
#> 106 1 1.000
#> 107 4 1.000
#> 108 1 0.000
#> 109 1 0.000
#> 110 4 1.000
#> 111 4 1.000
#> 112 1 0.000
#> 113 1 0.000
#> 114 1 0.000
#> 115 1 0.000
#> 116 1 1.000
#> 117 4 1.000
#> 118 1 0.498
#> 119 1 0.000
#> 120 1 0.000
#> 121 4 1.000
#> 122 4 1.000
#> 123 1 0.000
#> 124 4 1.000
#> 125 4 1.000
#> 126 4 1.000
#> 127 4 0.751
#> 128 4 1.000
#> 129 4 1.000
#> 130 4 1.000
#> 131 4 0.000
#> 132 1 0.000
#> 133 4 0.751
#> 134 4 0.747
#> 135 4 0.000
#> 136 4 0.253
#> 137 4 1.000
#> 138 4 1.000
#> 139 4 0.747
#> 140 1 0.498
#> 141 1 0.000
#> 142 1 0.000
#> 143 1 0.000
#> 144 1 0.000
#> 145 1 0.000
#> 146 1 0.000
#> 147 1 0.000
#> 148 1 0.000
#> 149 1 0.000
#> 150 1 0.000
#> 151 1 0.000
#> 152 1 1.000
#> 153 4 1.000
#> 154 4 1.000
#> 155 1 0.000
#> 156 1 0.000
#> 157 1 0.000
#> 158 1 1.000
#> 159 4 1.000
#> 160 4 1.000
#> 161 1 0.000
#> 162 4 1.000
#> 163 4 0.747
#> 164 1 0.747
#> 165 1 0.000
#> 166 4 1.000
#> 167 1 0.000
#> 168 4 0.000
#> 169 4 1.000
#> 170 1 0.000
#> 171 4 1.000
#> 172 1 1.000
#> 173 4 0.000
#> 174 4 1.000
#> 175 4 1.000
#> 176 4 1.000
#> 177 4 1.000
#> 178 4 1.000
#> 179 1 0.751
#> 180 4 1.000
#> 181 4 0.498
#> 182 4 1.000
#> 183 4 1.000
#> 184 1 0.747
#> 185 4 1.000
#> 186 1 0.000
#> 187 1 0.000
#> 188 1 0.000
#> 189 1 0.000
#> 190 1 1.000
#> 191 4 1.000
#> 192 1 1.000
#> 193 1 0.249
#> 194 4 1.000
#> 195 4 1.000
#> 196 4 1.000
#> 197 1 0.000
#> 198 1 0.000
#> 199 1 0.000
#> 200 1 0.249
#> 201 1 0.000
#> 202 1 0.000
#> 203 1 0.000
#> 204 1 0.000
#> 205 1 0.000
#> 206 1 0.000
#> 207 1 0.000
#> 208 1 0.000
#> 209 4 0.249
#> 210 4 0.000
#> 211 4 0.498
#> 212 4 1.000
#> 213 4 0.000
#> 214 4 0.751
#> 215 4 0.000
#> 216 4 0.751
#> 217 4 1.000
#> 218 4 0.000
#> 219 4 1.000
#> 220 4 0.000
#> 221 4 1.000
#> 222 4 1.000
#> 223 4 1.000
#> 224 4 1.000
#> 225 4 0.249
#> 226 1 0.249
#> 227 4 1.000
#> 228 4 1.000
#> 229 1 1.000
#> 230 1 0.000
#> 231 1 0.000
#> 232 1 0.000
#> 233 1 1.000
#> 234 4 1.000
#> 235 1 0.000
#> 236 1 0.000
#> 237 1 0.000
#> 238 4 1.000
#> 239 1 0.000
#> 240 1 1.000
#> 241 4 1.000
#> 242 1 0.000
#> 243 1 0.000
#> 244 1 0.000
#> 245 1 0.000
#> 246 1 0.000
#> 247 1 0.000
#> 248 1 0.000
#> 249 4 1.000
#> 250 1 0.000
#> 251 1 0.000
#> 252 1 0.000
#> 253 4 1.000
#> 254 4 1.000
#> 255 1 0.000
#> 256 1 0.498
#> 257 4 1.000
#> 258 1 0.747
#> 259 4 1.000
#> 260 4 1.000
#> 261 1 0.000
#> 262 1 0.498
#> 263 1 1.000
#> 264 1 0.000
#> 265 1 0.000
#> 266 1 0.000
#> 267 1 0.000
#> 268 4 1.000
#> 269 1 0.000
#> 270 1 0.747
#> 271 4 1.000
#> 272 4 0.747
#> 273 4 1.000
#> 274 4 1.000
#> 275 4 1.000
#> 276 1 1.000
#> 277 1 0.000
#> 278 1 0.000
#> 279 1 0.000
#> 280 1 0.000
#> 281 1 0.000
#> 282 1 0.000
#> 283 1 0.000
#> 284 4 0.000
#> 285 4 1.000
#> 286 4 0.000
#> 287 4 0.747
#> 288 4 1.000
#> 289 2 1.000
#> 290 4 0.000
#> 291 4 0.000
#> 292 4 1.000
#> 293 4 0.747
#> 294 1 0.000
#> 295 4 0.000
#> 296 4 1.000
#> 297 4 0.498
#> 298 1 0.000
#> 299 4 1.000
#> 300 4 0.000
#> 301 4 0.747
#> 302 4 0.000
#> 303 4 1.000
#> 304 4 0.000
#> 305 4 0.000
#> 306 4 0.000
#> 307 4 0.000
#> 308 4 1.000
#> 309 4 1.000
#> 310 4 0.747
#> 311 4 1.000
#> 312 4 0.502
#> 313 4 1.000
#> 314 4 0.751
#> 315 1 1.000
#> 316 4 0.751
#> 317 4 0.253
#> 318 4 1.000
#> 319 4 1.000
#> 320 4 1.000
#> 321 1 0.000
#> 322 1 1.000
#> 323 4 0.000
#> 324 4 1.000
#> 325 4 1.000
#> 326 1 0.000
#> 327 4 1.000
#> 328 4 0.000
#> 329 4 0.000
#> 330 4 1.000
#> 331 4 0.000
#> 332 1 0.000
#> 333 4 1.000
#> 334 4 1.000
#> 335 4 1.000
#> 336 4 0.000
#> 337 1 1.000
#> 338 1 0.000
#> 339 4 1.000
#> 340 1 0.000
#> 341 1 0.000
#> 342 1 0.000
#> 343 1 1.000
#> 344 1 1.000
#> 345 4 1.000
#> 346 1 0.000
#> 347 4 1.000
#> 348 4 1.000
#> 349 1 0.000
#> 350 1 1.000
#> 351 1 0.000
#> 352 4 1.000
#> 353 4 1.000
#> 354 4 0.000
#> 355 4 0.000
#> 356 4 0.000
#> 357 4 1.000
#> 358 4 0.000
#> 359 4 0.498
#> 360 1 0.498
#> 361 1 1.000
#> 362 4 0.498
#> 363 4 0.000
#> 364 1 0.000
#> 365 4 0.747
#> 366 4 1.000
#> 367 1 0.000
#> 368 1 0.000
#> 369 1 0.000
#> 370 4 0.747
#> 371 4 1.000
#> 372 4 0.747
#> 373 4 0.000
#> 374 4 1.000
#> 375 4 1.000
#> 376 1 1.000
#> 377 4 0.000
#> 378 4 0.747
#> 379 1 0.000
#> 380 1 1.000
#> 381 4 1.000
#> 382 1 0.000
#> 383 1 0.000
#> 384 4 1.000
#> 385 1 0.000
#> 386 1 1.000
#> 387 4 1.000
#> 388 4 0.747
#> 389 4 1.000
#> 390 4 1.000
#> 391 2 1.000
#> 392 4 1.000
#> 393 2 1.000
#> 394 2 0.000
#> 395 4 1.000
#> 396 2 0.751
#> 397 4 0.000
#> 398 2 0.000
#> 399 2 0.502
#> 400 4 0.000
#> 401 4 0.000
#> 402 4 1.000
#> 403 4 0.000
#> 404 4 0.000
#> 405 4 0.000
#> 406 2 0.000
#> 407 2 1.000
#> 408 4 0.000
#> 409 1 1.000
#> 410 4 1.000
#> 411 4 0.000
#> 412 4 0.000
#> 413 2 0.000
#> 414 2 0.000
#> 415 4 0.751
#> 416 1 0.000
#> 417 2 0.000
#> 418 1 0.000
#> 419 4 1.000
#> 420 1 0.000
#> 421 1 0.498
#> 422 1 1.000
#> 423 4 1.000
#> 424 1 0.000
#> 425 1 0.000
#> 426 4 1.000
#> 427 2 0.000
#> 428 4 0.000
#> 429 2 0.000
#> 430 4 1.000
#> 431 4 1.000
#> 432 3 1.000
#> 433 2 0.000
#> 434 4 1.000
#> 435 4 0.000
#> 436 3 0.000
#> 437 3 0.000
#> 438 3 0.000
#> 439 3 0.000
#> 440 3 0.000
#> 441 3 0.000
#> 442 3 0.000
#> 443 3 0.000
#> 444 3 0.000
#> 445 3 0.000
#> 446 3 0.000
#> 447 3 0.000
#> 448 3 0.000
#> 449 3 0.000
#> 450 3 0.000
#> 451 3 0.000
#> 452 3 0.000
#> 453 3 0.000
#> 454 3 0.000
#> 455 3 0.000
#> 456 3 0.000
#> 457 3 0.000
#> 458 3 0.000
#> 459 2 0.000
#> 460 3 0.000
#> 461 4 0.000
#> 462 3 0.000
#> 463 1 1.000
#> 464 3 0.000
#> 465 4 0.000
#> 466 4 0.000
#> 467 3 0.000
#> 468 3 0.000
#> 469 1 1.000
#> 470 3 0.000
#> 471 1 0.747
#> 472 2 0.000
#> 473 2 0.000
#> 474 2 0.000
#> 475 3 0.000
#> 476 1 1.000
#> 477 1 0.000
#> 478 3 1.000
#> 479 3 0.000
#> 480 3 0.000
#> 481 3 0.000
#> 482 3 0.000
#> 483 3 0.000
#> 484 3 0.000
#> 485 3 0.000
#> 486 3 0.000
#> 487 3 0.000
#> 488 3 0.000
#> 489 3 0.000
#> 490 3 0.000
#> 491 3 0.000
#> 492 3 0.000
#> 493 3 0.000
#> 494 3 0.000
#> 495 3 0.000
#> 496 3 0.000
#> 497 3 0.000
#> 498 3 0.000
#> 499 3 0.000
#> 500 3 0.000
#> 501 2 0.000
#> 502 2 0.000
#> 503 4 0.747
#> 504 1 1.000
#> 505 2 0.000
#> 506 4 0.000
#> 507 2 0.000
#> 508 2 0.000
#> 509 4 1.000
#> 510 1 0.751
#> 511 4 1.000
#> 512 2 1.000
#> 513 1 0.000
#> 514 1 0.000
#> 515 4 1.000
#> 516 4 1.000
#> 517 4 1.000
#> 518 2 0.000
#> 519 4 1.000
#> 520 4 1.000
#> 521 2 0.000
#> 522 4 1.000
#> 523 2 0.000
#> 524 4 0.249
#> 525 4 1.000
#> 526 2 0.000
#> 527 2 0.000
#> 528 4 0.751
#> 529 1 0.000
#> 530 3 0.000
#> 531 1 0.000
#> 532 2 0.000
#> 533 2 0.000
#> 534 2 0.000
#> 535 2 0.000
#> 536 2 1.000
#> 537 1 1.000
#> 538 4 1.000
#> 539 1 0.000
#> 540 2 0.000
#> 541 4 0.000
#> 542 3 0.000
#> 543 2 0.000
#> 544 1 0.000
#> 545 2 0.000
#> 546 2 0.000
#> 547 4 1.000
#> 548 2 0.000
#> 549 2 0.000
#> 550 3 0.000
#> 551 4 1.000
#> 552 2 0.000
#> 553 2 0.000
#> 554 3 0.000
#> 555 4 0.751
#> 556 2 0.000
#> 557 1 0.000
#> 558 2 0.000
#> 559 2 0.000
#> 560 2 0.000
#> 561 2 0.000
#> 562 2 1.000
#> 563 4 1.000
#> 564 3 0.000
#> 565 2 0.000
#> 566 1 0.000
#> 567 1 0.000
#> 568 2 0.000
#> 569 4 1.000
#> 570 2 0.000
#> 571 2 0.000
#> 572 2 0.000
#> 573 3 0.000
#> 574 2 0.000
#> 575 2 0.000
#> 576 2 0.000
#> 577 2 0.000
#> 578 2 0.000
#> 579 1 0.498
#> 580 2 0.000
#> 581 2 0.000
#> 582 2 0.000
#> 583 2 0.000
#> 584 2 0.000
#> 585 2 0.000
#> 586 2 0.000
#> 587 2 0.000
#> 588 2 0.000
#> 589 2 0.000
#> 590 2 0.000
#> 591 2 0.000
#> 592 2 0.000
#> 593 2 0.000
#> 594 2 0.000
#> 595 2 0.000
#> 596 2 0.000
#> 597 2 0.000
#> 598 2 0.000
#> 599 2 0.000
#> 600 2 0.000
#> 601 2 0.000
#> 602 2 0.000
#> 603 2 0.000
#> 604 2 0.000
#> 605 2 0.000
#> 606 2 0.000
#> 607 2 0.000
#> 608 2 0.000
#> 609 2 0.000
#> 610 2 0.000
#> 611 2 0.000
#> 612 2 0.000
#> 613 2 0.000
#> 614 2 0.000
#> 615 2 0.000
#> 616 2 0.000
#> 617 2 0.000
#> 618 2 0.000
#> 619 2 0.000
#> 620 2 0.000
#> 621 2 0.000
#> 622 2 0.000
#> 623 2 0.000
#> 624 2 0.000
#> 625 2 0.000
#> 626 2 0.000
#> 627 2 0.000
#> 628 2 0.000
#> 629 2 0.000
#> 630 2 0.000
#> 631 2 0.000
#> 632 2 0.000
#> 633 2 0.000
#> 634 2 0.000
#> 635 2 0.000
#> 636 2 0.000
#> 637 2 0.000
#> 638 2 0.000
#> 639 2 0.000
#> 640 2 0.000
#> 641 2 0.000
#> 642 2 0.000
#> 643 2 0.000
#> 644 2 0.000
#> 645 2 0.000
#> 646 2 0.000
#> 647 2 0.000
#> 648 2 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 568 2.02e-54 2
#> ATC:skmeans 577 1.13e-156 3
#> ATC:skmeans 381 1.15e-98 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node03. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["031"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 9434 rows and 402 columns.
#> Top rows (943) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.974 0.956 0.982 0.486 0.514 0.514
#> 3 3 0.996 0.957 0.983 0.367 0.723 0.508
#> 4 4 0.943 0.916 0.966 0.109 0.843 0.583
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.000 0.9851 1.00 0.00
#> 2 2 0.000 0.9758 0.00 1.00
#> 3 2 0.000 0.9758 0.00 1.00
#> 4 2 0.855 0.6191 0.28 0.72
#> 5 1 0.000 0.9851 1.00 0.00
#> 6 1 0.000 0.9851 1.00 0.00
#> 7 2 0.000 0.9758 0.00 1.00
#> 8 2 0.000 0.9758 0.00 1.00
#> 9 1 0.000 0.9851 1.00 0.00
#> 10 1 0.000 0.9851 1.00 0.00
#> 11 1 0.000 0.9851 1.00 0.00
#> 12 1 0.000 0.9851 1.00 0.00
#> 13 1 0.000 0.9851 1.00 0.00
#> 14 1 0.000 0.9851 1.00 0.00
#> 15 1 0.000 0.9851 1.00 0.00
#> 16 1 0.000 0.9851 1.00 0.00
#> 17 2 0.722 0.7536 0.20 0.80
#> 18 1 0.000 0.9851 1.00 0.00
#> 19 1 0.000 0.9851 1.00 0.00
#> 20 2 0.000 0.9758 0.00 1.00
#> 21 1 0.141 0.9665 0.98 0.02
#> 22 1 0.000 0.9851 1.00 0.00
#> 23 2 0.529 0.8584 0.12 0.88
#> 24 1 0.000 0.9851 1.00 0.00
#> 25 2 0.000 0.9758 0.00 1.00
#> 26 1 0.469 0.8822 0.90 0.10
#> 27 2 0.000 0.9758 0.00 1.00
#> 28 1 0.000 0.9851 1.00 0.00
#> 29 1 0.000 0.9851 1.00 0.00
#> 30 1 0.000 0.9851 1.00 0.00
#> 31 1 0.000 0.9851 1.00 0.00
#> 32 1 0.000 0.9851 1.00 0.00
#> 33 2 0.000 0.9758 0.00 1.00
#> 34 2 0.000 0.9758 0.00 1.00
#> 35 2 0.000 0.9758 0.00 1.00
#> 36 1 0.000 0.9851 1.00 0.00
#> 37 2 0.000 0.9758 0.00 1.00
#> 38 2 0.000 0.9758 0.00 1.00
#> 39 2 0.000 0.9758 0.00 1.00
#> 40 2 0.000 0.9758 0.00 1.00
#> 41 2 0.000 0.9758 0.00 1.00
#> 42 1 0.000 0.9851 1.00 0.00
#> 43 2 0.000 0.9758 0.00 1.00
#> 44 2 0.000 0.9758 0.00 1.00
#> 45 2 0.000 0.9758 0.00 1.00
#> 46 2 0.000 0.9758 0.00 1.00
#> 47 1 0.000 0.9851 1.00 0.00
#> 48 2 0.760 0.7232 0.22 0.78
#> 49 1 0.000 0.9851 1.00 0.00
#> 50 2 0.000 0.9758 0.00 1.00
#> 51 1 0.000 0.9851 1.00 0.00
#> 52 2 0.000 0.9758 0.00 1.00
#> 53 2 0.000 0.9758 0.00 1.00
#> 54 2 0.000 0.9758 0.00 1.00
#> 55 2 0.584 0.8342 0.14 0.86
#> 56 2 0.000 0.9758 0.00 1.00
#> 57 1 0.855 0.6094 0.72 0.28
#> 58 2 0.000 0.9758 0.00 1.00
#> 59 2 0.000 0.9758 0.00 1.00
#> 60 2 0.000 0.9758 0.00 1.00
#> 61 2 0.000 0.9758 0.00 1.00
#> 62 2 0.000 0.9758 0.00 1.00
#> 63 1 0.327 0.9266 0.94 0.06
#> 64 2 0.000 0.9758 0.00 1.00
#> 65 1 0.242 0.9473 0.96 0.04
#> 66 2 0.000 0.9758 0.00 1.00
#> 67 2 0.000 0.9758 0.00 1.00
#> 68 1 0.000 0.9851 1.00 0.00
#> 69 2 0.000 0.9758 0.00 1.00
#> 70 2 0.000 0.9758 0.00 1.00
#> 71 1 0.000 0.9851 1.00 0.00
#> 72 1 0.000 0.9851 1.00 0.00
#> 73 2 0.000 0.9758 0.00 1.00
#> 74 2 0.000 0.9758 0.00 1.00
#> 75 2 0.000 0.9758 0.00 1.00
#> 76 2 0.000 0.9758 0.00 1.00
#> 77 2 0.000 0.9758 0.00 1.00
#> 78 1 0.000 0.9851 1.00 0.00
#> 79 2 0.000 0.9758 0.00 1.00
#> 80 2 0.000 0.9758 0.00 1.00
#> 81 2 0.000 0.9758 0.00 1.00
#> 82 1 0.000 0.9851 1.00 0.00
#> 83 1 0.000 0.9851 1.00 0.00
#> 84 2 0.000 0.9758 0.00 1.00
#> 85 1 0.000 0.9851 1.00 0.00
#> 86 1 0.000 0.9851 1.00 0.00
#> 87 1 0.000 0.9851 1.00 0.00
#> 88 1 0.000 0.9851 1.00 0.00
#> 89 2 0.584 0.8347 0.14 0.86
#> 90 1 0.000 0.9851 1.00 0.00
#> 91 1 0.000 0.9851 1.00 0.00
#> 92 1 0.000 0.9851 1.00 0.00
#> 93 2 0.141 0.9589 0.02 0.98
#> 94 1 0.000 0.9851 1.00 0.00
#> 95 1 0.000 0.9851 1.00 0.00
#> 96 1 0.000 0.9851 1.00 0.00
#> 97 1 0.000 0.9851 1.00 0.00
#> 98 1 0.000 0.9851 1.00 0.00
#> 99 1 0.000 0.9851 1.00 0.00
#> 100 1 0.000 0.9851 1.00 0.00
#> 101 2 0.000 0.9758 0.00 1.00
#> 102 2 0.000 0.9758 0.00 1.00
#> 103 2 0.000 0.9758 0.00 1.00
#> 104 2 0.000 0.9758 0.00 1.00
#> 105 2 0.000 0.9758 0.00 1.00
#> 106 2 0.000 0.9758 0.00 1.00
#> 107 2 0.000 0.9758 0.00 1.00
#> 108 2 0.000 0.9758 0.00 1.00
#> 109 2 0.000 0.9758 0.00 1.00
#> 110 2 0.000 0.9758 0.00 1.00
#> 111 2 0.000 0.9758 0.00 1.00
#> 112 1 0.000 0.9851 1.00 0.00
#> 113 1 0.000 0.9851 1.00 0.00
#> 114 1 0.000 0.9851 1.00 0.00
#> 115 2 0.000 0.9758 0.00 1.00
#> 116 2 0.000 0.9758 0.00 1.00
#> 117 2 0.000 0.9758 0.00 1.00
#> 118 1 0.999 0.0588 0.52 0.48
#> 119 1 0.000 0.9851 1.00 0.00
#> 120 1 0.000 0.9851 1.00 0.00
#> 121 2 0.000 0.9758 0.00 1.00
#> 122 1 0.000 0.9851 1.00 0.00
#> 123 1 0.000 0.9851 1.00 0.00
#> 124 1 0.000 0.9851 1.00 0.00
#> 125 2 0.000 0.9758 0.00 1.00
#> 126 1 0.000 0.9851 1.00 0.00
#> 127 1 0.000 0.9851 1.00 0.00
#> 128 1 0.000 0.9851 1.00 0.00
#> 129 1 0.000 0.9851 1.00 0.00
#> 130 2 0.000 0.9758 0.00 1.00
#> 131 1 0.000 0.9851 1.00 0.00
#> 132 1 0.000 0.9851 1.00 0.00
#> 133 1 0.000 0.9851 1.00 0.00
#> 134 1 0.000 0.9851 1.00 0.00
#> 135 1 0.000 0.9851 1.00 0.00
#> 136 1 0.000 0.9851 1.00 0.00
#> 137 1 0.000 0.9851 1.00 0.00
#> 138 1 0.000 0.9851 1.00 0.00
#> 139 1 0.000 0.9851 1.00 0.00
#> 140 1 0.000 0.9851 1.00 0.00
#> 141 1 0.000 0.9851 1.00 0.00
#> 142 1 0.000 0.9851 1.00 0.00
#> 143 1 0.000 0.9851 1.00 0.00
#> 144 2 0.469 0.8807 0.10 0.90
#> 145 1 0.000 0.9851 1.00 0.00
#> 146 2 0.000 0.9758 0.00 1.00
#> 147 2 0.000 0.9758 0.00 1.00
#> 148 2 0.000 0.9758 0.00 1.00
#> 149 2 0.000 0.9758 0.00 1.00
#> 150 2 0.000 0.9758 0.00 1.00
#> 151 1 0.000 0.9851 1.00 0.00
#> 152 2 0.990 0.2295 0.44 0.56
#> 153 2 0.000 0.9758 0.00 1.00
#> 154 1 0.000 0.9851 1.00 0.00
#> 155 1 0.000 0.9851 1.00 0.00
#> 156 1 0.000 0.9851 1.00 0.00
#> 157 2 0.000 0.9758 0.00 1.00
#> 158 2 0.000 0.9758 0.00 1.00
#> 159 2 0.000 0.9758 0.00 1.00
#> 160 1 0.904 0.5275 0.68 0.32
#> 161 2 0.000 0.9758 0.00 1.00
#> 162 2 0.000 0.9758 0.00 1.00
#> 163 2 0.000 0.9758 0.00 1.00
#> 164 2 0.000 0.9758 0.00 1.00
#> 165 2 0.000 0.9758 0.00 1.00
#> 166 2 0.000 0.9758 0.00 1.00
#> 167 2 0.000 0.9758 0.00 1.00
#> 168 2 0.000 0.9758 0.00 1.00
#> 169 1 0.000 0.9851 1.00 0.00
#> 170 1 0.000 0.9851 1.00 0.00
#> 171 1 0.000 0.9851 1.00 0.00
#> 172 1 0.000 0.9851 1.00 0.00
#> 173 1 0.000 0.9851 1.00 0.00
#> 174 1 0.000 0.9851 1.00 0.00
#> 175 1 0.000 0.9851 1.00 0.00
#> 176 1 0.000 0.9851 1.00 0.00
#> 177 1 0.000 0.9851 1.00 0.00
#> 178 1 0.000 0.9851 1.00 0.00
#> 179 1 0.000 0.9851 1.00 0.00
#> 180 1 0.000 0.9851 1.00 0.00
#> 181 1 0.000 0.9851 1.00 0.00
#> 182 1 0.000 0.9851 1.00 0.00
#> 183 1 0.000 0.9851 1.00 0.00
#> 184 1 0.000 0.9851 1.00 0.00
#> 185 1 0.000 0.9851 1.00 0.00
#> 186 1 0.000 0.9851 1.00 0.00
#> 187 1 0.000 0.9851 1.00 0.00
#> 188 1 0.000 0.9851 1.00 0.00
#> 189 2 0.000 0.9758 0.00 1.00
#> 190 2 0.000 0.9758 0.00 1.00
#> 191 2 0.000 0.9758 0.00 1.00
#> 192 1 0.000 0.9851 1.00 0.00
#> 193 1 0.000 0.9851 1.00 0.00
#> 194 2 0.000 0.9758 0.00 1.00
#> 195 2 0.000 0.9758 0.00 1.00
#> 196 2 0.000 0.9758 0.00 1.00
#> 197 1 0.000 0.9851 1.00 0.00
#> 198 2 0.000 0.9758 0.00 1.00
#> 199 1 0.000 0.9851 1.00 0.00
#> 200 1 0.000 0.9851 1.00 0.00
#> 201 2 0.000 0.9758 0.00 1.00
#> 202 2 0.000 0.9758 0.00 1.00
#> 203 2 0.000 0.9758 0.00 1.00
#> 204 2 0.327 0.9220 0.06 0.94
#> 205 2 0.000 0.9758 0.00 1.00
#> 206 2 0.000 0.9758 0.00 1.00
#> 207 2 0.000 0.9758 0.00 1.00
#> 208 1 0.000 0.9851 1.00 0.00
#> 209 2 0.000 0.9758 0.00 1.00
#> 210 2 0.000 0.9758 0.00 1.00
#> 211 2 0.000 0.9758 0.00 1.00
#> 212 1 0.000 0.9851 1.00 0.00
#> 213 1 0.000 0.9851 1.00 0.00
#> 214 2 0.000 0.9758 0.00 1.00
#> 215 1 0.795 0.6830 0.76 0.24
#> 216 1 0.000 0.9851 1.00 0.00
#> 217 2 0.990 0.2311 0.44 0.56
#> 218 1 0.000 0.9851 1.00 0.00
#> 219 1 0.000 0.9851 1.00 0.00
#> 220 2 0.000 0.9758 0.00 1.00
#> 221 1 0.141 0.9665 0.98 0.02
#> 222 1 0.000 0.9851 1.00 0.00
#> 223 2 0.000 0.9758 0.00 1.00
#> 224 2 0.000 0.9758 0.00 1.00
#> 225 2 0.000 0.9758 0.00 1.00
#> 226 2 0.000 0.9758 0.00 1.00
#> 227 1 0.000 0.9851 1.00 0.00
#> 228 2 0.000 0.9758 0.00 1.00
#> 229 1 0.634 0.8061 0.84 0.16
#> 230 1 0.000 0.9851 1.00 0.00
#> 231 1 0.000 0.9851 1.00 0.00
#> 232 1 0.000 0.9851 1.00 0.00
#> 233 1 0.000 0.9851 1.00 0.00
#> 234 1 0.000 0.9851 1.00 0.00
#> 235 1 0.000 0.9851 1.00 0.00
#> 236 1 0.000 0.9851 1.00 0.00
#> 237 2 0.000 0.9758 0.00 1.00
#> 238 1 0.000 0.9851 1.00 0.00
#> 239 2 0.000 0.9758 0.00 1.00
#> 240 2 0.000 0.9758 0.00 1.00
#> 241 2 1.000 0.0111 0.50 0.50
#> 242 2 0.000 0.9758 0.00 1.00
#> 243 1 0.000 0.9851 1.00 0.00
#> 244 1 0.000 0.9851 1.00 0.00
#> 245 1 0.000 0.9851 1.00 0.00
#> 246 1 0.000 0.9851 1.00 0.00
#> 247 1 0.000 0.9851 1.00 0.00
#> 248 1 0.000 0.9851 1.00 0.00
#> 249 1 0.000 0.9851 1.00 0.00
#> 250 2 0.958 0.4014 0.38 0.62
#> 251 1 0.000 0.9851 1.00 0.00
#> 252 2 0.242 0.9414 0.04 0.96
#> 253 1 0.000 0.9851 1.00 0.00
#> 254 2 0.000 0.9758 0.00 1.00
#> 255 1 0.000 0.9851 1.00 0.00
#> 256 1 0.000 0.9851 1.00 0.00
#> 257 1 0.000 0.9851 1.00 0.00
#> 258 1 0.000 0.9851 1.00 0.00
#> 259 1 0.000 0.9851 1.00 0.00
#> 260 1 0.000 0.9851 1.00 0.00
#> 261 1 0.000 0.9851 1.00 0.00
#> 262 1 0.000 0.9851 1.00 0.00
#> 263 1 0.000 0.9851 1.00 0.00
#> 264 1 0.000 0.9851 1.00 0.00
#> 265 1 0.000 0.9851 1.00 0.00
#> 266 1 0.634 0.8060 0.84 0.16
#> 267 1 0.000 0.9851 1.00 0.00
#> 268 1 0.000 0.9851 1.00 0.00
#> 269 1 0.000 0.9851 1.00 0.00
#> 270 1 0.000 0.9851 1.00 0.00
#> 271 1 0.000 0.9851 1.00 0.00
#> 272 1 0.000 0.9851 1.00 0.00
#> 273 1 0.000 0.9851 1.00 0.00
#> 274 1 0.000 0.9851 1.00 0.00
#> 275 1 0.000 0.9851 1.00 0.00
#> 276 2 0.000 0.9758 0.00 1.00
#> 277 1 0.402 0.9052 0.92 0.08
#> 278 1 0.000 0.9851 1.00 0.00
#> 279 1 0.000 0.9851 1.00 0.00
#> 280 1 0.000 0.9851 1.00 0.00
#> 281 2 0.000 0.9758 0.00 1.00
#> 282 1 0.000 0.9851 1.00 0.00
#> 283 1 0.000 0.9851 1.00 0.00
#> 284 1 0.000 0.9851 1.00 0.00
#> 285 1 0.000 0.9851 1.00 0.00
#> 286 1 0.000 0.9851 1.00 0.00
#> 287 2 0.680 0.7823 0.18 0.82
#> 288 1 0.000 0.9851 1.00 0.00
#> 289 1 0.000 0.9851 1.00 0.00
#> 290 1 0.000 0.9851 1.00 0.00
#> 291 1 0.000 0.9851 1.00 0.00
#> 292 2 0.000 0.9758 0.00 1.00
#> 293 2 0.000 0.9758 0.00 1.00
#> 294 2 0.680 0.7819 0.18 0.82
#> 295 2 0.000 0.9758 0.00 1.00
#> 296 2 0.000 0.9758 0.00 1.00
#> 297 2 0.000 0.9758 0.00 1.00
#> 298 2 0.000 0.9758 0.00 1.00
#> 299 2 0.000 0.9758 0.00 1.00
#> 300 1 0.000 0.9851 1.00 0.00
#> 301 2 0.000 0.9758 0.00 1.00
#> 302 1 0.000 0.9851 1.00 0.00
#> 303 1 0.000 0.9851 1.00 0.00
#> 304 2 0.000 0.9758 0.00 1.00
#> 305 2 0.000 0.9758 0.00 1.00
#> 306 2 0.000 0.9758 0.00 1.00
#> 307 1 0.000 0.9851 1.00 0.00
#> 308 1 0.000 0.9851 1.00 0.00
#> 309 1 0.000 0.9851 1.00 0.00
#> 310 1 0.000 0.9851 1.00 0.00
#> 311 1 0.000 0.9851 1.00 0.00
#> 312 1 0.000 0.9851 1.00 0.00
#> 313 2 0.141 0.9589 0.02 0.98
#> 314 1 0.943 0.4330 0.64 0.36
#> 315 1 0.000 0.9851 1.00 0.00
#> 316 1 0.000 0.9851 1.00 0.00
#> 317 2 0.242 0.9411 0.04 0.96
#> 318 1 0.000 0.9851 1.00 0.00
#> 319 2 0.000 0.9758 0.00 1.00
#> 320 2 0.000 0.9758 0.00 1.00
#> 321 2 0.000 0.9758 0.00 1.00
#> 322 2 0.000 0.9758 0.00 1.00
#> 323 1 0.000 0.9851 1.00 0.00
#> 324 1 0.000 0.9851 1.00 0.00
#> 325 1 0.000 0.9851 1.00 0.00
#> 326 1 0.141 0.9666 0.98 0.02
#> 327 1 0.000 0.9851 1.00 0.00
#> 328 2 0.000 0.9758 0.00 1.00
#> 329 1 0.000 0.9851 1.00 0.00
#> 330 1 0.000 0.9851 1.00 0.00
#> 331 2 0.000 0.9758 0.00 1.00
#> 332 1 0.000 0.9851 1.00 0.00
#> 333 1 0.000 0.9851 1.00 0.00
#> 334 2 0.000 0.9758 0.00 1.00
#> 335 2 0.000 0.9758 0.00 1.00
#> 336 1 0.000 0.9851 1.00 0.00
#> 337 2 0.000 0.9758 0.00 1.00
#> 338 2 0.000 0.9758 0.00 1.00
#> 339 1 0.000 0.9851 1.00 0.00
#> 340 1 0.000 0.9851 1.00 0.00
#> 341 1 0.000 0.9851 1.00 0.00
#> 342 1 0.000 0.9851 1.00 0.00
#> 343 1 0.000 0.9851 1.00 0.00
#> 344 1 0.000 0.9851 1.00 0.00
#> 345 1 0.000 0.9851 1.00 0.00
#> 346 1 0.000 0.9851 1.00 0.00
#> 347 1 0.000 0.9851 1.00 0.00
#> 348 1 0.000 0.9851 1.00 0.00
#> 349 1 0.000 0.9851 1.00 0.00
#> 350 1 0.000 0.9851 1.00 0.00
#> 351 1 0.000 0.9851 1.00 0.00
#> 352 1 0.000 0.9851 1.00 0.00
#> 353 1 0.000 0.9851 1.00 0.00
#> 354 2 0.000 0.9758 0.00 1.00
#> 355 2 0.000 0.9758 0.00 1.00
#> 356 2 0.000 0.9758 0.00 1.00
#> 357 2 0.242 0.9413 0.04 0.96
#> 358 2 0.958 0.4020 0.38 0.62
#> 359 1 0.000 0.9851 1.00 0.00
#> 360 1 0.958 0.3810 0.62 0.38
#> 361 2 0.000 0.9758 0.00 1.00
#> 362 1 0.000 0.9851 1.00 0.00
#> 363 1 0.000 0.9851 1.00 0.00
#> 364 1 0.000 0.9851 1.00 0.00
#> 365 1 0.634 0.8053 0.84 0.16
#> 366 1 0.000 0.9851 1.00 0.00
#> 367 1 0.000 0.9851 1.00 0.00
#> 368 1 0.000 0.9851 1.00 0.00
#> 369 1 0.000 0.9851 1.00 0.00
#> 370 1 0.000 0.9851 1.00 0.00
#> 371 1 0.000 0.9851 1.00 0.00
#> 372 1 0.000 0.9851 1.00 0.00
#> 373 1 0.000 0.9851 1.00 0.00
#> 374 2 0.000 0.9758 0.00 1.00
#> 375 1 0.000 0.9851 1.00 0.00
#> 376 1 0.000 0.9851 1.00 0.00
#> 377 1 0.680 0.7779 0.82 0.18
#> 378 1 0.141 0.9666 0.98 0.02
#> 379 1 0.000 0.9851 1.00 0.00
#> 380 2 0.000 0.9758 0.00 1.00
#> 381 2 0.000 0.9758 0.00 1.00
#> 382 1 0.000 0.9851 1.00 0.00
#> 383 1 0.000 0.9851 1.00 0.00
#> 384 1 0.000 0.9851 1.00 0.00
#> 385 1 0.000 0.9851 1.00 0.00
#> 386 1 0.000 0.9851 1.00 0.00
#> 387 1 0.000 0.9851 1.00 0.00
#> 388 1 0.000 0.9851 1.00 0.00
#> 389 2 0.000 0.9758 0.00 1.00
#> 390 2 0.000 0.9758 0.00 1.00
#> 391 2 0.000 0.9758 0.00 1.00
#> 392 1 0.000 0.9851 1.00 0.00
#> 393 2 0.000 0.9758 0.00 1.00
#> 394 1 0.000 0.9851 1.00 0.00
#> 395 2 0.000 0.9758 0.00 1.00
#> 396 2 0.000 0.9758 0.00 1.00
#> 397 2 0.000 0.9758 0.00 1.00
#> 398 2 0.000 0.9758 0.00 1.00
#> 399 2 0.000 0.9758 0.00 1.00
#> 400 2 0.000 0.9758 0.00 1.00
#> 401 1 0.904 0.5266 0.68 0.32
#> 402 2 0.000 0.9758 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.0000 0.970324 0.00 0.00 1.00
#> 2 2 0.0000 0.990731 0.00 1.00 0.00
#> 3 2 0.0000 0.990731 0.00 1.00 0.00
#> 4 1 0.5706 0.535731 0.68 0.32 0.00
#> 5 1 0.0000 0.983275 1.00 0.00 0.00
#> 6 1 0.0000 0.983275 1.00 0.00 0.00
#> 7 2 0.0000 0.990731 0.00 1.00 0.00
#> 8 2 0.0000 0.990731 0.00 1.00 0.00
#> 9 3 0.0000 0.970324 0.00 0.00 1.00
#> 10 1 0.0000 0.983275 1.00 0.00 0.00
#> 11 1 0.0000 0.983275 1.00 0.00 0.00
#> 12 3 0.0000 0.970324 0.00 0.00 1.00
#> 13 3 0.0000 0.970324 0.00 0.00 1.00
#> 14 3 0.0000 0.970324 0.00 0.00 1.00
#> 15 3 0.0000 0.970324 0.00 0.00 1.00
#> 16 3 0.0000 0.970324 0.00 0.00 1.00
#> 17 3 0.0000 0.970324 0.00 0.00 1.00
#> 18 3 0.0000 0.970324 0.00 0.00 1.00
#> 19 3 0.0000 0.970324 0.00 0.00 1.00
#> 20 3 0.0000 0.970324 0.00 0.00 1.00
#> 21 3 0.0000 0.970324 0.00 0.00 1.00
#> 22 3 0.0000 0.970324 0.00 0.00 1.00
#> 23 3 0.0000 0.970324 0.00 0.00 1.00
#> 24 3 0.0000 0.970324 0.00 0.00 1.00
#> 25 3 0.5706 0.537475 0.00 0.32 0.68
#> 26 3 0.0000 0.970324 0.00 0.00 1.00
#> 27 2 0.0000 0.990731 0.00 1.00 0.00
#> 28 3 0.0000 0.970324 0.00 0.00 1.00
#> 29 3 0.0000 0.970324 0.00 0.00 1.00
#> 30 3 0.0000 0.970324 0.00 0.00 1.00
#> 31 3 0.0000 0.970324 0.00 0.00 1.00
#> 32 3 0.0000 0.970324 0.00 0.00 1.00
#> 33 2 0.0000 0.990731 0.00 1.00 0.00
#> 34 3 0.0000 0.970324 0.00 0.00 1.00
#> 35 2 0.0000 0.990731 0.00 1.00 0.00
#> 36 3 0.0000 0.970324 0.00 0.00 1.00
#> 37 2 0.0000 0.990731 0.00 1.00 0.00
#> 38 2 0.0000 0.990731 0.00 1.00 0.00
#> 39 2 0.0000 0.990731 0.00 1.00 0.00
#> 40 2 0.0000 0.990731 0.00 1.00 0.00
#> 41 2 0.0000 0.990731 0.00 1.00 0.00
#> 42 3 0.0000 0.970324 0.00 0.00 1.00
#> 43 2 0.0000 0.990731 0.00 1.00 0.00
#> 44 3 0.0000 0.970324 0.00 0.00 1.00
#> 45 2 0.0000 0.990731 0.00 1.00 0.00
#> 46 2 0.0000 0.990731 0.00 1.00 0.00
#> 47 3 0.0000 0.970324 0.00 0.00 1.00
#> 48 3 0.0000 0.970324 0.00 0.00 1.00
#> 49 3 0.0000 0.970324 0.00 0.00 1.00
#> 50 2 0.0000 0.990731 0.00 1.00 0.00
#> 51 3 0.0000 0.970324 0.00 0.00 1.00
#> 52 3 0.6045 0.397457 0.00 0.38 0.62
#> 53 2 0.0000 0.990731 0.00 1.00 0.00
#> 54 2 0.0000 0.990731 0.00 1.00 0.00
#> 55 3 0.0000 0.970324 0.00 0.00 1.00
#> 56 2 0.0000 0.990731 0.00 1.00 0.00
#> 57 3 0.0000 0.970324 0.00 0.00 1.00
#> 58 2 0.0000 0.990731 0.00 1.00 0.00
#> 59 3 0.0000 0.970324 0.00 0.00 1.00
#> 60 2 0.0000 0.990731 0.00 1.00 0.00
#> 61 2 0.0000 0.990731 0.00 1.00 0.00
#> 62 3 0.0000 0.970324 0.00 0.00 1.00
#> 63 3 0.0000 0.970324 0.00 0.00 1.00
#> 64 2 0.0000 0.990731 0.00 1.00 0.00
#> 65 3 0.0000 0.970324 0.00 0.00 1.00
#> 66 2 0.0000 0.990731 0.00 1.00 0.00
#> 67 2 0.0000 0.990731 0.00 1.00 0.00
#> 68 3 0.0000 0.970324 0.00 0.00 1.00
#> 69 2 0.0000 0.990731 0.00 1.00 0.00
#> 70 3 0.0000 0.970324 0.00 0.00 1.00
#> 71 3 0.0000 0.970324 0.00 0.00 1.00
#> 72 3 0.0000 0.970324 0.00 0.00 1.00
#> 73 2 0.0000 0.990731 0.00 1.00 0.00
#> 74 2 0.0000 0.990731 0.00 1.00 0.00
#> 75 2 0.0000 0.990731 0.00 1.00 0.00
#> 76 2 0.0000 0.990731 0.00 1.00 0.00
#> 77 2 0.0000 0.990731 0.00 1.00 0.00
#> 78 3 0.0000 0.970324 0.00 0.00 1.00
#> 79 3 0.3686 0.827502 0.00 0.14 0.86
#> 80 2 0.4796 0.712150 0.00 0.78 0.22
#> 81 2 0.0000 0.990731 0.00 1.00 0.00
#> 82 3 0.0000 0.970324 0.00 0.00 1.00
#> 83 3 0.0000 0.970324 0.00 0.00 1.00
#> 84 2 0.0000 0.990731 0.00 1.00 0.00
#> 85 3 0.0000 0.970324 0.00 0.00 1.00
#> 86 3 0.0000 0.970324 0.00 0.00 1.00
#> 87 3 0.0000 0.970324 0.00 0.00 1.00
#> 88 3 0.0000 0.970324 0.00 0.00 1.00
#> 89 3 0.0000 0.970324 0.00 0.00 1.00
#> 90 3 0.0000 0.970324 0.00 0.00 1.00
#> 91 3 0.0000 0.970324 0.00 0.00 1.00
#> 92 3 0.0000 0.970324 0.00 0.00 1.00
#> 93 3 0.6126 0.347891 0.00 0.40 0.60
#> 94 3 0.0000 0.970324 0.00 0.00 1.00
#> 95 3 0.0000 0.970324 0.00 0.00 1.00
#> 96 3 0.0000 0.970324 0.00 0.00 1.00
#> 97 3 0.0000 0.970324 0.00 0.00 1.00
#> 98 3 0.1529 0.936747 0.04 0.00 0.96
#> 99 3 0.0000 0.970324 0.00 0.00 1.00
#> 100 3 0.0000 0.970324 0.00 0.00 1.00
#> 101 2 0.0000 0.990731 0.00 1.00 0.00
#> 102 2 0.0000 0.990731 0.00 1.00 0.00
#> 103 2 0.0000 0.990731 0.00 1.00 0.00
#> 104 2 0.0000 0.990731 0.00 1.00 0.00
#> 105 2 0.0000 0.990731 0.00 1.00 0.00
#> 106 2 0.0000 0.990731 0.00 1.00 0.00
#> 107 2 0.0000 0.990731 0.00 1.00 0.00
#> 108 2 0.0000 0.990731 0.00 1.00 0.00
#> 109 2 0.0000 0.990731 0.00 1.00 0.00
#> 110 2 0.0000 0.990731 0.00 1.00 0.00
#> 111 2 0.0000 0.990731 0.00 1.00 0.00
#> 112 3 0.0000 0.970324 0.00 0.00 1.00
#> 113 3 0.0000 0.970324 0.00 0.00 1.00
#> 114 3 0.0000 0.970324 0.00 0.00 1.00
#> 115 2 0.0000 0.990731 0.00 1.00 0.00
#> 116 2 0.0892 0.970787 0.00 0.98 0.02
#> 117 2 0.0000 0.990731 0.00 1.00 0.00
#> 118 3 0.0000 0.970324 0.00 0.00 1.00
#> 119 3 0.0000 0.970324 0.00 0.00 1.00
#> 120 3 0.0000 0.970324 0.00 0.00 1.00
#> 121 2 0.0000 0.990731 0.00 1.00 0.00
#> 122 3 0.0000 0.970324 0.00 0.00 1.00
#> 123 3 0.0000 0.970324 0.00 0.00 1.00
#> 124 3 0.0000 0.970324 0.00 0.00 1.00
#> 125 2 0.0000 0.990731 0.00 1.00 0.00
#> 126 3 0.0000 0.970324 0.00 0.00 1.00
#> 127 1 0.6192 0.266280 0.58 0.00 0.42
#> 128 3 0.2537 0.898223 0.08 0.00 0.92
#> 129 3 0.0000 0.970324 0.00 0.00 1.00
#> 130 3 0.5216 0.652394 0.00 0.26 0.74
#> 131 3 0.0000 0.970324 0.00 0.00 1.00
#> 132 3 0.0000 0.970324 0.00 0.00 1.00
#> 133 3 0.0000 0.970324 0.00 0.00 1.00
#> 134 3 0.0000 0.970324 0.00 0.00 1.00
#> 135 3 0.0000 0.970324 0.00 0.00 1.00
#> 136 3 0.0000 0.970324 0.00 0.00 1.00
#> 137 3 0.0000 0.970324 0.00 0.00 1.00
#> 138 3 0.0000 0.970324 0.00 0.00 1.00
#> 139 1 0.0000 0.983275 1.00 0.00 0.00
#> 140 1 0.3686 0.830851 0.86 0.00 0.14
#> 141 3 0.0000 0.970324 0.00 0.00 1.00
#> 142 3 0.0000 0.970324 0.00 0.00 1.00
#> 143 3 0.0000 0.970324 0.00 0.00 1.00
#> 144 3 0.0000 0.970324 0.00 0.00 1.00
#> 145 3 0.0000 0.970324 0.00 0.00 1.00
#> 146 2 0.0000 0.990731 0.00 1.00 0.00
#> 147 2 0.0000 0.990731 0.00 1.00 0.00
#> 148 2 0.0000 0.990731 0.00 1.00 0.00
#> 149 2 0.0000 0.990731 0.00 1.00 0.00
#> 150 2 0.0000 0.990731 0.00 1.00 0.00
#> 151 3 0.0000 0.970324 0.00 0.00 1.00
#> 152 3 0.0000 0.970324 0.00 0.00 1.00
#> 153 3 0.0000 0.970324 0.00 0.00 1.00
#> 154 3 0.0000 0.970324 0.00 0.00 1.00
#> 155 3 0.0000 0.970324 0.00 0.00 1.00
#> 156 3 0.1529 0.936747 0.04 0.00 0.96
#> 157 2 0.0000 0.990731 0.00 1.00 0.00
#> 158 2 0.0000 0.990731 0.00 1.00 0.00
#> 159 2 0.0000 0.990731 0.00 1.00 0.00
#> 160 3 0.0000 0.970324 0.00 0.00 1.00
#> 161 2 0.0000 0.990731 0.00 1.00 0.00
#> 162 2 0.0000 0.990731 0.00 1.00 0.00
#> 163 2 0.0000 0.990731 0.00 1.00 0.00
#> 164 2 0.0000 0.990731 0.00 1.00 0.00
#> 165 2 0.0000 0.990731 0.00 1.00 0.00
#> 166 2 0.0000 0.990731 0.00 1.00 0.00
#> 167 2 0.0000 0.990731 0.00 1.00 0.00
#> 168 2 0.0000 0.990731 0.00 1.00 0.00
#> 169 3 0.2537 0.897786 0.08 0.00 0.92
#> 170 1 0.0000 0.983275 1.00 0.00 0.00
#> 171 1 0.0000 0.983275 1.00 0.00 0.00
#> 172 1 0.0000 0.983275 1.00 0.00 0.00
#> 173 1 0.0000 0.983275 1.00 0.00 0.00
#> 174 1 0.0000 0.983275 1.00 0.00 0.00
#> 175 1 0.5560 0.568672 0.70 0.00 0.30
#> 176 3 0.4555 0.745640 0.20 0.00 0.80
#> 177 1 0.0000 0.983275 1.00 0.00 0.00
#> 178 1 0.4002 0.804073 0.84 0.00 0.16
#> 179 1 0.0000 0.983275 1.00 0.00 0.00
#> 180 1 0.0892 0.964805 0.98 0.00 0.02
#> 181 1 0.0000 0.983275 1.00 0.00 0.00
#> 182 1 0.0000 0.983275 1.00 0.00 0.00
#> 183 1 0.0000 0.983275 1.00 0.00 0.00
#> 184 3 0.2959 0.875908 0.10 0.00 0.90
#> 185 1 0.0000 0.983275 1.00 0.00 0.00
#> 186 1 0.0000 0.983275 1.00 0.00 0.00
#> 187 1 0.0000 0.983275 1.00 0.00 0.00
#> 188 1 0.0000 0.983275 1.00 0.00 0.00
#> 189 2 0.0000 0.990731 0.00 1.00 0.00
#> 190 2 0.0000 0.990731 0.00 1.00 0.00
#> 191 2 0.0000 0.990731 0.00 1.00 0.00
#> 192 1 0.0000 0.983275 1.00 0.00 0.00
#> 193 1 0.0000 0.983275 1.00 0.00 0.00
#> 194 2 0.2537 0.903878 0.08 0.92 0.00
#> 195 2 0.0000 0.990731 0.00 1.00 0.00
#> 196 2 0.0000 0.990731 0.00 1.00 0.00
#> 197 1 0.2537 0.902787 0.92 0.00 0.08
#> 198 2 0.0000 0.990731 0.00 1.00 0.00
#> 199 1 0.0000 0.983275 1.00 0.00 0.00
#> 200 1 0.0000 0.983275 1.00 0.00 0.00
#> 201 2 0.0000 0.990731 0.00 1.00 0.00
#> 202 2 0.0000 0.990731 0.00 1.00 0.00
#> 203 2 0.0000 0.990731 0.00 1.00 0.00
#> 204 1 0.0892 0.964451 0.98 0.02 0.00
#> 205 2 0.0000 0.990731 0.00 1.00 0.00
#> 206 2 0.0000 0.990731 0.00 1.00 0.00
#> 207 2 0.0000 0.990731 0.00 1.00 0.00
#> 208 1 0.0000 0.983275 1.00 0.00 0.00
#> 209 2 0.0000 0.990731 0.00 1.00 0.00
#> 210 2 0.0000 0.990731 0.00 1.00 0.00
#> 211 2 0.0000 0.990731 0.00 1.00 0.00
#> 212 1 0.0000 0.983275 1.00 0.00 0.00
#> 213 1 0.0000 0.983275 1.00 0.00 0.00
#> 214 2 0.0000 0.990731 0.00 1.00 0.00
#> 215 1 0.1529 0.945241 0.96 0.04 0.00
#> 216 1 0.0000 0.983275 1.00 0.00 0.00
#> 217 1 0.0000 0.983275 1.00 0.00 0.00
#> 218 1 0.0000 0.983275 1.00 0.00 0.00
#> 219 1 0.0000 0.983275 1.00 0.00 0.00
#> 220 2 0.0000 0.990731 0.00 1.00 0.00
#> 221 1 0.0000 0.983275 1.00 0.00 0.00
#> 222 1 0.0000 0.983275 1.00 0.00 0.00
#> 223 2 0.0000 0.990731 0.00 1.00 0.00
#> 224 2 0.0000 0.990731 0.00 1.00 0.00
#> 225 2 0.0000 0.990731 0.00 1.00 0.00
#> 226 2 0.0000 0.990731 0.00 1.00 0.00
#> 227 1 0.0000 0.983275 1.00 0.00 0.00
#> 228 2 0.0000 0.990731 0.00 1.00 0.00
#> 229 1 0.0000 0.983275 1.00 0.00 0.00
#> 230 1 0.0000 0.983275 1.00 0.00 0.00
#> 231 1 0.0000 0.983275 1.00 0.00 0.00
#> 232 1 0.0000 0.983275 1.00 0.00 0.00
#> 233 1 0.0000 0.983275 1.00 0.00 0.00
#> 234 1 0.0000 0.983275 1.00 0.00 0.00
#> 235 1 0.0000 0.983275 1.00 0.00 0.00
#> 236 1 0.0000 0.983275 1.00 0.00 0.00
#> 237 2 0.0000 0.990731 0.00 1.00 0.00
#> 238 1 0.0000 0.983275 1.00 0.00 0.00
#> 239 2 0.0000 0.990731 0.00 1.00 0.00
#> 240 2 0.0000 0.990731 0.00 1.00 0.00
#> 241 3 0.4555 0.749699 0.00 0.20 0.80
#> 242 2 0.0000 0.990731 0.00 1.00 0.00
#> 243 1 0.0000 0.983275 1.00 0.00 0.00
#> 244 1 0.0000 0.983275 1.00 0.00 0.00
#> 245 1 0.0000 0.983275 1.00 0.00 0.00
#> 246 1 0.0000 0.983275 1.00 0.00 0.00
#> 247 1 0.0000 0.983275 1.00 0.00 0.00
#> 248 1 0.0000 0.983275 1.00 0.00 0.00
#> 249 1 0.0000 0.983275 1.00 0.00 0.00
#> 250 2 0.7464 0.246777 0.04 0.56 0.40
#> 251 1 0.0000 0.983275 1.00 0.00 0.00
#> 252 1 0.0000 0.983275 1.00 0.00 0.00
#> 253 3 0.6280 0.153099 0.46 0.00 0.54
#> 254 2 0.0000 0.990731 0.00 1.00 0.00
#> 255 3 0.1529 0.936768 0.04 0.00 0.96
#> 256 3 0.0000 0.970324 0.00 0.00 1.00
#> 257 3 0.0000 0.970324 0.00 0.00 1.00
#> 258 3 0.0000 0.970324 0.00 0.00 1.00
#> 259 1 0.0000 0.983275 1.00 0.00 0.00
#> 260 1 0.0000 0.983275 1.00 0.00 0.00
#> 261 1 0.0000 0.983275 1.00 0.00 0.00
#> 262 1 0.0000 0.983275 1.00 0.00 0.00
#> 263 3 0.0892 0.953633 0.02 0.00 0.98
#> 264 1 0.0000 0.983275 1.00 0.00 0.00
#> 265 1 0.0000 0.983275 1.00 0.00 0.00
#> 266 1 0.0000 0.983275 1.00 0.00 0.00
#> 267 1 0.0000 0.983275 1.00 0.00 0.00
#> 268 1 0.0000 0.983275 1.00 0.00 0.00
#> 269 1 0.0000 0.983275 1.00 0.00 0.00
#> 270 3 0.0000 0.970324 0.00 0.00 1.00
#> 271 1 0.0000 0.983275 1.00 0.00 0.00
#> 272 1 0.0000 0.983275 1.00 0.00 0.00
#> 273 1 0.0000 0.983275 1.00 0.00 0.00
#> 274 1 0.0000 0.983275 1.00 0.00 0.00
#> 275 1 0.0000 0.983275 1.00 0.00 0.00
#> 276 2 0.0000 0.990731 0.00 1.00 0.00
#> 277 1 0.7138 0.677857 0.72 0.12 0.16
#> 278 1 0.0000 0.983275 1.00 0.00 0.00
#> 279 1 0.0000 0.983275 1.00 0.00 0.00
#> 280 1 0.0000 0.983275 1.00 0.00 0.00
#> 281 2 0.0000 0.990731 0.00 1.00 0.00
#> 282 1 0.0000 0.983275 1.00 0.00 0.00
#> 283 1 0.0000 0.983275 1.00 0.00 0.00
#> 284 1 0.0000 0.983275 1.00 0.00 0.00
#> 285 1 0.0000 0.983275 1.00 0.00 0.00
#> 286 1 0.0000 0.983275 1.00 0.00 0.00
#> 287 3 0.3340 0.852654 0.00 0.12 0.88
#> 288 1 0.0000 0.983275 1.00 0.00 0.00
#> 289 1 0.0000 0.983275 1.00 0.00 0.00
#> 290 1 0.0000 0.983275 1.00 0.00 0.00
#> 291 1 0.0000 0.983275 1.00 0.00 0.00
#> 292 2 0.0000 0.990731 0.00 1.00 0.00
#> 293 2 0.0000 0.990731 0.00 1.00 0.00
#> 294 1 0.0892 0.964451 0.98 0.02 0.00
#> 295 2 0.0000 0.990731 0.00 1.00 0.00
#> 296 2 0.0000 0.990731 0.00 1.00 0.00
#> 297 2 0.0000 0.990731 0.00 1.00 0.00
#> 298 1 0.0000 0.983275 1.00 0.00 0.00
#> 299 1 0.5560 0.576687 0.70 0.30 0.00
#> 300 1 0.0000 0.983275 1.00 0.00 0.00
#> 301 2 0.0000 0.990731 0.00 1.00 0.00
#> 302 1 0.0000 0.983275 1.00 0.00 0.00
#> 303 1 0.0000 0.983275 1.00 0.00 0.00
#> 304 2 0.0000 0.990731 0.00 1.00 0.00
#> 305 2 0.0000 0.990731 0.00 1.00 0.00
#> 306 2 0.0000 0.990731 0.00 1.00 0.00
#> 307 1 0.0000 0.983275 1.00 0.00 0.00
#> 308 1 0.0000 0.983275 1.00 0.00 0.00
#> 309 1 0.0000 0.983275 1.00 0.00 0.00
#> 310 1 0.0000 0.983275 1.00 0.00 0.00
#> 311 1 0.0000 0.983275 1.00 0.00 0.00
#> 312 1 0.0000 0.983275 1.00 0.00 0.00
#> 313 1 0.0000 0.983275 1.00 0.00 0.00
#> 314 1 0.0000 0.983275 1.00 0.00 0.00
#> 315 1 0.0000 0.983275 1.00 0.00 0.00
#> 316 1 0.0000 0.983275 1.00 0.00 0.00
#> 317 1 0.0000 0.983275 1.00 0.00 0.00
#> 318 1 0.0000 0.983275 1.00 0.00 0.00
#> 319 1 0.2066 0.924520 0.94 0.06 0.00
#> 320 2 0.0000 0.990731 0.00 1.00 0.00
#> 321 2 0.0000 0.990731 0.00 1.00 0.00
#> 322 2 0.0000 0.990731 0.00 1.00 0.00
#> 323 1 0.0000 0.983275 1.00 0.00 0.00
#> 324 1 0.0000 0.983275 1.00 0.00 0.00
#> 325 1 0.0000 0.983275 1.00 0.00 0.00
#> 326 1 0.0000 0.983275 1.00 0.00 0.00
#> 327 1 0.0000 0.983275 1.00 0.00 0.00
#> 328 2 0.1529 0.948482 0.04 0.96 0.00
#> 329 1 0.0000 0.983275 1.00 0.00 0.00
#> 330 1 0.0000 0.983275 1.00 0.00 0.00
#> 331 2 0.0000 0.990731 0.00 1.00 0.00
#> 332 1 0.0000 0.983275 1.00 0.00 0.00
#> 333 1 0.0000 0.983275 1.00 0.00 0.00
#> 334 2 0.0000 0.990731 0.00 1.00 0.00
#> 335 2 0.0000 0.990731 0.00 1.00 0.00
#> 336 1 0.0000 0.983275 1.00 0.00 0.00
#> 337 2 0.0000 0.990731 0.00 1.00 0.00
#> 338 1 0.0000 0.983275 1.00 0.00 0.00
#> 339 1 0.0000 0.983275 1.00 0.00 0.00
#> 340 1 0.0000 0.983275 1.00 0.00 0.00
#> 341 1 0.0000 0.983275 1.00 0.00 0.00
#> 342 1 0.0000 0.983275 1.00 0.00 0.00
#> 343 1 0.0000 0.983275 1.00 0.00 0.00
#> 344 1 0.0000 0.983275 1.00 0.00 0.00
#> 345 1 0.0000 0.983275 1.00 0.00 0.00
#> 346 1 0.0000 0.983275 1.00 0.00 0.00
#> 347 1 0.0000 0.983275 1.00 0.00 0.00
#> 348 1 0.0000 0.983275 1.00 0.00 0.00
#> 349 1 0.0000 0.983275 1.00 0.00 0.00
#> 350 1 0.0000 0.983275 1.00 0.00 0.00
#> 351 1 0.0000 0.983275 1.00 0.00 0.00
#> 352 1 0.0000 0.983275 1.00 0.00 0.00
#> 353 1 0.0000 0.983275 1.00 0.00 0.00
#> 354 2 0.0000 0.990731 0.00 1.00 0.00
#> 355 2 0.0000 0.990731 0.00 1.00 0.00
#> 356 2 0.0000 0.990731 0.00 1.00 0.00
#> 357 1 0.0000 0.983275 1.00 0.00 0.00
#> 358 1 0.0000 0.983275 1.00 0.00 0.00
#> 359 3 0.0000 0.970324 0.00 0.00 1.00
#> 360 3 0.0000 0.970324 0.00 0.00 1.00
#> 361 2 0.0000 0.990731 0.00 1.00 0.00
#> 362 1 0.0000 0.983275 1.00 0.00 0.00
#> 363 3 0.0000 0.970324 0.00 0.00 1.00
#> 364 3 0.6309 0.000536 0.50 0.00 0.50
#> 365 1 0.0000 0.983275 1.00 0.00 0.00
#> 366 1 0.0000 0.983275 1.00 0.00 0.00
#> 367 1 0.0000 0.983275 1.00 0.00 0.00
#> 368 1 0.0000 0.983275 1.00 0.00 0.00
#> 369 1 0.0000 0.983275 1.00 0.00 0.00
#> 370 1 0.0000 0.983275 1.00 0.00 0.00
#> 371 3 0.0000 0.970324 0.00 0.00 1.00
#> 372 3 0.0000 0.970324 0.00 0.00 1.00
#> 373 1 0.0000 0.983275 1.00 0.00 0.00
#> 374 2 0.5948 0.432250 0.36 0.64 0.00
#> 375 1 0.0000 0.983275 1.00 0.00 0.00
#> 376 1 0.0000 0.983275 1.00 0.00 0.00
#> 377 3 0.0000 0.970324 0.00 0.00 1.00
#> 378 1 0.0000 0.983275 1.00 0.00 0.00
#> 379 1 0.0000 0.983275 1.00 0.00 0.00
#> 380 2 0.0000 0.990731 0.00 1.00 0.00
#> 381 2 0.0000 0.990731 0.00 1.00 0.00
#> 382 1 0.0000 0.983275 1.00 0.00 0.00
#> 383 3 0.0000 0.970324 0.00 0.00 1.00
#> 384 3 0.0000 0.970324 0.00 0.00 1.00
#> 385 3 0.0000 0.970324 0.00 0.00 1.00
#> 386 3 0.0000 0.970324 0.00 0.00 1.00
#> 387 1 0.0000 0.983275 1.00 0.00 0.00
#> 388 3 0.0000 0.970324 0.00 0.00 1.00
#> 389 2 0.0000 0.990731 0.00 1.00 0.00
#> 390 2 0.0000 0.990731 0.00 1.00 0.00
#> 391 2 0.0000 0.990731 0.00 1.00 0.00
#> 392 1 0.0000 0.983275 1.00 0.00 0.00
#> 393 2 0.0000 0.990731 0.00 1.00 0.00
#> 394 1 0.0000 0.983275 1.00 0.00 0.00
#> 395 2 0.0000 0.990731 0.00 1.00 0.00
#> 396 1 0.5706 0.536958 0.68 0.32 0.00
#> 397 2 0.0000 0.990731 0.00 1.00 0.00
#> 398 2 0.0000 0.990731 0.00 1.00 0.00
#> 399 2 0.0000 0.990731 0.00 1.00 0.00
#> 400 2 0.0000 0.990731 0.00 1.00 0.00
#> 401 1 0.0000 0.983275 1.00 0.00 0.00
#> 402 2 0.0000 0.990731 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.4277 0.6090 0.28 0.00 0.72 0.00
#> 2 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 3 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 4 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 5 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 6 1 0.5355 0.3621 0.62 0.00 0.02 0.36
#> 7 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 8 4 0.3610 0.7095 0.00 0.20 0.00 0.80
#> 9 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 10 1 0.0707 0.9349 0.98 0.00 0.00 0.02
#> 11 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 12 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 13 3 0.0707 0.9427 0.02 0.00 0.98 0.00
#> 14 3 0.0707 0.9438 0.02 0.00 0.98 0.00
#> 15 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 16 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 17 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 18 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 19 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 20 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 21 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 22 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 23 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 24 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 25 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 26 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 27 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 28 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 29 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 30 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 31 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 32 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 33 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 34 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 35 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 36 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 37 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 38 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 39 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 40 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 41 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 42 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 43 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 44 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 45 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 46 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 47 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 48 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 49 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 50 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 51 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 52 3 0.2647 0.8295 0.00 0.12 0.88 0.00
#> 53 2 0.1211 0.9549 0.00 0.96 0.04 0.00
#> 54 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 55 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 56 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 57 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 58 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 59 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 60 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 61 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 62 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 63 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 64 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 65 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 66 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 67 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 68 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 69 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 70 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 71 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 72 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 73 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 74 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 75 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 76 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 77 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 78 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 79 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 80 3 0.2345 0.8526 0.00 0.10 0.90 0.00
#> 81 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 82 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 83 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 84 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 85 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 86 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 87 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 88 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 89 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 90 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 91 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 92 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 93 2 0.4894 0.7470 0.12 0.78 0.10 0.00
#> 94 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 95 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 96 1 0.4790 0.3695 0.62 0.00 0.38 0.00
#> 97 1 0.0707 0.9334 0.98 0.00 0.02 0.00
#> 98 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 99 3 0.0707 0.9436 0.02 0.00 0.98 0.00
#> 100 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 101 2 0.0707 0.9742 0.02 0.98 0.00 0.00
#> 102 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 103 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 104 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 105 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 106 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 107 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 108 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 109 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 110 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 111 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 112 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 113 1 0.2345 0.8524 0.90 0.00 0.10 0.00
#> 114 3 0.4406 0.5800 0.30 0.00 0.70 0.00
#> 115 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 116 2 0.1211 0.9551 0.00 0.96 0.04 0.00
#> 117 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 118 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 119 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 120 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 121 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 122 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 123 1 0.0707 0.9334 0.98 0.00 0.02 0.00
#> 124 1 0.3801 0.7007 0.78 0.00 0.22 0.00
#> 125 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 126 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 127 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 128 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 129 1 0.0707 0.9341 0.98 0.00 0.02 0.00
#> 130 3 0.4522 0.5290 0.00 0.32 0.68 0.00
#> 131 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 132 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 133 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 134 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 135 3 0.4994 0.0945 0.48 0.00 0.52 0.00
#> 136 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 137 3 0.1211 0.9239 0.04 0.00 0.96 0.00
#> 138 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 139 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 140 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 141 3 0.4790 0.4022 0.38 0.00 0.62 0.00
#> 142 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 143 1 0.0707 0.9334 0.98 0.00 0.02 0.00
#> 144 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 145 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 146 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 147 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 148 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 149 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 150 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 151 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 152 3 0.0707 0.9424 0.00 0.02 0.98 0.00
#> 153 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 154 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 155 3 0.4134 0.6501 0.26 0.00 0.74 0.00
#> 156 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 157 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 158 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 159 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 160 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 161 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 162 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 163 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 164 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 165 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 166 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 167 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 168 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 169 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 170 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 171 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 172 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 173 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 174 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 175 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 176 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 177 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 178 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 179 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 180 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 181 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 182 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 183 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 184 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 185 4 0.4134 0.6596 0.26 0.00 0.00 0.74
#> 186 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 187 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 188 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 189 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 190 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 191 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 192 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 193 4 0.0707 0.9067 0.02 0.00 0.00 0.98
#> 194 4 0.3610 0.7098 0.00 0.20 0.00 0.80
#> 195 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 196 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 197 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 198 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 199 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 200 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 201 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 202 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 203 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 204 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 205 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 206 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 207 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 208 4 0.0707 0.9067 0.02 0.00 0.00 0.98
#> 209 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 210 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 211 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 212 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 213 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 214 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 215 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 216 1 0.0707 0.9350 0.98 0.00 0.00 0.02
#> 217 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 218 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 219 1 0.2011 0.8824 0.92 0.00 0.00 0.08
#> 220 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 221 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 222 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 223 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 224 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 225 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 226 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 227 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 228 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 229 4 0.3172 0.7897 0.16 0.00 0.00 0.84
#> 230 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 231 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 232 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 233 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 234 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 235 1 0.3172 0.7854 0.84 0.00 0.00 0.16
#> 236 1 0.3172 0.7822 0.84 0.00 0.00 0.16
#> 237 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 238 4 0.3801 0.7150 0.22 0.00 0.00 0.78
#> 239 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 240 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 241 1 0.3525 0.8093 0.86 0.10 0.04 0.00
#> 242 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 243 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 244 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 245 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 246 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 247 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 248 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 249 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 250 1 0.4406 0.5513 0.70 0.30 0.00 0.00
#> 251 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 252 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 253 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 254 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 255 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 256 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 257 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 258 1 0.2011 0.8758 0.92 0.00 0.08 0.00
#> 259 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 260 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 261 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 262 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 263 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 264 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 265 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 266 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 267 4 0.0707 0.9067 0.02 0.00 0.00 0.98
#> 268 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 269 1 0.2345 0.8577 0.90 0.00 0.00 0.10
#> 270 1 0.1211 0.9153 0.96 0.00 0.04 0.00
#> 271 4 0.0707 0.9067 0.02 0.00 0.00 0.98
#> 272 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 273 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 274 4 0.4522 0.5634 0.32 0.00 0.00 0.68
#> 275 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 276 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 277 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 278 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 279 4 0.0707 0.9067 0.02 0.00 0.00 0.98
#> 280 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 281 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 282 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 283 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 284 1 0.2647 0.8335 0.88 0.00 0.00 0.12
#> 285 1 0.0707 0.9350 0.98 0.00 0.00 0.02
#> 286 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 287 3 0.7653 0.2870 0.30 0.24 0.46 0.00
#> 288 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 289 1 0.1637 0.8992 0.94 0.00 0.00 0.06
#> 290 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 291 1 0.2921 0.8090 0.86 0.00 0.00 0.14
#> 292 2 0.0707 0.9781 0.00 0.98 0.00 0.02
#> 293 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 294 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 295 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 296 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 297 2 0.1211 0.9584 0.00 0.96 0.00 0.04
#> 298 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 299 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 300 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 301 2 0.2011 0.9146 0.00 0.92 0.00 0.08
#> 302 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 303 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 304 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 305 2 0.0707 0.9781 0.00 0.98 0.00 0.02
#> 306 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 307 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 308 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 309 4 0.4855 0.3886 0.40 0.00 0.00 0.60
#> 310 4 0.4855 0.3919 0.40 0.00 0.00 0.60
#> 311 4 0.4994 0.1441 0.48 0.00 0.00 0.52
#> 312 4 0.1637 0.8806 0.06 0.00 0.00 0.94
#> 313 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 314 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 315 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 316 1 0.1637 0.8995 0.94 0.00 0.00 0.06
#> 317 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 318 4 0.0707 0.9067 0.02 0.00 0.00 0.98
#> 319 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 320 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 321 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 322 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 323 4 0.1211 0.8949 0.04 0.00 0.00 0.96
#> 324 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 325 4 0.1637 0.8807 0.06 0.00 0.00 0.94
#> 326 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 327 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 328 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 329 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 330 4 0.4907 0.3364 0.42 0.00 0.00 0.58
#> 331 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 332 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 333 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 334 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 335 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 336 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 337 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 338 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 339 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 340 4 0.4855 0.3874 0.40 0.00 0.00 0.60
#> 341 4 0.4406 0.5963 0.30 0.00 0.00 0.70
#> 342 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 343 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 344 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 345 1 0.4134 0.6188 0.74 0.00 0.00 0.26
#> 346 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 347 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 348 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 349 1 0.4948 0.1487 0.56 0.00 0.00 0.44
#> 350 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 351 4 0.0707 0.9067 0.02 0.00 0.00 0.98
#> 352 1 0.4948 0.1487 0.56 0.00 0.00 0.44
#> 353 1 0.4948 0.1542 0.56 0.00 0.00 0.44
#> 354 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 355 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 356 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 357 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 358 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 359 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 360 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 361 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 362 4 0.4855 0.3887 0.40 0.00 0.00 0.60
#> 363 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 364 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 365 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 366 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 367 4 0.4522 0.5617 0.32 0.00 0.00 0.68
#> 368 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 369 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 370 1 0.4907 0.2165 0.58 0.00 0.00 0.42
#> 371 1 0.2921 0.8066 0.86 0.00 0.14 0.00
#> 372 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 373 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 374 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 375 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 376 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 377 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 378 4 0.4977 0.1980 0.46 0.00 0.00 0.54
#> 379 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 380 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 381 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 382 1 0.4713 0.3971 0.64 0.00 0.00 0.36
#> 383 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 384 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 385 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 386 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 387 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 388 3 0.0000 0.9623 0.00 0.00 1.00 0.00
#> 389 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 390 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 391 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 392 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 393 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 394 1 0.0000 0.9505 1.00 0.00 0.00 0.00
#> 395 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 396 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 397 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 398 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 399 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 400 2 0.0000 0.9960 0.00 1.00 0.00 0.00
#> 401 4 0.0000 0.9150 0.00 0.00 0.00 1.00
#> 402 2 0.0000 0.9960 0.00 1.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 394 0.0985 2
#> ATC:skmeans 395 0.1796 3
#> ATC:skmeans 385 0.3778 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node031. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0311"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 8477 rows and 153 columns.
#> Top rows (848) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.973 0.964 0.985 0.492 0.508 0.508
#> 3 3 0.957 0.937 0.976 0.315 0.783 0.596
#> 4 4 0.731 0.724 0.866 0.103 0.931 0.808
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.981 0.00 1.00
#> 2 2 0.000 0.981 0.00 1.00
#> 3 2 0.000 0.981 0.00 1.00
#> 4 1 0.000 0.987 1.00 0.00
#> 5 1 0.000 0.987 1.00 0.00
#> 6 1 0.000 0.987 1.00 0.00
#> 7 1 0.000 0.987 1.00 0.00
#> 8 1 0.000 0.987 1.00 0.00
#> 9 1 0.000 0.987 1.00 0.00
#> 10 1 0.000 0.987 1.00 0.00
#> 11 1 0.000 0.987 1.00 0.00
#> 12 1 0.000 0.987 1.00 0.00
#> 13 1 0.000 0.987 1.00 0.00
#> 14 1 0.000 0.987 1.00 0.00
#> 15 1 0.000 0.987 1.00 0.00
#> 16 1 0.000 0.987 1.00 0.00
#> 17 1 0.000 0.987 1.00 0.00
#> 18 1 0.000 0.987 1.00 0.00
#> 19 1 0.000 0.987 1.00 0.00
#> 20 1 0.000 0.987 1.00 0.00
#> 21 1 0.141 0.970 0.98 0.02
#> 22 1 0.000 0.987 1.00 0.00
#> 23 1 0.000 0.987 1.00 0.00
#> 24 1 0.000 0.987 1.00 0.00
#> 25 1 0.000 0.987 1.00 0.00
#> 26 1 0.000 0.987 1.00 0.00
#> 27 2 0.000 0.981 0.00 1.00
#> 28 1 0.000 0.987 1.00 0.00
#> 29 1 0.000 0.987 1.00 0.00
#> 30 1 0.000 0.987 1.00 0.00
#> 31 1 0.141 0.970 0.98 0.02
#> 32 1 0.141 0.970 0.98 0.02
#> 33 1 0.000 0.987 1.00 0.00
#> 34 1 0.000 0.987 1.00 0.00
#> 35 1 0.000 0.987 1.00 0.00
#> 36 1 0.000 0.987 1.00 0.00
#> 37 1 0.000 0.987 1.00 0.00
#> 38 1 0.000 0.987 1.00 0.00
#> 39 1 0.000 0.987 1.00 0.00
#> 40 2 0.000 0.981 0.00 1.00
#> 41 1 0.000 0.987 1.00 0.00
#> 42 1 0.000 0.987 1.00 0.00
#> 43 1 0.000 0.987 1.00 0.00
#> 44 1 0.000 0.987 1.00 0.00
#> 45 1 0.000 0.987 1.00 0.00
#> 46 1 0.000 0.987 1.00 0.00
#> 47 1 0.000 0.987 1.00 0.00
#> 48 1 0.000 0.987 1.00 0.00
#> 49 1 0.000 0.987 1.00 0.00
#> 50 1 0.000 0.987 1.00 0.00
#> 51 1 0.000 0.987 1.00 0.00
#> 52 1 0.000 0.987 1.00 0.00
#> 53 1 0.000 0.987 1.00 0.00
#> 54 1 0.000 0.987 1.00 0.00
#> 55 1 0.000 0.987 1.00 0.00
#> 56 2 0.760 0.723 0.22 0.78
#> 57 1 0.000 0.987 1.00 0.00
#> 58 1 0.242 0.951 0.96 0.04
#> 59 1 0.000 0.987 1.00 0.00
#> 60 2 0.584 0.836 0.14 0.86
#> 61 1 0.000 0.987 1.00 0.00
#> 62 1 0.000 0.987 1.00 0.00
#> 63 1 0.760 0.721 0.78 0.22
#> 64 1 0.000 0.987 1.00 0.00
#> 65 1 0.000 0.987 1.00 0.00
#> 66 2 0.000 0.981 0.00 1.00
#> 67 2 0.000 0.981 0.00 1.00
#> 68 2 0.000 0.981 0.00 1.00
#> 69 1 0.000 0.987 1.00 0.00
#> 70 1 0.000 0.987 1.00 0.00
#> 71 1 0.584 0.833 0.86 0.14
#> 72 1 0.000 0.987 1.00 0.00
#> 73 1 0.000 0.987 1.00 0.00
#> 74 1 0.000 0.987 1.00 0.00
#> 75 1 0.000 0.987 1.00 0.00
#> 76 1 0.000 0.987 1.00 0.00
#> 77 1 0.000 0.987 1.00 0.00
#> 78 2 0.242 0.946 0.04 0.96
#> 79 1 0.000 0.987 1.00 0.00
#> 80 2 0.242 0.946 0.04 0.96
#> 81 1 0.000 0.987 1.00 0.00
#> 82 1 0.000 0.987 1.00 0.00
#> 83 2 0.000 0.981 0.00 1.00
#> 84 1 0.000 0.987 1.00 0.00
#> 85 1 0.000 0.987 1.00 0.00
#> 86 2 0.995 0.137 0.46 0.54
#> 87 1 0.000 0.987 1.00 0.00
#> 88 1 0.634 0.809 0.84 0.16
#> 89 2 0.000 0.981 0.00 1.00
#> 90 2 0.000 0.981 0.00 1.00
#> 91 2 0.000 0.981 0.00 1.00
#> 92 2 0.000 0.981 0.00 1.00
#> 93 2 0.000 0.981 0.00 1.00
#> 94 2 0.000 0.981 0.00 1.00
#> 95 2 0.000 0.981 0.00 1.00
#> 96 2 0.000 0.981 0.00 1.00
#> 97 2 0.000 0.981 0.00 1.00
#> 98 2 0.000 0.981 0.00 1.00
#> 99 2 0.000 0.981 0.00 1.00
#> 100 2 0.000 0.981 0.00 1.00
#> 101 2 0.000 0.981 0.00 1.00
#> 102 2 0.000 0.981 0.00 1.00
#> 103 2 0.000 0.981 0.00 1.00
#> 104 2 0.000 0.981 0.00 1.00
#> 105 2 0.000 0.981 0.00 1.00
#> 106 2 0.000 0.981 0.00 1.00
#> 107 2 0.000 0.981 0.00 1.00
#> 108 2 0.000 0.981 0.00 1.00
#> 109 2 0.000 0.981 0.00 1.00
#> 110 2 0.000 0.981 0.00 1.00
#> 111 2 0.000 0.981 0.00 1.00
#> 112 2 0.000 0.981 0.00 1.00
#> 113 2 0.000 0.981 0.00 1.00
#> 114 2 0.000 0.981 0.00 1.00
#> 115 1 0.000 0.987 1.00 0.00
#> 116 2 0.000 0.981 0.00 1.00
#> 117 2 0.000 0.981 0.00 1.00
#> 118 2 0.000 0.981 0.00 1.00
#> 119 2 0.000 0.981 0.00 1.00
#> 120 2 0.000 0.981 0.00 1.00
#> 121 2 0.000 0.981 0.00 1.00
#> 122 2 0.000 0.981 0.00 1.00
#> 123 1 0.141 0.970 0.98 0.02
#> 124 2 0.584 0.835 0.14 0.86
#> 125 2 0.000 0.981 0.00 1.00
#> 126 1 0.981 0.279 0.58 0.42
#> 127 2 0.000 0.981 0.00 1.00
#> 128 1 0.141 0.970 0.98 0.02
#> 129 2 0.000 0.981 0.00 1.00
#> 130 2 0.000 0.981 0.00 1.00
#> 131 2 0.000 0.981 0.00 1.00
#> 132 2 0.000 0.981 0.00 1.00
#> 133 1 0.242 0.950 0.96 0.04
#> 134 2 0.000 0.981 0.00 1.00
#> 135 2 0.000 0.981 0.00 1.00
#> 136 2 0.000 0.981 0.00 1.00
#> 137 2 0.000 0.981 0.00 1.00
#> 138 1 0.000 0.987 1.00 0.00
#> 139 2 0.000 0.981 0.00 1.00
#> 140 1 0.000 0.987 1.00 0.00
#> 141 1 0.000 0.987 1.00 0.00
#> 142 2 0.000 0.981 0.00 1.00
#> 143 1 0.000 0.987 1.00 0.00
#> 144 2 0.000 0.981 0.00 1.00
#> 145 1 0.000 0.987 1.00 0.00
#> 146 1 0.000 0.987 1.00 0.00
#> 147 1 0.000 0.987 1.00 0.00
#> 148 1 0.000 0.987 1.00 0.00
#> 149 2 0.584 0.836 0.14 0.86
#> 150 2 0.000 0.981 0.00 1.00
#> 151 1 0.000 0.987 1.00 0.00
#> 152 2 0.000 0.981 0.00 1.00
#> 153 2 0.000 0.981 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 2 0.0000 0.96602 0.00 1.00 0.00
#> 2 2 0.0000 0.96602 0.00 1.00 0.00
#> 3 2 0.2959 0.86581 0.00 0.90 0.10
#> 4 1 0.0000 0.98588 1.00 0.00 0.00
#> 5 1 0.1529 0.94997 0.96 0.00 0.04
#> 6 1 0.0000 0.98588 1.00 0.00 0.00
#> 7 3 0.0000 0.95726 0.00 0.00 1.00
#> 8 3 0.0000 0.95726 0.00 0.00 1.00
#> 9 3 0.0000 0.95726 0.00 0.00 1.00
#> 10 1 0.0000 0.98588 1.00 0.00 0.00
#> 11 3 0.0000 0.95726 0.00 0.00 1.00
#> 12 1 0.2959 0.88388 0.90 0.00 0.10
#> 13 3 0.0000 0.95726 0.00 0.00 1.00
#> 14 1 0.0000 0.98588 1.00 0.00 0.00
#> 15 1 0.0000 0.98588 1.00 0.00 0.00
#> 16 1 0.0000 0.98588 1.00 0.00 0.00
#> 17 1 0.0000 0.98588 1.00 0.00 0.00
#> 18 3 0.0000 0.95726 0.00 0.00 1.00
#> 19 3 0.0000 0.95726 0.00 0.00 1.00
#> 20 1 0.0000 0.98588 1.00 0.00 0.00
#> 21 3 0.0000 0.95726 0.00 0.00 1.00
#> 22 1 0.0000 0.98588 1.00 0.00 0.00
#> 23 1 0.0000 0.98588 1.00 0.00 0.00
#> 24 1 0.0000 0.98588 1.00 0.00 0.00
#> 25 1 0.0000 0.98588 1.00 0.00 0.00
#> 26 1 0.0000 0.98588 1.00 0.00 0.00
#> 27 2 0.0000 0.96602 0.00 1.00 0.00
#> 28 1 0.0000 0.98588 1.00 0.00 0.00
#> 29 1 0.0000 0.98588 1.00 0.00 0.00
#> 30 1 0.0000 0.98588 1.00 0.00 0.00
#> 31 1 0.1529 0.94797 0.96 0.04 0.00
#> 32 1 0.0000 0.98588 1.00 0.00 0.00
#> 33 1 0.0000 0.98588 1.00 0.00 0.00
#> 34 1 0.0000 0.98588 1.00 0.00 0.00
#> 35 1 0.0000 0.98588 1.00 0.00 0.00
#> 36 1 0.0000 0.98588 1.00 0.00 0.00
#> 37 1 0.0000 0.98588 1.00 0.00 0.00
#> 38 1 0.0000 0.98588 1.00 0.00 0.00
#> 39 1 0.0000 0.98588 1.00 0.00 0.00
#> 40 2 0.0000 0.96602 0.00 1.00 0.00
#> 41 1 0.0000 0.98588 1.00 0.00 0.00
#> 42 1 0.0000 0.98588 1.00 0.00 0.00
#> 43 3 0.3686 0.82511 0.14 0.00 0.86
#> 44 1 0.0000 0.98588 1.00 0.00 0.00
#> 45 1 0.0000 0.98588 1.00 0.00 0.00
#> 46 1 0.0000 0.98588 1.00 0.00 0.00
#> 47 1 0.0000 0.98588 1.00 0.00 0.00
#> 48 1 0.0000 0.98588 1.00 0.00 0.00
#> 49 1 0.0000 0.98588 1.00 0.00 0.00
#> 50 1 0.0000 0.98588 1.00 0.00 0.00
#> 51 1 0.0000 0.98588 1.00 0.00 0.00
#> 52 3 0.0000 0.95726 0.00 0.00 1.00
#> 53 1 0.0000 0.98588 1.00 0.00 0.00
#> 54 3 0.0000 0.95726 0.00 0.00 1.00
#> 55 3 0.0892 0.94029 0.02 0.00 0.98
#> 56 3 0.0000 0.95726 0.00 0.00 1.00
#> 57 1 0.0000 0.98588 1.00 0.00 0.00
#> 58 1 0.0000 0.98588 1.00 0.00 0.00
#> 59 3 0.0000 0.95726 0.00 0.00 1.00
#> 60 3 0.0000 0.95726 0.00 0.00 1.00
#> 61 1 0.0000 0.98588 1.00 0.00 0.00
#> 62 1 0.0000 0.98588 1.00 0.00 0.00
#> 63 1 0.6244 0.19222 0.56 0.44 0.00
#> 64 1 0.0000 0.98588 1.00 0.00 0.00
#> 65 1 0.0000 0.98588 1.00 0.00 0.00
#> 66 3 0.0000 0.95726 0.00 0.00 1.00
#> 67 2 0.0000 0.96602 0.00 1.00 0.00
#> 68 3 0.0000 0.95726 0.00 0.00 1.00
#> 69 1 0.0000 0.98588 1.00 0.00 0.00
#> 70 1 0.0000 0.98588 1.00 0.00 0.00
#> 71 3 0.0000 0.95726 0.00 0.00 1.00
#> 72 1 0.0000 0.98588 1.00 0.00 0.00
#> 73 1 0.1529 0.94980 0.96 0.00 0.04
#> 74 1 0.0000 0.98588 1.00 0.00 0.00
#> 75 1 0.0000 0.98588 1.00 0.00 0.00
#> 76 1 0.0000 0.98588 1.00 0.00 0.00
#> 77 1 0.0000 0.98588 1.00 0.00 0.00
#> 78 2 0.2959 0.85806 0.10 0.90 0.00
#> 79 1 0.0000 0.98588 1.00 0.00 0.00
#> 80 3 0.6192 0.25972 0.00 0.42 0.58
#> 81 3 0.0000 0.95726 0.00 0.00 1.00
#> 82 1 0.0000 0.98588 1.00 0.00 0.00
#> 83 2 0.0000 0.96602 0.00 1.00 0.00
#> 84 1 0.0000 0.98588 1.00 0.00 0.00
#> 85 1 0.0000 0.98588 1.00 0.00 0.00
#> 86 2 0.6309 0.00391 0.50 0.50 0.00
#> 87 1 0.0000 0.98588 1.00 0.00 0.00
#> 88 1 0.1529 0.94689 0.96 0.04 0.00
#> 89 3 0.0000 0.95726 0.00 0.00 1.00
#> 90 2 0.0000 0.96602 0.00 1.00 0.00
#> 91 2 0.0000 0.96602 0.00 1.00 0.00
#> 92 2 0.0000 0.96602 0.00 1.00 0.00
#> 93 2 0.0000 0.96602 0.00 1.00 0.00
#> 94 2 0.0000 0.96602 0.00 1.00 0.00
#> 95 2 0.0000 0.96602 0.00 1.00 0.00
#> 96 2 0.0000 0.96602 0.00 1.00 0.00
#> 97 2 0.0000 0.96602 0.00 1.00 0.00
#> 98 2 0.0000 0.96602 0.00 1.00 0.00
#> 99 2 0.0000 0.96602 0.00 1.00 0.00
#> 100 2 0.0000 0.96602 0.00 1.00 0.00
#> 101 2 0.0000 0.96602 0.00 1.00 0.00
#> 102 2 0.0000 0.96602 0.00 1.00 0.00
#> 103 2 0.0000 0.96602 0.00 1.00 0.00
#> 104 2 0.0000 0.96602 0.00 1.00 0.00
#> 105 2 0.0000 0.96602 0.00 1.00 0.00
#> 106 2 0.0000 0.96602 0.00 1.00 0.00
#> 107 3 0.0000 0.95726 0.00 0.00 1.00
#> 108 2 0.0000 0.96602 0.00 1.00 0.00
#> 109 2 0.0000 0.96602 0.00 1.00 0.00
#> 110 2 0.0000 0.96602 0.00 1.00 0.00
#> 111 3 0.0000 0.95726 0.00 0.00 1.00
#> 112 2 0.0000 0.96602 0.00 1.00 0.00
#> 113 3 0.0000 0.95726 0.00 0.00 1.00
#> 114 2 0.0000 0.96602 0.00 1.00 0.00
#> 115 1 0.0000 0.98588 1.00 0.00 0.00
#> 116 2 0.0000 0.96602 0.00 1.00 0.00
#> 117 2 0.0000 0.96602 0.00 1.00 0.00
#> 118 2 0.0000 0.96602 0.00 1.00 0.00
#> 119 2 0.0000 0.96602 0.00 1.00 0.00
#> 120 2 0.0000 0.96602 0.00 1.00 0.00
#> 121 2 0.0000 0.96602 0.00 1.00 0.00
#> 122 2 0.0000 0.96602 0.00 1.00 0.00
#> 123 3 0.5016 0.68313 0.24 0.00 0.76
#> 124 2 0.2537 0.88153 0.08 0.92 0.00
#> 125 3 0.0000 0.95726 0.00 0.00 1.00
#> 126 2 0.5216 0.64498 0.26 0.74 0.00
#> 127 3 0.0000 0.95726 0.00 0.00 1.00
#> 128 1 0.1529 0.94761 0.96 0.04 0.00
#> 129 2 0.0000 0.96602 0.00 1.00 0.00
#> 130 2 0.0000 0.96602 0.00 1.00 0.00
#> 131 2 0.0000 0.96602 0.00 1.00 0.00
#> 132 2 0.0000 0.96602 0.00 1.00 0.00
#> 133 3 0.0000 0.95726 0.00 0.00 1.00
#> 134 2 0.0000 0.96602 0.00 1.00 0.00
#> 135 3 0.0000 0.95726 0.00 0.00 1.00
#> 136 2 0.0000 0.96602 0.00 1.00 0.00
#> 137 2 0.0000 0.96602 0.00 1.00 0.00
#> 138 3 0.0000 0.95726 0.00 0.00 1.00
#> 139 2 0.0000 0.96602 0.00 1.00 0.00
#> 140 1 0.0000 0.98588 1.00 0.00 0.00
#> 141 1 0.0000 0.98588 1.00 0.00 0.00
#> 142 2 0.0000 0.96602 0.00 1.00 0.00
#> 143 1 0.0000 0.98588 1.00 0.00 0.00
#> 144 2 0.0000 0.96602 0.00 1.00 0.00
#> 145 1 0.0000 0.98588 1.00 0.00 0.00
#> 146 3 0.0000 0.95726 0.00 0.00 1.00
#> 147 1 0.3340 0.85684 0.88 0.00 0.12
#> 148 3 0.6126 0.34734 0.40 0.00 0.60
#> 149 3 0.0000 0.95726 0.00 0.00 1.00
#> 150 2 0.6244 0.19067 0.00 0.56 0.44
#> 151 1 0.0000 0.98588 1.00 0.00 0.00
#> 152 3 0.3340 0.83819 0.00 0.12 0.88
#> 153 3 0.0000 0.95726 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 2 0.0707 0.8366 0.00 0.98 0.00 0.02
#> 2 2 0.1211 0.8243 0.00 0.96 0.00 0.04
#> 3 2 0.5428 0.5611 0.00 0.74 0.14 0.12
#> 4 1 0.2345 0.8144 0.90 0.00 0.00 0.10
#> 5 1 0.7427 0.0174 0.50 0.00 0.30 0.20
#> 6 1 0.1637 0.8368 0.94 0.00 0.00 0.06
#> 7 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 8 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 9 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 10 1 0.2647 0.8016 0.88 0.00 0.00 0.12
#> 11 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 12 1 0.7372 -0.2240 0.42 0.00 0.16 0.42
#> 13 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 14 1 0.3801 0.6855 0.78 0.00 0.00 0.22
#> 15 1 0.2345 0.8160 0.90 0.00 0.00 0.10
#> 16 1 0.2011 0.8286 0.92 0.00 0.00 0.08
#> 17 4 0.4713 0.2485 0.36 0.00 0.00 0.64
#> 18 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 19 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 20 1 0.4731 0.6884 0.78 0.00 0.06 0.16
#> 21 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 22 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 23 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 24 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 25 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 26 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 27 2 0.2345 0.8340 0.00 0.90 0.00 0.10
#> 28 1 0.0707 0.8542 0.98 0.00 0.00 0.02
#> 29 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 30 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 31 1 0.1211 0.8347 0.96 0.04 0.00 0.00
#> 32 1 0.1637 0.8211 0.94 0.00 0.00 0.06
#> 33 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 34 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 35 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 36 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 37 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 38 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 39 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 40 2 0.3198 0.7888 0.08 0.88 0.00 0.04
#> 41 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 42 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 43 3 0.3610 0.5948 0.20 0.00 0.80 0.00
#> 44 1 0.2011 0.8259 0.92 0.00 0.00 0.08
#> 45 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 46 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 47 1 0.2345 0.7817 0.90 0.00 0.00 0.10
#> 48 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 49 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 50 1 0.1913 0.8205 0.94 0.00 0.04 0.02
#> 51 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 52 3 0.0707 0.9099 0.00 0.00 0.98 0.02
#> 53 1 0.0707 0.8542 0.98 0.00 0.00 0.02
#> 54 3 0.0707 0.9070 0.00 0.00 0.98 0.02
#> 55 4 0.5860 0.1285 0.04 0.00 0.38 0.58
#> 56 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 57 1 0.4790 0.4599 0.62 0.00 0.00 0.38
#> 58 1 0.4406 0.5672 0.70 0.00 0.00 0.30
#> 59 3 0.0707 0.9106 0.00 0.00 0.98 0.02
#> 60 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 61 1 0.2647 0.8016 0.88 0.00 0.00 0.12
#> 62 1 0.2647 0.8016 0.88 0.00 0.00 0.12
#> 63 4 0.7869 -0.0250 0.28 0.34 0.00 0.38
#> 64 1 0.2647 0.8016 0.88 0.00 0.00 0.12
#> 65 1 0.1211 0.8465 0.96 0.00 0.00 0.04
#> 66 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 67 2 0.1211 0.8463 0.00 0.96 0.00 0.04
#> 68 3 0.0707 0.9124 0.00 0.00 0.98 0.02
#> 69 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 70 1 0.4406 0.5564 0.70 0.00 0.00 0.30
#> 71 3 0.1637 0.8802 0.00 0.00 0.94 0.06
#> 72 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 73 1 0.5956 0.5365 0.68 0.00 0.10 0.22
#> 74 1 0.2345 0.7816 0.90 0.00 0.00 0.10
#> 75 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 76 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 77 1 0.4948 0.1845 0.56 0.00 0.00 0.44
#> 78 2 0.7274 0.4446 0.22 0.54 0.00 0.24
#> 79 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 80 3 0.7783 0.2695 0.04 0.26 0.56 0.14
#> 81 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 82 1 0.4277 0.5384 0.72 0.00 0.00 0.28
#> 83 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 84 1 0.2345 0.8138 0.90 0.00 0.00 0.10
#> 85 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 86 1 0.7748 -0.0712 0.44 0.28 0.00 0.28
#> 87 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 88 1 0.5392 0.4651 0.68 0.04 0.00 0.28
#> 89 3 0.0707 0.9124 0.00 0.00 0.98 0.02
#> 90 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 91 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 92 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 93 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 94 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 95 2 0.1211 0.8459 0.00 0.96 0.00 0.04
#> 96 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 97 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 98 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 99 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 100 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 101 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 102 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 103 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 104 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 105 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 106 2 0.0707 0.8471 0.00 0.98 0.00 0.02
#> 107 3 0.0707 0.9124 0.00 0.00 0.98 0.02
#> 108 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 109 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 110 2 0.1637 0.8433 0.00 0.94 0.00 0.06
#> 111 3 0.0707 0.9124 0.00 0.00 0.98 0.02
#> 112 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 113 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 114 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 115 1 0.0000 0.8610 1.00 0.00 0.00 0.00
#> 116 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 117 2 0.3400 0.8096 0.00 0.82 0.00 0.18
#> 118 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 119 2 0.0000 0.8463 0.00 1.00 0.00 0.00
#> 120 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 121 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 122 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 123 4 0.4642 0.3326 0.02 0.00 0.24 0.74
#> 124 2 0.6836 0.5417 0.14 0.58 0.00 0.28
#> 125 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 126 4 0.7365 -0.2023 0.16 0.40 0.00 0.44
#> 127 3 0.0000 0.9211 0.00 0.00 1.00 0.00
#> 128 1 0.6766 0.2281 0.52 0.10 0.00 0.38
#> 129 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 130 2 0.0707 0.8466 0.00 0.98 0.00 0.02
#> 131 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 132 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 133 4 0.4994 -0.1835 0.00 0.00 0.48 0.52
#> 134 2 0.5713 0.3149 0.00 0.62 0.04 0.34
#> 135 3 0.5062 0.5718 0.00 0.02 0.68 0.30
#> 136 2 0.1637 0.8111 0.00 0.94 0.00 0.06
#> 137 2 0.1637 0.8105 0.00 0.94 0.00 0.06
#> 138 3 0.3801 0.6996 0.00 0.00 0.78 0.22
#> 139 2 0.1637 0.8111 0.00 0.94 0.00 0.06
#> 140 4 0.4790 0.3358 0.38 0.00 0.00 0.62
#> 141 1 0.3172 0.7674 0.84 0.00 0.00 0.16
#> 142 2 0.4134 0.7793 0.00 0.74 0.00 0.26
#> 143 1 0.4134 0.5930 0.74 0.00 0.00 0.26
#> 144 2 0.0707 0.8376 0.00 0.98 0.00 0.02
#> 145 4 0.5000 -0.0533 0.50 0.00 0.00 0.50
#> 146 4 0.4790 0.0647 0.00 0.00 0.38 0.62
#> 147 4 0.4277 0.4881 0.28 0.00 0.00 0.72
#> 148 4 0.5173 0.4681 0.32 0.00 0.02 0.66
#> 149 3 0.2011 0.8678 0.00 0.00 0.92 0.08
#> 150 4 0.5355 0.3024 0.00 0.36 0.02 0.62
#> 151 4 0.4134 0.4948 0.26 0.00 0.00 0.74
#> 152 4 0.6594 0.3492 0.00 0.24 0.14 0.62
#> 153 3 0.4790 0.4606 0.00 0.00 0.62 0.38
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 151 0.4714 2
#> ATC:skmeans 148 0.0856 3
#> ATC:skmeans 128 0.2420 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node031. Child nodes: Node01131-leaf , Node01132-leaf , Node01133-leaf , Node01211-leaf , Node01212-leaf , Node01221-leaf , Node01222-leaf , Node01223-leaf , Node01231-leaf , Node01232-leaf , Node01233-leaf , Node01234-leaf , Node02111 , Node02112 , Node02113-leaf , Node02121-leaf , Node02122-leaf , Node02123-leaf , Node02221-leaf , Node02222-leaf , Node03111-leaf , Node03112-leaf , Node03121-leaf , Node03122 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0312"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 8616 rows and 131 columns.
#> Top rows (862) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.952 0.981 0.501 0.500 0.500
#> 3 3 0.974 0.937 0.973 0.334 0.748 0.535
#> 4 4 0.920 0.911 0.959 0.110 0.895 0.701
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.000 0.9807 1.00 0.00
#> 2 1 0.000 0.9807 1.00 0.00
#> 3 1 0.000 0.9807 1.00 0.00
#> 4 1 0.000 0.9807 1.00 0.00
#> 5 1 0.000 0.9807 1.00 0.00
#> 6 1 0.000 0.9807 1.00 0.00
#> 7 1 0.000 0.9807 1.00 0.00
#> 8 1 0.000 0.9807 1.00 0.00
#> 9 1 0.000 0.9807 1.00 0.00
#> 10 1 0.000 0.9807 1.00 0.00
#> 11 2 0.000 0.9806 0.00 1.00
#> 12 1 0.000 0.9807 1.00 0.00
#> 13 1 0.000 0.9807 1.00 0.00
#> 14 1 0.000 0.9807 1.00 0.00
#> 15 1 0.000 0.9807 1.00 0.00
#> 16 1 0.000 0.9807 1.00 0.00
#> 17 1 0.000 0.9807 1.00 0.00
#> 18 1 0.000 0.9807 1.00 0.00
#> 19 2 0.242 0.9459 0.04 0.96
#> 20 1 0.000 0.9807 1.00 0.00
#> 21 1 0.000 0.9807 1.00 0.00
#> 22 1 0.000 0.9807 1.00 0.00
#> 23 1 0.000 0.9807 1.00 0.00
#> 24 1 0.000 0.9807 1.00 0.00
#> 25 1 0.000 0.9807 1.00 0.00
#> 26 1 0.000 0.9807 1.00 0.00
#> 27 1 0.000 0.9807 1.00 0.00
#> 28 1 0.000 0.9807 1.00 0.00
#> 29 1 0.000 0.9807 1.00 0.00
#> 30 1 0.000 0.9807 1.00 0.00
#> 31 1 0.000 0.9807 1.00 0.00
#> 32 1 0.000 0.9807 1.00 0.00
#> 33 1 0.000 0.9807 1.00 0.00
#> 34 1 0.000 0.9807 1.00 0.00
#> 35 1 0.000 0.9807 1.00 0.00
#> 36 1 0.000 0.9807 1.00 0.00
#> 37 1 0.000 0.9807 1.00 0.00
#> 38 2 0.000 0.9806 0.00 1.00
#> 39 1 0.000 0.9807 1.00 0.00
#> 40 1 0.000 0.9807 1.00 0.00
#> 41 1 0.000 0.9807 1.00 0.00
#> 42 1 0.000 0.9807 1.00 0.00
#> 43 1 0.000 0.9807 1.00 0.00
#> 44 2 0.000 0.9806 0.00 1.00
#> 45 1 0.000 0.9807 1.00 0.00
#> 46 2 0.000 0.9806 0.00 1.00
#> 47 1 0.000 0.9807 1.00 0.00
#> 48 1 0.000 0.9807 1.00 0.00
#> 49 1 0.000 0.9807 1.00 0.00
#> 50 1 0.000 0.9807 1.00 0.00
#> 51 2 0.000 0.9806 0.00 1.00
#> 52 1 0.000 0.9807 1.00 0.00
#> 53 2 0.000 0.9806 0.00 1.00
#> 54 2 0.995 0.1397 0.46 0.54
#> 55 1 0.000 0.9807 1.00 0.00
#> 56 1 0.000 0.9807 1.00 0.00
#> 57 1 0.000 0.9807 1.00 0.00
#> 58 1 0.000 0.9807 1.00 0.00
#> 59 2 0.000 0.9806 0.00 1.00
#> 60 2 0.000 0.9806 0.00 1.00
#> 61 1 0.000 0.9807 1.00 0.00
#> 62 1 0.000 0.9807 1.00 0.00
#> 63 1 0.000 0.9807 1.00 0.00
#> 64 1 0.000 0.9807 1.00 0.00
#> 65 2 0.000 0.9806 0.00 1.00
#> 66 2 0.402 0.9045 0.08 0.92
#> 67 2 0.000 0.9806 0.00 1.00
#> 68 2 0.881 0.5749 0.30 0.70
#> 69 1 0.000 0.9807 1.00 0.00
#> 70 1 0.000 0.9807 1.00 0.00
#> 71 2 0.000 0.9806 0.00 1.00
#> 72 2 0.141 0.9641 0.02 0.98
#> 73 2 0.000 0.9806 0.00 1.00
#> 74 2 0.000 0.9806 0.00 1.00
#> 75 2 0.000 0.9806 0.00 1.00
#> 76 2 0.529 0.8602 0.12 0.88
#> 77 2 0.000 0.9806 0.00 1.00
#> 78 2 0.000 0.9806 0.00 1.00
#> 79 2 0.000 0.9806 0.00 1.00
#> 80 2 0.327 0.9270 0.06 0.94
#> 81 2 0.000 0.9806 0.00 1.00
#> 82 2 0.000 0.9806 0.00 1.00
#> 83 1 0.000 0.9807 1.00 0.00
#> 84 2 0.000 0.9806 0.00 1.00
#> 85 1 0.000 0.9807 1.00 0.00
#> 86 2 0.242 0.9465 0.04 0.96
#> 87 2 0.000 0.9806 0.00 1.00
#> 88 2 0.000 0.9806 0.00 1.00
#> 89 2 0.000 0.9806 0.00 1.00
#> 90 1 0.000 0.9807 1.00 0.00
#> 91 2 0.000 0.9806 0.00 1.00
#> 92 1 0.000 0.9807 1.00 0.00
#> 93 1 0.000 0.9807 1.00 0.00
#> 94 1 0.242 0.9415 0.96 0.04
#> 95 2 0.000 0.9806 0.00 1.00
#> 96 1 0.000 0.9807 1.00 0.00
#> 97 2 0.000 0.9806 0.00 1.00
#> 98 2 0.000 0.9806 0.00 1.00
#> 99 2 0.000 0.9806 0.00 1.00
#> 100 2 0.000 0.9806 0.00 1.00
#> 101 2 0.000 0.9806 0.00 1.00
#> 102 2 0.000 0.9806 0.00 1.00
#> 103 2 0.000 0.9806 0.00 1.00
#> 104 2 0.000 0.9806 0.00 1.00
#> 105 2 0.000 0.9806 0.00 1.00
#> 106 2 0.000 0.9806 0.00 1.00
#> 107 2 0.000 0.9806 0.00 1.00
#> 108 2 0.000 0.9806 0.00 1.00
#> 109 2 0.000 0.9806 0.00 1.00
#> 110 2 0.000 0.9806 0.00 1.00
#> 111 2 0.000 0.9806 0.00 1.00
#> 112 2 0.000 0.9806 0.00 1.00
#> 113 2 0.000 0.9806 0.00 1.00
#> 114 2 0.000 0.9806 0.00 1.00
#> 115 1 0.141 0.9616 0.98 0.02
#> 116 1 0.000 0.9807 1.00 0.00
#> 117 1 0.981 0.2673 0.58 0.42
#> 118 2 0.000 0.9806 0.00 1.00
#> 119 1 0.000 0.9807 1.00 0.00
#> 120 1 0.943 0.4313 0.64 0.36
#> 121 2 0.000 0.9806 0.00 1.00
#> 122 2 0.000 0.9806 0.00 1.00
#> 123 2 0.000 0.9806 0.00 1.00
#> 124 2 0.000 0.9806 0.00 1.00
#> 125 1 0.999 0.0721 0.52 0.48
#> 126 2 0.000 0.9806 0.00 1.00
#> 127 2 0.000 0.9806 0.00 1.00
#> 128 1 0.000 0.9807 1.00 0.00
#> 129 2 0.000 0.9806 0.00 1.00
#> 130 2 0.000 0.9806 0.00 1.00
#> 131 1 0.000 0.9807 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 1 0.0000 0.965 1.00 0.00 0.00
#> 2 1 0.0000 0.965 1.00 0.00 0.00
#> 3 1 0.0000 0.965 1.00 0.00 0.00
#> 4 3 0.0000 0.978 0.00 0.00 1.00
#> 5 1 0.0000 0.965 1.00 0.00 0.00
#> 6 1 0.0000 0.965 1.00 0.00 0.00
#> 7 1 0.0000 0.965 1.00 0.00 0.00
#> 8 1 0.0000 0.965 1.00 0.00 0.00
#> 9 1 0.0000 0.965 1.00 0.00 0.00
#> 10 1 0.0000 0.965 1.00 0.00 0.00
#> 11 2 0.0000 0.975 0.00 1.00 0.00
#> 12 1 0.0000 0.965 1.00 0.00 0.00
#> 13 1 0.0000 0.965 1.00 0.00 0.00
#> 14 1 0.0000 0.965 1.00 0.00 0.00
#> 15 1 0.0000 0.965 1.00 0.00 0.00
#> 16 1 0.0000 0.965 1.00 0.00 0.00
#> 17 1 0.2066 0.911 0.94 0.00 0.06
#> 18 1 0.0000 0.965 1.00 0.00 0.00
#> 19 1 0.0892 0.947 0.98 0.02 0.00
#> 20 1 0.0000 0.965 1.00 0.00 0.00
#> 21 1 0.0000 0.965 1.00 0.00 0.00
#> 22 1 0.0000 0.965 1.00 0.00 0.00
#> 23 1 0.0000 0.965 1.00 0.00 0.00
#> 24 1 0.0000 0.965 1.00 0.00 0.00
#> 25 1 0.0000 0.965 1.00 0.00 0.00
#> 26 1 0.0000 0.965 1.00 0.00 0.00
#> 27 1 0.0000 0.965 1.00 0.00 0.00
#> 28 1 0.0000 0.965 1.00 0.00 0.00
#> 29 1 0.0000 0.965 1.00 0.00 0.00
#> 30 1 0.0000 0.965 1.00 0.00 0.00
#> 31 1 0.0000 0.965 1.00 0.00 0.00
#> 32 1 0.0000 0.965 1.00 0.00 0.00
#> 33 1 0.0000 0.965 1.00 0.00 0.00
#> 34 1 0.0000 0.965 1.00 0.00 0.00
#> 35 3 0.0000 0.978 0.00 0.00 1.00
#> 36 1 0.0000 0.965 1.00 0.00 0.00
#> 37 1 0.0000 0.965 1.00 0.00 0.00
#> 38 2 0.2947 0.910 0.02 0.92 0.06
#> 39 1 0.0000 0.965 1.00 0.00 0.00
#> 40 3 0.0000 0.978 0.00 0.00 1.00
#> 41 3 0.0000 0.978 0.00 0.00 1.00
#> 42 3 0.0000 0.978 0.00 0.00 1.00
#> 43 1 0.0000 0.965 1.00 0.00 0.00
#> 44 2 0.0000 0.975 0.00 1.00 0.00
#> 45 1 0.0000 0.965 1.00 0.00 0.00
#> 46 2 0.0000 0.975 0.00 1.00 0.00
#> 47 3 0.0000 0.978 0.00 0.00 1.00
#> 48 3 0.0000 0.978 0.00 0.00 1.00
#> 49 1 0.0000 0.965 1.00 0.00 0.00
#> 50 1 0.5948 0.444 0.64 0.00 0.36
#> 51 3 0.5216 0.653 0.00 0.26 0.74
#> 52 1 0.6192 0.286 0.58 0.00 0.42
#> 53 2 0.0000 0.975 0.00 1.00 0.00
#> 54 3 0.0892 0.963 0.00 0.02 0.98
#> 55 1 0.5835 0.488 0.66 0.00 0.34
#> 56 3 0.1529 0.946 0.04 0.00 0.96
#> 57 3 0.1529 0.946 0.04 0.00 0.96
#> 58 1 0.0892 0.948 0.98 0.00 0.02
#> 59 2 0.0000 0.975 0.00 1.00 0.00
#> 60 2 0.5706 0.524 0.00 0.68 0.32
#> 61 3 0.2959 0.886 0.10 0.00 0.90
#> 62 3 0.4291 0.781 0.18 0.00 0.82
#> 63 3 0.0000 0.978 0.00 0.00 1.00
#> 64 3 0.0000 0.978 0.00 0.00 1.00
#> 65 2 0.0000 0.975 0.00 1.00 0.00
#> 66 1 0.5835 0.484 0.66 0.34 0.00
#> 67 2 0.0000 0.975 0.00 1.00 0.00
#> 68 3 0.0000 0.978 0.00 0.00 1.00
#> 69 3 0.0000 0.978 0.00 0.00 1.00
#> 70 3 0.0000 0.978 0.00 0.00 1.00
#> 71 3 0.0000 0.978 0.00 0.00 1.00
#> 72 3 0.0000 0.978 0.00 0.00 1.00
#> 73 2 0.0000 0.975 0.00 1.00 0.00
#> 74 2 0.0000 0.975 0.00 1.00 0.00
#> 75 2 0.0000 0.975 0.00 1.00 0.00
#> 76 3 0.0000 0.978 0.00 0.00 1.00
#> 77 2 0.0000 0.975 0.00 1.00 0.00
#> 78 2 0.0000 0.975 0.00 1.00 0.00
#> 79 3 0.0000 0.978 0.00 0.00 1.00
#> 80 3 0.0000 0.978 0.00 0.00 1.00
#> 81 2 0.0000 0.975 0.00 1.00 0.00
#> 82 2 0.0000 0.975 0.00 1.00 0.00
#> 83 3 0.0000 0.978 0.00 0.00 1.00
#> 84 2 0.0000 0.975 0.00 1.00 0.00
#> 85 3 0.0000 0.978 0.00 0.00 1.00
#> 86 3 0.0000 0.978 0.00 0.00 1.00
#> 87 3 0.0000 0.978 0.00 0.00 1.00
#> 88 2 0.0000 0.975 0.00 1.00 0.00
#> 89 2 0.0000 0.975 0.00 1.00 0.00
#> 90 3 0.0000 0.978 0.00 0.00 1.00
#> 91 2 0.0000 0.975 0.00 1.00 0.00
#> 92 3 0.0000 0.978 0.00 0.00 1.00
#> 93 3 0.0000 0.978 0.00 0.00 1.00
#> 94 3 0.0000 0.978 0.00 0.00 1.00
#> 95 3 0.0000 0.978 0.00 0.00 1.00
#> 96 3 0.0000 0.978 0.00 0.00 1.00
#> 97 2 0.2066 0.930 0.00 0.94 0.06
#> 98 2 0.0000 0.975 0.00 1.00 0.00
#> 99 2 0.0000 0.975 0.00 1.00 0.00
#> 100 2 0.0000 0.975 0.00 1.00 0.00
#> 101 2 0.0000 0.975 0.00 1.00 0.00
#> 102 2 0.0000 0.975 0.00 1.00 0.00
#> 103 2 0.0000 0.975 0.00 1.00 0.00
#> 104 2 0.0000 0.975 0.00 1.00 0.00
#> 105 2 0.0000 0.975 0.00 1.00 0.00
#> 106 2 0.0000 0.975 0.00 1.00 0.00
#> 107 2 0.2066 0.929 0.00 0.94 0.06
#> 108 2 0.1529 0.946 0.00 0.96 0.04
#> 109 2 0.0000 0.975 0.00 1.00 0.00
#> 110 2 0.2066 0.929 0.00 0.94 0.06
#> 111 2 0.1529 0.946 0.00 0.96 0.04
#> 112 2 0.0000 0.975 0.00 1.00 0.00
#> 113 2 0.0000 0.975 0.00 1.00 0.00
#> 114 2 0.6280 0.180 0.00 0.54 0.46
#> 115 1 0.0000 0.965 1.00 0.00 0.00
#> 116 1 0.0000 0.965 1.00 0.00 0.00
#> 117 3 0.0000 0.978 0.00 0.00 1.00
#> 118 2 0.0000 0.975 0.00 1.00 0.00
#> 119 3 0.0000 0.978 0.00 0.00 1.00
#> 120 3 0.4035 0.883 0.08 0.04 0.88
#> 121 2 0.0000 0.975 0.00 1.00 0.00
#> 122 2 0.0000 0.975 0.00 1.00 0.00
#> 123 2 0.0000 0.975 0.00 1.00 0.00
#> 124 2 0.1529 0.946 0.00 0.96 0.04
#> 125 1 0.0892 0.947 0.98 0.02 0.00
#> 126 2 0.0000 0.975 0.00 1.00 0.00
#> 127 2 0.0000 0.975 0.00 1.00 0.00
#> 128 1 0.0000 0.965 1.00 0.00 0.00
#> 129 2 0.0000 0.975 0.00 1.00 0.00
#> 130 2 0.0000 0.975 0.00 1.00 0.00
#> 131 1 0.0000 0.965 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 2 1 0.4522 0.538 0.68 0.32 0.00 0.00
#> 3 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 4 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 5 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 6 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 7 1 0.1211 0.926 0.96 0.00 0.00 0.04
#> 8 1 0.0707 0.948 0.98 0.02 0.00 0.00
#> 9 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 10 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 11 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 12 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 13 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 14 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 15 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 16 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 17 1 0.0707 0.943 0.98 0.00 0.02 0.00
#> 18 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 19 2 0.0707 0.921 0.02 0.98 0.00 0.00
#> 20 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 21 1 0.1637 0.916 0.94 0.06 0.00 0.00
#> 22 1 0.0707 0.948 0.98 0.02 0.00 0.00
#> 23 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 24 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 25 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 26 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 27 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 28 1 0.0707 0.948 0.98 0.02 0.00 0.00
#> 29 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 30 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 31 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 32 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 33 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 34 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 35 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 36 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 37 1 0.0707 0.948 0.98 0.02 0.00 0.00
#> 38 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 39 1 0.0707 0.948 0.98 0.02 0.00 0.00
#> 40 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 41 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 42 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 43 1 0.0707 0.948 0.98 0.02 0.00 0.00
#> 44 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 45 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 46 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 47 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 48 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 49 1 0.0707 0.948 0.98 0.02 0.00 0.00
#> 50 1 0.4522 0.543 0.68 0.00 0.32 0.00
#> 51 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 52 1 0.4624 0.499 0.66 0.00 0.34 0.00
#> 53 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 54 3 0.1637 0.907 0.00 0.06 0.94 0.00
#> 55 1 0.4522 0.544 0.68 0.00 0.32 0.00
#> 56 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 57 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 58 1 0.3606 0.818 0.84 0.02 0.14 0.00
#> 59 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 60 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 61 3 0.3975 0.674 0.24 0.00 0.76 0.00
#> 62 3 0.3172 0.789 0.16 0.00 0.84 0.00
#> 63 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 64 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 65 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 66 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 67 2 0.2345 0.880 0.00 0.90 0.00 0.10
#> 68 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 69 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 70 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 71 3 0.4790 0.406 0.00 0.00 0.62 0.38
#> 72 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 73 2 0.4713 0.489 0.00 0.64 0.00 0.36
#> 74 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 75 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 76 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 77 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 78 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 79 3 0.0707 0.939 0.00 0.00 0.98 0.02
#> 80 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 81 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 82 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 83 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 84 2 0.4994 0.143 0.00 0.52 0.00 0.48
#> 85 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 86 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 87 3 0.0707 0.938 0.00 0.02 0.98 0.00
#> 88 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 89 2 0.3400 0.791 0.00 0.82 0.00 0.18
#> 90 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 91 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 92 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 93 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 94 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 95 3 0.3801 0.715 0.00 0.00 0.78 0.22
#> 96 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 97 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 98 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 99 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 100 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 101 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 102 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 103 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 104 2 0.1637 0.911 0.00 0.94 0.00 0.06
#> 105 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 106 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 107 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 108 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 109 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 110 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 111 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 112 2 0.2647 0.859 0.00 0.88 0.00 0.12
#> 113 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 114 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 115 2 0.4522 0.504 0.32 0.68 0.00 0.00
#> 116 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 117 3 0.0000 0.953 0.00 0.00 1.00 0.00
#> 118 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 119 3 0.1637 0.906 0.00 0.00 0.94 0.06
#> 120 3 0.6606 0.600 0.10 0.22 0.66 0.02
#> 121 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 122 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 123 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 124 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 125 2 0.1637 0.884 0.06 0.94 0.00 0.00
#> 126 2 0.0707 0.936 0.00 0.98 0.00 0.02
#> 127 4 0.2921 0.821 0.00 0.14 0.00 0.86
#> 128 1 0.0000 0.957 1.00 0.00 0.00 0.00
#> 129 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 130 4 0.0000 0.992 0.00 0.00 0.00 1.00
#> 131 1 0.0000 0.957 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 127 0.0696 2
#> ATC:skmeans 126 0.1794 3
#> ATC:skmeans 127 0.1304 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0312. Child nodes: Node021111-leaf , Node021112-leaf , Node021121-leaf , Node021122-leaf , Node031221-leaf , Node031222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["03122"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7376 rows and 60 columns.
#> Top rows (738) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.979 0.990 0.5073 0.494 0.494
#> 3 3 0.871 0.916 0.964 0.3224 0.754 0.540
#> 4 4 0.749 0.826 0.892 0.0884 0.928 0.786
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.000 0.982 1.00 0.00
#> 2 1 0.000 0.982 1.00 0.00
#> 3 1 0.000 0.982 1.00 0.00
#> 4 1 0.000 0.982 1.00 0.00
#> 5 1 0.000 0.982 1.00 0.00
#> 6 1 0.000 0.982 1.00 0.00
#> 7 1 0.000 0.982 1.00 0.00
#> 8 1 0.722 0.763 0.80 0.20
#> 9 1 0.000 0.982 1.00 0.00
#> 10 1 0.000 0.982 1.00 0.00
#> 11 1 0.000 0.982 1.00 0.00
#> 12 1 0.000 0.982 1.00 0.00
#> 13 1 0.141 0.967 0.98 0.02
#> 14 1 0.634 0.820 0.84 0.16
#> 15 2 0.000 0.998 0.00 1.00
#> 16 2 0.000 0.998 0.00 1.00
#> 17 1 0.529 0.868 0.88 0.12
#> 18 1 0.000 0.982 1.00 0.00
#> 19 1 0.000 0.982 1.00 0.00
#> 20 2 0.000 0.998 0.00 1.00
#> 21 1 0.000 0.982 1.00 0.00
#> 22 1 0.000 0.982 1.00 0.00
#> 23 2 0.000 0.998 0.00 1.00
#> 24 2 0.000 0.998 0.00 1.00
#> 25 1 0.000 0.982 1.00 0.00
#> 26 1 0.000 0.982 1.00 0.00
#> 27 2 0.000 0.998 0.00 1.00
#> 28 2 0.000 0.998 0.00 1.00
#> 29 2 0.327 0.935 0.06 0.94
#> 30 2 0.000 0.998 0.00 1.00
#> 31 1 0.000 0.982 1.00 0.00
#> 32 1 0.000 0.982 1.00 0.00
#> 33 2 0.000 0.998 0.00 1.00
#> 34 2 0.000 0.998 0.00 1.00
#> 35 2 0.000 0.998 0.00 1.00
#> 36 2 0.000 0.998 0.00 1.00
#> 37 2 0.000 0.998 0.00 1.00
#> 38 2 0.000 0.998 0.00 1.00
#> 39 2 0.000 0.998 0.00 1.00
#> 40 2 0.000 0.998 0.00 1.00
#> 41 1 0.000 0.982 1.00 0.00
#> 42 2 0.000 0.998 0.00 1.00
#> 43 1 0.000 0.982 1.00 0.00
#> 44 2 0.000 0.998 0.00 1.00
#> 45 2 0.000 0.998 0.00 1.00
#> 46 1 0.000 0.982 1.00 0.00
#> 47 2 0.000 0.998 0.00 1.00
#> 48 2 0.000 0.998 0.00 1.00
#> 49 1 0.000 0.982 1.00 0.00
#> 50 2 0.000 0.998 0.00 1.00
#> 51 2 0.000 0.998 0.00 1.00
#> 52 1 0.000 0.982 1.00 0.00
#> 53 1 0.000 0.982 1.00 0.00
#> 54 2 0.000 0.998 0.00 1.00
#> 55 1 0.000 0.982 1.00 0.00
#> 56 2 0.000 0.998 0.00 1.00
#> 57 1 0.000 0.982 1.00 0.00
#> 58 1 0.242 0.950 0.96 0.04
#> 59 2 0.000 0.998 0.00 1.00
#> 60 2 0.000 0.998 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 1 0.0000 0.952 1.00 0.00 0.00
#> 2 1 0.0000 0.952 1.00 0.00 0.00
#> 3 3 0.0892 0.918 0.02 0.00 0.98
#> 4 1 0.0000 0.952 1.00 0.00 0.00
#> 5 1 0.0000 0.952 1.00 0.00 0.00
#> 6 3 0.2959 0.861 0.10 0.00 0.90
#> 7 1 0.0000 0.952 1.00 0.00 0.00
#> 8 3 0.0000 0.928 0.00 0.00 1.00
#> 9 1 0.0000 0.952 1.00 0.00 0.00
#> 10 3 0.4555 0.750 0.20 0.00 0.80
#> 11 1 0.0000 0.952 1.00 0.00 0.00
#> 12 1 0.0000 0.952 1.00 0.00 0.00
#> 13 3 0.0000 0.928 0.00 0.00 1.00
#> 14 3 0.0000 0.928 0.00 0.00 1.00
#> 15 3 0.2959 0.859 0.00 0.10 0.90
#> 16 3 0.0000 0.928 0.00 0.00 1.00
#> 17 1 0.8472 0.240 0.54 0.10 0.36
#> 18 3 0.4555 0.747 0.20 0.00 0.80
#> 19 1 0.5706 0.512 0.68 0.00 0.32
#> 20 3 0.0000 0.928 0.00 0.00 1.00
#> 21 1 0.0000 0.952 1.00 0.00 0.00
#> 22 1 0.0000 0.952 1.00 0.00 0.00
#> 23 3 0.0000 0.928 0.00 0.00 1.00
#> 24 3 0.0000 0.928 0.00 0.00 1.00
#> 25 1 0.0000 0.952 1.00 0.00 0.00
#> 26 1 0.0000 0.952 1.00 0.00 0.00
#> 27 3 0.2959 0.858 0.00 0.10 0.90
#> 28 3 0.6126 0.357 0.00 0.40 0.60
#> 29 3 0.0000 0.928 0.00 0.00 1.00
#> 30 2 0.0000 1.000 0.00 1.00 0.00
#> 31 1 0.0000 0.952 1.00 0.00 0.00
#> 32 1 0.0000 0.952 1.00 0.00 0.00
#> 33 3 0.0000 0.928 0.00 0.00 1.00
#> 34 2 0.0000 1.000 0.00 1.00 0.00
#> 35 2 0.0000 1.000 0.00 1.00 0.00
#> 36 2 0.0000 1.000 0.00 1.00 0.00
#> 37 2 0.0000 1.000 0.00 1.00 0.00
#> 38 2 0.0000 1.000 0.00 1.00 0.00
#> 39 2 0.0000 1.000 0.00 1.00 0.00
#> 40 2 0.0000 1.000 0.00 1.00 0.00
#> 41 1 0.0000 0.952 1.00 0.00 0.00
#> 42 2 0.0000 1.000 0.00 1.00 0.00
#> 43 1 0.0000 0.952 1.00 0.00 0.00
#> 44 2 0.0000 1.000 0.00 1.00 0.00
#> 45 2 0.0000 1.000 0.00 1.00 0.00
#> 46 1 0.0000 0.952 1.00 0.00 0.00
#> 47 2 0.0000 1.000 0.00 1.00 0.00
#> 48 2 0.0000 1.000 0.00 1.00 0.00
#> 49 1 0.0000 0.952 1.00 0.00 0.00
#> 50 2 0.0000 1.000 0.00 1.00 0.00
#> 51 2 0.0000 1.000 0.00 1.00 0.00
#> 52 1 0.0000 0.952 1.00 0.00 0.00
#> 53 3 0.0000 0.928 0.00 0.00 1.00
#> 54 2 0.0000 1.000 0.00 1.00 0.00
#> 55 1 0.0000 0.952 1.00 0.00 0.00
#> 56 2 0.0000 1.000 0.00 1.00 0.00
#> 57 1 0.0000 0.952 1.00 0.00 0.00
#> 58 1 0.5397 0.605 0.72 0.28 0.00
#> 59 2 0.0000 1.000 0.00 1.00 0.00
#> 60 2 0.0000 1.000 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 2 1 0.1637 0.888 0.94 0.00 0.00 0.06
#> 3 4 0.4088 0.792 0.04 0.00 0.14 0.82
#> 4 1 0.1637 0.888 0.94 0.00 0.00 0.06
#> 5 1 0.3172 0.810 0.84 0.00 0.00 0.16
#> 6 4 0.4939 0.756 0.04 0.00 0.22 0.74
#> 7 1 0.1637 0.888 0.94 0.00 0.00 0.06
#> 8 3 0.3610 0.674 0.00 0.00 0.80 0.20
#> 9 1 0.1637 0.888 0.94 0.00 0.00 0.06
#> 10 4 0.4581 0.782 0.08 0.00 0.12 0.80
#> 11 1 0.4277 0.639 0.72 0.00 0.00 0.28
#> 12 1 0.4994 0.128 0.52 0.00 0.00 0.48
#> 13 4 0.3801 0.756 0.00 0.00 0.22 0.78
#> 14 4 0.4406 0.665 0.00 0.00 0.30 0.70
#> 15 3 0.4079 0.730 0.00 0.18 0.80 0.02
#> 16 3 0.0707 0.874 0.00 0.02 0.98 0.00
#> 17 4 0.8188 0.504 0.22 0.08 0.14 0.56
#> 18 4 0.3611 0.785 0.06 0.00 0.08 0.86
#> 19 4 0.7016 0.501 0.32 0.00 0.14 0.54
#> 20 3 0.0707 0.874 0.00 0.02 0.98 0.00
#> 21 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 22 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 23 3 0.1637 0.839 0.00 0.00 0.94 0.06
#> 24 3 0.0707 0.874 0.00 0.02 0.98 0.00
#> 25 1 0.0707 0.897 0.98 0.00 0.00 0.02
#> 26 1 0.1211 0.893 0.96 0.00 0.00 0.04
#> 27 3 0.2830 0.850 0.00 0.06 0.90 0.04
#> 28 3 0.3172 0.764 0.00 0.16 0.84 0.00
#> 29 3 0.2647 0.786 0.00 0.00 0.88 0.12
#> 30 2 0.1913 0.928 0.00 0.94 0.04 0.02
#> 31 1 0.1211 0.875 0.96 0.00 0.00 0.04
#> 32 1 0.3172 0.810 0.84 0.00 0.00 0.16
#> 33 3 0.1211 0.869 0.00 0.04 0.96 0.00
#> 34 2 0.3400 0.809 0.00 0.82 0.18 0.00
#> 35 2 0.0707 0.933 0.00 0.98 0.00 0.02
#> 36 2 0.1411 0.925 0.00 0.96 0.02 0.02
#> 37 2 0.0707 0.933 0.00 0.98 0.00 0.02
#> 38 2 0.0707 0.933 0.00 0.98 0.00 0.02
#> 39 2 0.0707 0.933 0.00 0.98 0.00 0.02
#> 40 2 0.0707 0.933 0.00 0.98 0.00 0.02
#> 41 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 42 2 0.1411 0.933 0.00 0.96 0.02 0.02
#> 43 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 44 2 0.2345 0.893 0.00 0.90 0.10 0.00
#> 45 2 0.3037 0.883 0.00 0.88 0.10 0.02
#> 46 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 47 2 0.0707 0.932 0.00 0.98 0.02 0.00
#> 48 2 0.3335 0.866 0.00 0.86 0.12 0.02
#> 49 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 50 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> 51 2 0.2345 0.893 0.00 0.90 0.10 0.00
#> 52 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 53 4 0.3172 0.771 0.00 0.00 0.16 0.84
#> 54 2 0.1211 0.931 0.00 0.96 0.04 0.00
#> 55 1 0.2011 0.876 0.92 0.00 0.00 0.08
#> 56 2 0.1211 0.931 0.00 0.96 0.04 0.00
#> 57 1 0.0000 0.899 1.00 0.00 0.00 0.00
#> 58 1 0.7355 0.345 0.58 0.26 0.02 0.14
#> 59 2 0.3606 0.827 0.00 0.84 0.02 0.14
#> 60 2 0.3335 0.846 0.00 0.86 0.02 0.12
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 60 0.633 2
#> ATC:skmeans 58 0.532 3
#> ATC:skmeans 58 0.569 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node03. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["032"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 8202 rows and 185 columns.
#> Top rows (820) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1 0.987 0.994 0.503 0.498 0.498
#> 3 3 1 0.978 0.992 0.274 0.804 0.628
#> 4 4 1 0.976 0.990 0.108 0.911 0.760
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 2 0.000 0.992 0.00 1.00
#> 2 2 0.000 0.992 0.00 1.00
#> 3 2 0.000 0.992 0.00 1.00
#> 4 2 0.000 0.992 0.00 1.00
#> 5 2 0.000 0.992 0.00 1.00
#> 6 2 0.000 0.992 0.00 1.00
#> 7 2 0.000 0.992 0.00 1.00
#> 8 2 0.000 0.992 0.00 1.00
#> 9 2 0.000 0.992 0.00 1.00
#> 10 2 0.000 0.992 0.00 1.00
#> 11 2 0.000 0.992 0.00 1.00
#> 12 2 0.000 0.992 0.00 1.00
#> 13 2 0.000 0.992 0.00 1.00
#> 14 2 0.000 0.992 0.00 1.00
#> 15 2 0.000 0.992 0.00 1.00
#> 16 2 0.000 0.992 0.00 1.00
#> 17 2 0.000 0.992 0.00 1.00
#> 18 2 0.000 0.992 0.00 1.00
#> 19 2 0.000 0.992 0.00 1.00
#> 20 2 0.000 0.992 0.00 1.00
#> 21 2 0.000 0.992 0.00 1.00
#> 22 1 0.000 0.997 1.00 0.00
#> 23 2 0.000 0.992 0.00 1.00
#> 24 1 0.000 0.997 1.00 0.00
#> 25 2 0.000 0.992 0.00 1.00
#> 26 2 0.242 0.952 0.04 0.96
#> 27 2 0.000 0.992 0.00 1.00
#> 28 2 0.000 0.992 0.00 1.00
#> 29 2 0.000 0.992 0.00 1.00
#> 30 2 0.000 0.992 0.00 1.00
#> 31 2 0.000 0.992 0.00 1.00
#> 32 2 0.000 0.992 0.00 1.00
#> 33 2 0.000 0.992 0.00 1.00
#> 34 2 0.000 0.992 0.00 1.00
#> 35 2 0.000 0.992 0.00 1.00
#> 36 2 0.000 0.992 0.00 1.00
#> 37 2 0.000 0.992 0.00 1.00
#> 38 2 0.000 0.992 0.00 1.00
#> 39 2 0.000 0.992 0.00 1.00
#> 40 2 0.000 0.992 0.00 1.00
#> 41 2 0.990 0.212 0.44 0.56
#> 42 1 0.000 0.997 1.00 0.00
#> 43 2 0.000 0.992 0.00 1.00
#> 44 1 0.327 0.935 0.94 0.06
#> 45 2 0.000 0.992 0.00 1.00
#> 46 2 0.000 0.992 0.00 1.00
#> 47 2 0.000 0.992 0.00 1.00
#> 48 2 0.000 0.992 0.00 1.00
#> 49 2 0.000 0.992 0.00 1.00
#> 50 2 0.000 0.992 0.00 1.00
#> 51 2 0.000 0.992 0.00 1.00
#> 52 1 0.000 0.997 1.00 0.00
#> 53 2 0.141 0.973 0.02 0.98
#> 54 1 0.000 0.997 1.00 0.00
#> 55 1 0.000 0.997 1.00 0.00
#> 56 1 0.722 0.749 0.80 0.20
#> 57 2 0.000 0.992 0.00 1.00
#> 58 1 0.000 0.997 1.00 0.00
#> 59 1 0.327 0.935 0.94 0.06
#> 60 2 0.000 0.992 0.00 1.00
#> 61 1 0.000 0.997 1.00 0.00
#> 62 2 0.000 0.992 0.00 1.00
#> 63 2 0.000 0.992 0.00 1.00
#> 64 2 0.000 0.992 0.00 1.00
#> 65 2 0.000 0.992 0.00 1.00
#> 66 2 0.722 0.749 0.20 0.80
#> 67 1 0.000 0.997 1.00 0.00
#> 68 1 0.000 0.997 1.00 0.00
#> 69 1 0.000 0.997 1.00 0.00
#> 70 1 0.000 0.997 1.00 0.00
#> 71 1 0.000 0.997 1.00 0.00
#> 72 2 0.000 0.992 0.00 1.00
#> 73 1 0.000 0.997 1.00 0.00
#> 74 2 0.000 0.992 0.00 1.00
#> 75 1 0.000 0.997 1.00 0.00
#> 76 1 0.000 0.997 1.00 0.00
#> 77 2 0.000 0.992 0.00 1.00
#> 78 2 0.000 0.992 0.00 1.00
#> 79 2 0.000 0.992 0.00 1.00
#> 80 1 0.000 0.997 1.00 0.00
#> 81 1 0.000 0.997 1.00 0.00
#> 82 2 0.000 0.992 0.00 1.00
#> 83 2 0.000 0.992 0.00 1.00
#> 84 2 0.000 0.992 0.00 1.00
#> 85 2 0.000 0.992 0.00 1.00
#> 86 1 0.000 0.997 1.00 0.00
#> 87 2 0.000 0.992 0.00 1.00
#> 88 2 0.000 0.992 0.00 1.00
#> 89 2 0.000 0.992 0.00 1.00
#> 90 2 0.000 0.992 0.00 1.00
#> 91 2 0.000 0.992 0.00 1.00
#> 92 2 0.000 0.992 0.00 1.00
#> 93 2 0.000 0.992 0.00 1.00
#> 94 2 0.000 0.992 0.00 1.00
#> 95 2 0.000 0.992 0.00 1.00
#> 96 2 0.000 0.992 0.00 1.00
#> 97 2 0.000 0.992 0.00 1.00
#> 98 2 0.000 0.992 0.00 1.00
#> 99 2 0.000 0.992 0.00 1.00
#> 100 2 0.000 0.992 0.00 1.00
#> 101 2 0.000 0.992 0.00 1.00
#> 102 2 0.000 0.992 0.00 1.00
#> 103 2 0.000 0.992 0.00 1.00
#> 104 2 0.000 0.992 0.00 1.00
#> 105 2 0.000 0.992 0.00 1.00
#> 106 2 0.000 0.992 0.00 1.00
#> 107 2 0.000 0.992 0.00 1.00
#> 108 2 0.000 0.992 0.00 1.00
#> 109 2 0.000 0.992 0.00 1.00
#> 110 2 0.000 0.992 0.00 1.00
#> 111 2 0.000 0.992 0.00 1.00
#> 112 1 0.000 0.997 1.00 0.00
#> 113 1 0.000 0.997 1.00 0.00
#> 114 1 0.000 0.997 1.00 0.00
#> 115 1 0.000 0.997 1.00 0.00
#> 116 1 0.000 0.997 1.00 0.00
#> 117 1 0.000 0.997 1.00 0.00
#> 118 1 0.000 0.997 1.00 0.00
#> 119 1 0.000 0.997 1.00 0.00
#> 120 1 0.000 0.997 1.00 0.00
#> 121 1 0.000 0.997 1.00 0.00
#> 122 1 0.000 0.997 1.00 0.00
#> 123 1 0.000 0.997 1.00 0.00
#> 124 1 0.000 0.997 1.00 0.00
#> 125 1 0.000 0.997 1.00 0.00
#> 126 1 0.000 0.997 1.00 0.00
#> 127 1 0.000 0.997 1.00 0.00
#> 128 1 0.000 0.997 1.00 0.00
#> 129 1 0.000 0.997 1.00 0.00
#> 130 1 0.000 0.997 1.00 0.00
#> 131 1 0.000 0.997 1.00 0.00
#> 132 1 0.000 0.997 1.00 0.00
#> 133 1 0.000 0.997 1.00 0.00
#> 134 1 0.000 0.997 1.00 0.00
#> 135 1 0.000 0.997 1.00 0.00
#> 136 1 0.000 0.997 1.00 0.00
#> 137 1 0.000 0.997 1.00 0.00
#> 138 1 0.000 0.997 1.00 0.00
#> 139 1 0.000 0.997 1.00 0.00
#> 140 1 0.000 0.997 1.00 0.00
#> 141 1 0.000 0.997 1.00 0.00
#> 142 1 0.000 0.997 1.00 0.00
#> 143 1 0.000 0.997 1.00 0.00
#> 144 1 0.000 0.997 1.00 0.00
#> 145 1 0.000 0.997 1.00 0.00
#> 146 1 0.000 0.997 1.00 0.00
#> 147 1 0.000 0.997 1.00 0.00
#> 148 1 0.000 0.997 1.00 0.00
#> 149 1 0.000 0.997 1.00 0.00
#> 150 1 0.000 0.997 1.00 0.00
#> 151 1 0.000 0.997 1.00 0.00
#> 152 1 0.000 0.997 1.00 0.00
#> 153 1 0.000 0.997 1.00 0.00
#> 154 1 0.000 0.997 1.00 0.00
#> 155 1 0.000 0.997 1.00 0.00
#> 156 1 0.000 0.997 1.00 0.00
#> 157 1 0.000 0.997 1.00 0.00
#> 158 1 0.000 0.997 1.00 0.00
#> 159 1 0.000 0.997 1.00 0.00
#> 160 2 0.000 0.992 0.00 1.00
#> 161 1 0.000 0.997 1.00 0.00
#> 162 1 0.000 0.997 1.00 0.00
#> 163 1 0.000 0.997 1.00 0.00
#> 164 1 0.000 0.997 1.00 0.00
#> 165 1 0.000 0.997 1.00 0.00
#> 166 1 0.000 0.997 1.00 0.00
#> 167 1 0.000 0.997 1.00 0.00
#> 168 1 0.000 0.997 1.00 0.00
#> 169 1 0.000 0.997 1.00 0.00
#> 170 1 0.000 0.997 1.00 0.00
#> 171 1 0.000 0.997 1.00 0.00
#> 172 1 0.000 0.997 1.00 0.00
#> 173 1 0.000 0.997 1.00 0.00
#> 174 1 0.000 0.997 1.00 0.00
#> 175 1 0.000 0.997 1.00 0.00
#> 176 1 0.000 0.997 1.00 0.00
#> 177 1 0.000 0.997 1.00 0.00
#> 178 1 0.000 0.997 1.00 0.00
#> 179 1 0.000 0.997 1.00 0.00
#> 180 1 0.000 0.997 1.00 0.00
#> 181 1 0.000 0.997 1.00 0.00
#> 182 1 0.000 0.997 1.00 0.00
#> 183 1 0.000 0.997 1.00 0.00
#> 184 1 0.000 0.997 1.00 0.00
#> 185 1 0.000 0.997 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 2 0.0000 0.992 0.00 1.00 0.00
#> 2 2 0.0000 0.992 0.00 1.00 0.00
#> 3 2 0.0000 0.992 0.00 1.00 0.00
#> 4 2 0.0000 0.992 0.00 1.00 0.00
#> 5 2 0.0000 0.992 0.00 1.00 0.00
#> 6 2 0.0000 0.992 0.00 1.00 0.00
#> 7 2 0.0000 0.992 0.00 1.00 0.00
#> 8 2 0.0000 0.992 0.00 1.00 0.00
#> 9 2 0.0000 0.992 0.00 1.00 0.00
#> 10 2 0.0000 0.992 0.00 1.00 0.00
#> 11 2 0.0000 0.992 0.00 1.00 0.00
#> 12 2 0.0000 0.992 0.00 1.00 0.00
#> 13 2 0.0000 0.992 0.00 1.00 0.00
#> 14 2 0.0000 0.992 0.00 1.00 0.00
#> 15 2 0.0000 0.992 0.00 1.00 0.00
#> 16 2 0.0000 0.992 0.00 1.00 0.00
#> 17 2 0.0000 0.992 0.00 1.00 0.00
#> 18 2 0.0000 0.992 0.00 1.00 0.00
#> 19 2 0.0000 0.992 0.00 1.00 0.00
#> 20 2 0.0000 0.992 0.00 1.00 0.00
#> 21 2 0.0000 0.992 0.00 1.00 0.00
#> 22 1 0.0892 0.967 0.98 0.02 0.00
#> 23 2 0.0000 0.992 0.00 1.00 0.00
#> 24 1 0.0000 0.988 1.00 0.00 0.00
#> 25 2 0.0000 0.992 0.00 1.00 0.00
#> 26 2 0.0000 0.992 0.00 1.00 0.00
#> 27 2 0.0000 0.992 0.00 1.00 0.00
#> 28 2 0.0000 0.992 0.00 1.00 0.00
#> 29 2 0.0000 0.992 0.00 1.00 0.00
#> 30 2 0.0000 0.992 0.00 1.00 0.00
#> 31 2 0.0000 0.992 0.00 1.00 0.00
#> 32 2 0.0000 0.992 0.00 1.00 0.00
#> 33 2 0.0000 0.992 0.00 1.00 0.00
#> 34 2 0.0000 0.992 0.00 1.00 0.00
#> 35 2 0.0000 0.992 0.00 1.00 0.00
#> 36 2 0.0000 0.992 0.00 1.00 0.00
#> 37 2 0.0000 0.992 0.00 1.00 0.00
#> 38 2 0.0000 0.992 0.00 1.00 0.00
#> 39 2 0.0000 0.992 0.00 1.00 0.00
#> 40 2 0.0000 0.992 0.00 1.00 0.00
#> 41 2 0.0000 0.992 0.00 1.00 0.00
#> 42 2 0.0000 0.992 0.00 1.00 0.00
#> 43 2 0.0000 0.992 0.00 1.00 0.00
#> 44 2 0.0000 0.992 0.00 1.00 0.00
#> 45 2 0.0000 0.992 0.00 1.00 0.00
#> 46 2 0.0000 0.992 0.00 1.00 0.00
#> 47 2 0.0000 0.992 0.00 1.00 0.00
#> 48 2 0.0000 0.992 0.00 1.00 0.00
#> 49 2 0.0892 0.972 0.00 0.98 0.02
#> 50 2 0.0000 0.992 0.00 1.00 0.00
#> 51 2 0.0000 0.992 0.00 1.00 0.00
#> 52 1 0.0000 0.988 1.00 0.00 0.00
#> 53 2 0.0000 0.992 0.00 1.00 0.00
#> 54 2 0.0000 0.992 0.00 1.00 0.00
#> 55 1 0.0000 0.988 1.00 0.00 0.00
#> 56 2 0.0000 0.992 0.00 1.00 0.00
#> 57 2 0.0000 0.992 0.00 1.00 0.00
#> 58 1 0.0000 0.988 1.00 0.00 0.00
#> 59 2 0.0000 0.992 0.00 1.00 0.00
#> 60 2 0.0000 0.992 0.00 1.00 0.00
#> 61 2 0.0000 0.992 0.00 1.00 0.00
#> 62 3 0.0000 0.996 0.00 0.00 1.00
#> 63 2 0.0000 0.992 0.00 1.00 0.00
#> 64 2 0.0000 0.992 0.00 1.00 0.00
#> 65 2 0.0000 0.992 0.00 1.00 0.00
#> 66 2 0.9372 0.280 0.30 0.50 0.20
#> 67 1 0.0000 0.988 1.00 0.00 0.00
#> 68 1 0.0000 0.988 1.00 0.00 0.00
#> 69 1 0.0000 0.988 1.00 0.00 0.00
#> 70 1 0.0000 0.988 1.00 0.00 0.00
#> 71 1 0.0000 0.988 1.00 0.00 0.00
#> 72 3 0.0000 0.996 0.00 0.00 1.00
#> 73 1 0.0000 0.988 1.00 0.00 0.00
#> 74 2 0.0000 0.992 0.00 1.00 0.00
#> 75 3 0.0000 0.996 0.00 0.00 1.00
#> 76 3 0.0892 0.976 0.02 0.00 0.98
#> 77 3 0.0000 0.996 0.00 0.00 1.00
#> 78 3 0.0000 0.996 0.00 0.00 1.00
#> 79 2 0.0000 0.992 0.00 1.00 0.00
#> 80 3 0.0000 0.996 0.00 0.00 1.00
#> 81 3 0.0000 0.996 0.00 0.00 1.00
#> 82 3 0.0000 0.996 0.00 0.00 1.00
#> 83 3 0.0000 0.996 0.00 0.00 1.00
#> 84 3 0.0000 0.996 0.00 0.00 1.00
#> 85 2 0.0000 0.992 0.00 1.00 0.00
#> 86 1 0.0892 0.968 0.98 0.00 0.02
#> 87 3 0.0000 0.996 0.00 0.00 1.00
#> 88 3 0.0000 0.996 0.00 0.00 1.00
#> 89 3 0.0000 0.996 0.00 0.00 1.00
#> 90 3 0.0000 0.996 0.00 0.00 1.00
#> 91 3 0.0000 0.996 0.00 0.00 1.00
#> 92 3 0.0000 0.996 0.00 0.00 1.00
#> 93 3 0.0000 0.996 0.00 0.00 1.00
#> 94 3 0.0000 0.996 0.00 0.00 1.00
#> 95 3 0.0000 0.996 0.00 0.00 1.00
#> 96 3 0.0000 0.996 0.00 0.00 1.00
#> 97 3 0.0000 0.996 0.00 0.00 1.00
#> 98 3 0.0000 0.996 0.00 0.00 1.00
#> 99 3 0.0000 0.996 0.00 0.00 1.00
#> 100 3 0.0000 0.996 0.00 0.00 1.00
#> 101 3 0.0000 0.996 0.00 0.00 1.00
#> 102 3 0.0000 0.996 0.00 0.00 1.00
#> 103 3 0.0000 0.996 0.00 0.00 1.00
#> 104 3 0.0000 0.996 0.00 0.00 1.00
#> 105 3 0.0000 0.996 0.00 0.00 1.00
#> 106 3 0.0000 0.996 0.00 0.00 1.00
#> 107 3 0.0000 0.996 0.00 0.00 1.00
#> 108 3 0.0000 0.996 0.00 0.00 1.00
#> 109 3 0.0000 0.996 0.00 0.00 1.00
#> 110 3 0.0000 0.996 0.00 0.00 1.00
#> 111 3 0.0000 0.996 0.00 0.00 1.00
#> 112 1 0.0000 0.988 1.00 0.00 0.00
#> 113 1 0.0000 0.988 1.00 0.00 0.00
#> 114 1 0.0000 0.988 1.00 0.00 0.00
#> 115 1 0.0000 0.988 1.00 0.00 0.00
#> 116 1 0.0000 0.988 1.00 0.00 0.00
#> 117 1 0.0000 0.988 1.00 0.00 0.00
#> 118 1 0.0000 0.988 1.00 0.00 0.00
#> 119 1 0.0000 0.988 1.00 0.00 0.00
#> 120 1 0.0000 0.988 1.00 0.00 0.00
#> 121 1 0.0000 0.988 1.00 0.00 0.00
#> 122 1 0.0000 0.988 1.00 0.00 0.00
#> 123 1 0.0000 0.988 1.00 0.00 0.00
#> 124 1 0.0000 0.988 1.00 0.00 0.00
#> 125 1 0.0000 0.988 1.00 0.00 0.00
#> 126 1 0.0000 0.988 1.00 0.00 0.00
#> 127 1 0.0000 0.988 1.00 0.00 0.00
#> 128 1 0.0000 0.988 1.00 0.00 0.00
#> 129 1 0.0000 0.988 1.00 0.00 0.00
#> 130 1 0.0000 0.988 1.00 0.00 0.00
#> 131 1 0.0000 0.988 1.00 0.00 0.00
#> 132 1 0.0000 0.988 1.00 0.00 0.00
#> 133 1 0.0000 0.988 1.00 0.00 0.00
#> 134 1 0.0000 0.988 1.00 0.00 0.00
#> 135 1 0.0000 0.988 1.00 0.00 0.00
#> 136 1 0.0000 0.988 1.00 0.00 0.00
#> 137 1 0.0000 0.988 1.00 0.00 0.00
#> 138 1 0.0000 0.988 1.00 0.00 0.00
#> 139 1 0.0000 0.988 1.00 0.00 0.00
#> 140 1 0.0000 0.988 1.00 0.00 0.00
#> 141 1 0.0000 0.988 1.00 0.00 0.00
#> 142 1 0.0000 0.988 1.00 0.00 0.00
#> 143 1 0.0000 0.988 1.00 0.00 0.00
#> 144 1 0.0000 0.988 1.00 0.00 0.00
#> 145 1 0.0000 0.988 1.00 0.00 0.00
#> 146 1 0.0000 0.988 1.00 0.00 0.00
#> 147 1 0.0000 0.988 1.00 0.00 0.00
#> 148 1 0.0000 0.988 1.00 0.00 0.00
#> 149 1 0.0000 0.988 1.00 0.00 0.00
#> 150 1 0.6126 0.334 0.60 0.40 0.00
#> 151 1 0.0000 0.988 1.00 0.00 0.00
#> 152 1 0.0000 0.988 1.00 0.00 0.00
#> 153 1 0.0000 0.988 1.00 0.00 0.00
#> 154 1 0.0000 0.988 1.00 0.00 0.00
#> 155 1 0.0000 0.988 1.00 0.00 0.00
#> 156 1 0.0000 0.988 1.00 0.00 0.00
#> 157 1 0.0000 0.988 1.00 0.00 0.00
#> 158 1 0.0000 0.988 1.00 0.00 0.00
#> 159 1 0.0000 0.988 1.00 0.00 0.00
#> 160 3 0.0000 0.996 0.00 0.00 1.00
#> 161 1 0.0000 0.988 1.00 0.00 0.00
#> 162 1 0.0000 0.988 1.00 0.00 0.00
#> 163 1 0.0000 0.988 1.00 0.00 0.00
#> 164 1 0.0000 0.988 1.00 0.00 0.00
#> 165 1 0.0000 0.988 1.00 0.00 0.00
#> 166 1 0.0000 0.988 1.00 0.00 0.00
#> 167 1 0.0000 0.988 1.00 0.00 0.00
#> 168 1 0.0000 0.988 1.00 0.00 0.00
#> 169 1 0.0000 0.988 1.00 0.00 0.00
#> 170 1 0.0000 0.988 1.00 0.00 0.00
#> 171 1 0.0000 0.988 1.00 0.00 0.00
#> 172 1 0.6280 0.150 0.54 0.46 0.00
#> 173 1 0.0000 0.988 1.00 0.00 0.00
#> 174 1 0.0000 0.988 1.00 0.00 0.00
#> 175 1 0.0000 0.988 1.00 0.00 0.00
#> 176 1 0.0000 0.988 1.00 0.00 0.00
#> 177 1 0.0000 0.988 1.00 0.00 0.00
#> 178 1 0.0000 0.988 1.00 0.00 0.00
#> 179 1 0.0000 0.988 1.00 0.00 0.00
#> 180 1 0.0000 0.988 1.00 0.00 0.00
#> 181 1 0.0000 0.988 1.00 0.00 0.00
#> 182 1 0.0000 0.988 1.00 0.00 0.00
#> 183 1 0.0000 0.988 1.00 0.00 0.00
#> 184 3 0.2959 0.887 0.10 0.00 0.90
#> 185 1 0.0000 0.988 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 2 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 3 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 4 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 5 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 6 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 7 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 8 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 9 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 10 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 11 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 12 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 13 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 14 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 15 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 16 2 0.0707 0.976 0.00 0.98 0.00 0.02
#> 17 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 18 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 19 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 20 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 21 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 22 1 0.2335 0.911 0.92 0.06 0.00 0.02
#> 23 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 24 4 0.2011 0.853 0.08 0.00 0.00 0.92
#> 25 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 26 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 27 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 28 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 29 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 30 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 31 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 32 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 33 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 34 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 35 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 36 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 37 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 38 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 39 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 40 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 41 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 42 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 43 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 44 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 45 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 46 4 0.4522 0.558 0.00 0.32 0.00 0.68
#> 47 4 0.4522 0.558 0.00 0.32 0.00 0.68
#> 48 4 0.4522 0.558 0.00 0.32 0.00 0.68
#> 49 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 50 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 51 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 52 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 53 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 54 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 55 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 56 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 57 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 58 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 59 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 60 4 0.1637 0.889 0.00 0.06 0.00 0.94
#> 61 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 62 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 63 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 64 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 65 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 66 2 0.2011 0.882 0.08 0.92 0.00 0.00
#> 67 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 68 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 69 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 70 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 71 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 72 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 73 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 74 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 75 3 0.0707 0.969 0.02 0.00 0.98 0.00
#> 76 3 0.1637 0.915 0.06 0.00 0.94 0.00
#> 77 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 78 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 79 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 80 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 81 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 82 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 83 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 84 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 85 2 0.0000 0.997 0.00 1.00 0.00 0.00
#> 86 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 87 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 88 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 89 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 90 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 91 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 92 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 93 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 94 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 95 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 96 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 97 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 98 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 99 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 100 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 101 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 102 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 103 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 104 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 105 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 106 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 107 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 108 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 109 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 110 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 111 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 112 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 113 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 114 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 115 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 116 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 117 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 118 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 119 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 120 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 121 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 122 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 123 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 124 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 125 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 126 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 127 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 128 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 129 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 130 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 131 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 132 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 133 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 134 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 135 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 136 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 137 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 138 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 139 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 140 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 141 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 142 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 143 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 144 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 145 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 146 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 147 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 148 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 149 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 150 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 151 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 152 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 153 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 154 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 155 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 156 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 157 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 158 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 159 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 160 3 0.0000 0.993 0.00 0.00 1.00 0.00
#> 161 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 162 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 163 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 164 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 165 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 166 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 167 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 168 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 169 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 170 4 0.4790 0.379 0.38 0.00 0.00 0.62
#> 171 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 172 4 0.0000 0.933 0.00 0.00 0.00 1.00
#> 173 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 174 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 175 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 176 1 0.0707 0.978 0.98 0.00 0.00 0.02
#> 177 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 178 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 179 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 180 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 181 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 182 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 183 1 0.0000 0.999 1.00 0.00 0.00 0.00
#> 184 3 0.2345 0.857 0.10 0.00 0.90 0.00
#> 185 1 0.0000 0.999 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 184 8.28e-24 2
#> ATC:skmeans 182 5.76e-54 3
#> ATC:skmeans 184 1.87e-53 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node03. Child nodes: Node0111-leaf , Node0112-leaf , Node0113 , Node0121 , Node0122 , Node0123 , Node0131-leaf , Node0132-leaf , Node0141-leaf , Node0142-leaf , Node0143-leaf , Node0211 , Node0212 , Node0221-leaf , Node0222 , Node0223-leaf , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0234-leaf , Node0311 , Node0312 , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0323-leaf , Node0324-leaf , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["033"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 7257 rows and 61 columns.
#> Top rows (671) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1 0.990 0.995 0.5057 0.495 0.495
#> 3 3 1 0.989 0.995 0.3125 0.780 0.581
#> 4 4 1 0.981 0.991 0.0875 0.915 0.757
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> 1 1 0.141 0.977 0.98 0.02
#> 2 1 0.000 0.993 1.00 0.00
#> 3 2 0.000 0.997 0.00 1.00
#> 4 2 0.000 0.997 0.00 1.00
#> 5 2 0.000 0.997 0.00 1.00
#> 6 1 0.000 0.993 1.00 0.00
#> 7 2 0.141 0.980 0.02 0.98
#> 8 2 0.000 0.997 0.00 1.00
#> 9 2 0.000 0.997 0.00 1.00
#> 10 1 0.000 0.993 1.00 0.00
#> 11 2 0.000 0.997 0.00 1.00
#> 12 1 0.000 0.993 1.00 0.00
#> 13 1 0.634 0.816 0.84 0.16
#> 14 2 0.000 0.997 0.00 1.00
#> 15 1 0.000 0.993 1.00 0.00
#> 16 2 0.000 0.997 0.00 1.00
#> 17 1 0.000 0.993 1.00 0.00
#> 18 1 0.000 0.993 1.00 0.00
#> 19 1 0.000 0.993 1.00 0.00
#> 20 1 0.000 0.993 1.00 0.00
#> 21 1 0.000 0.993 1.00 0.00
#> 22 1 0.000 0.993 1.00 0.00
#> 23 2 0.000 0.997 0.00 1.00
#> 24 1 0.000 0.993 1.00 0.00
#> 25 1 0.000 0.993 1.00 0.00
#> 26 1 0.141 0.977 0.98 0.02
#> 27 1 0.000 0.993 1.00 0.00
#> 28 1 0.000 0.993 1.00 0.00
#> 29 2 0.000 0.997 0.00 1.00
#> 30 1 0.141 0.977 0.98 0.02
#> 31 1 0.000 0.993 1.00 0.00
#> 32 1 0.000 0.993 1.00 0.00
#> 33 2 0.000 0.997 0.00 1.00
#> 34 2 0.000 0.997 0.00 1.00
#> 35 2 0.000 0.997 0.00 1.00
#> 36 2 0.000 0.997 0.00 1.00
#> 37 2 0.141 0.980 0.02 0.98
#> 38 2 0.000 0.997 0.00 1.00
#> 39 2 0.000 0.997 0.00 1.00
#> 40 1 0.000 0.993 1.00 0.00
#> 41 2 0.000 0.997 0.00 1.00
#> 42 1 0.000 0.993 1.00 0.00
#> 43 2 0.000 0.997 0.00 1.00
#> 44 1 0.000 0.993 1.00 0.00
#> 45 2 0.000 0.997 0.00 1.00
#> 46 1 0.000 0.993 1.00 0.00
#> 47 2 0.000 0.997 0.00 1.00
#> 48 1 0.000 0.993 1.00 0.00
#> 49 1 0.000 0.993 1.00 0.00
#> 50 2 0.000 0.997 0.00 1.00
#> 51 1 0.000 0.993 1.00 0.00
#> 52 1 0.000 0.993 1.00 0.00
#> 53 1 0.000 0.993 1.00 0.00
#> 54 1 0.000 0.993 1.00 0.00
#> 55 1 0.000 0.993 1.00 0.00
#> 56 2 0.000 0.997 0.00 1.00
#> 57 2 0.000 0.997 0.00 1.00
#> 58 2 0.000 0.997 0.00 1.00
#> 59 2 0.242 0.961 0.04 0.96
#> 60 1 0.000 0.993 1.00 0.00
#> 61 2 0.000 0.997 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> 1 3 0.000 0.988 0.00 0.00 1.00
#> 2 1 0.000 1.000 1.00 0.00 0.00
#> 3 3 0.000 0.988 0.00 0.00 1.00
#> 4 2 0.153 0.961 0.00 0.96 0.04
#> 5 2 0.153 0.961 0.00 0.96 0.04
#> 6 1 0.000 1.000 1.00 0.00 0.00
#> 7 2 0.000 0.992 0.00 1.00 0.00
#> 8 2 0.153 0.961 0.00 0.96 0.04
#> 9 2 0.000 0.992 0.00 1.00 0.00
#> 10 1 0.000 1.000 1.00 0.00 0.00
#> 11 3 0.000 0.988 0.00 0.00 1.00
#> 12 1 0.000 1.000 1.00 0.00 0.00
#> 13 3 0.000 0.988 0.00 0.00 1.00
#> 14 3 0.400 0.808 0.00 0.16 0.84
#> 15 1 0.000 1.000 1.00 0.00 0.00
#> 16 3 0.000 0.988 0.00 0.00 1.00
#> 17 1 0.000 1.000 1.00 0.00 0.00
#> 18 1 0.000 1.000 1.00 0.00 0.00
#> 19 1 0.000 1.000 1.00 0.00 0.00
#> 20 1 0.000 1.000 1.00 0.00 0.00
#> 21 1 0.000 1.000 1.00 0.00 0.00
#> 22 1 0.000 1.000 1.00 0.00 0.00
#> 23 2 0.000 0.992 0.00 1.00 0.00
#> 24 1 0.000 1.000 1.00 0.00 0.00
#> 25 3 0.000 0.988 0.00 0.00 1.00
#> 26 3 0.000 0.988 0.00 0.00 1.00
#> 27 3 0.000 0.988 0.00 0.00 1.00
#> 28 3 0.000 0.988 0.00 0.00 1.00
#> 29 3 0.000 0.988 0.00 0.00 1.00
#> 30 3 0.000 0.988 0.00 0.00 1.00
#> 31 1 0.000 1.000 1.00 0.00 0.00
#> 32 3 0.000 0.988 0.00 0.00 1.00
#> 33 3 0.000 0.988 0.00 0.00 1.00
#> 34 2 0.000 0.992 0.00 1.00 0.00
#> 35 2 0.000 0.992 0.00 1.00 0.00
#> 36 2 0.000 0.992 0.00 1.00 0.00
#> 37 2 0.000 0.992 0.00 1.00 0.00
#> 38 2 0.000 0.992 0.00 1.00 0.00
#> 39 2 0.000 0.992 0.00 1.00 0.00
#> 40 1 0.000 1.000 1.00 0.00 0.00
#> 41 2 0.000 0.992 0.00 1.00 0.00
#> 42 1 0.000 1.000 1.00 0.00 0.00
#> 43 2 0.000 0.992 0.00 1.00 0.00
#> 44 1 0.000 1.000 1.00 0.00 0.00
#> 45 2 0.000 0.992 0.00 1.00 0.00
#> 46 1 0.000 1.000 1.00 0.00 0.00
#> 47 2 0.000 0.992 0.00 1.00 0.00
#> 48 1 0.000 1.000 1.00 0.00 0.00
#> 49 1 0.000 1.000 1.00 0.00 0.00
#> 50 2 0.000 0.992 0.00 1.00 0.00
#> 51 1 0.000 1.000 1.00 0.00 0.00
#> 52 1 0.000 1.000 1.00 0.00 0.00
#> 53 1 0.000 1.000 1.00 0.00 0.00
#> 54 1 0.000 1.000 1.00 0.00 0.00
#> 55 1 0.000 1.000 1.00 0.00 0.00
#> 56 2 0.000 0.992 0.00 1.00 0.00
#> 57 2 0.000 0.992 0.00 1.00 0.00
#> 58 3 0.000 0.988 0.00 0.00 1.00
#> 59 2 0.153 0.951 0.04 0.96 0.00
#> 60 1 0.000 1.000 1.00 0.00 0.00
#> 61 2 0.000 0.992 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> 1 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 2 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 3 2 0.340 0.775 0.00 0.82 0.18 0.00
#> 4 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 5 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 6 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 7 4 0.000 1.000 0.00 0.00 0.00 1.00
#> 8 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 9 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 10 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 11 3 0.317 0.802 0.00 0.16 0.84 0.00
#> 12 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 13 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 14 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 15 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 16 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 17 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 18 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 19 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 20 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 21 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 22 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 23 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 24 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 25 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 26 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 27 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 28 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 29 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 30 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 31 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 32 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 33 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 34 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 35 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 36 4 0.000 1.000 0.00 0.00 0.00 1.00
#> 37 4 0.000 1.000 0.00 0.00 0.00 1.00
#> 38 4 0.000 1.000 0.00 0.00 0.00 1.00
#> 39 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 40 1 0.164 0.936 0.94 0.00 0.00 0.06
#> 41 2 0.265 0.857 0.00 0.88 0.00 0.12
#> 42 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 43 4 0.000 1.000 0.00 0.00 0.00 1.00
#> 44 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 45 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 46 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 47 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 48 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 49 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 50 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 51 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 52 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 53 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 54 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 55 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 56 2 0.000 0.977 0.00 1.00 0.00 0.00
#> 57 4 0.000 1.000 0.00 0.00 0.00 1.00
#> 58 3 0.000 0.985 0.00 0.00 1.00 0.00
#> 59 4 0.000 1.000 0.00 0.00 0.00 1.00
#> 60 1 0.000 0.997 1.00 0.00 0.00 0.00
#> 61 4 0.000 1.000 0.00 0.00 0.00 1.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample level1.class(p-value) k
#> ATC:skmeans 61 0.1069 2
#> ATC:skmeans 61 0.1849 3
#> ATC:skmeans 61 0.0659 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
#> [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid parallel stats4 stats graphics grDevices utils datasets methods
#> [10] base
#>
#> other attached packages:
#> [1] genefilter_1.74.0 ComplexHeatmap_2.8.0 markdown_1.1
#> [4] knitr_1.33 scRNAseq_2.6.1 SingleCellExperiment_1.14.1
#> [7] SummarizedExperiment_1.22.0 Biobase_2.52.0 GenomicRanges_1.44.0
#> [10] GenomeInfoDb_1.28.1 IRanges_2.26.0 S4Vectors_0.30.0
#> [13] BiocGenerics_0.38.0 MatrixGenerics_1.4.0 matrixStats_0.59.0
#> [16] cola_1.9.4
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.13 AnnotationHub_3.0.1 BiocFileCache_2.0.0
#> [4] lazyeval_0.2.2 polylabelr_0.2.0 splines_4.1.0
#> [7] Polychrome_1.3.1 BiocParallel_1.26.1 ggplot2_3.3.5
#> [10] digest_0.6.27 foreach_1.5.1 ensembldb_2.16.3
#> [13] htmltools_0.5.1.1 viridis_0.6.1 fansi_0.5.0
#> [16] magrittr_2.0.1 memoise_2.0.0 cluster_2.1.2
#> [19] doParallel_1.0.16 Biostrings_2.60.1 annotate_1.70.0
#> [22] askpass_1.1 prettyunits_1.1.1 colorspace_2.0-2
#> [25] blob_1.2.1 rappdirs_0.3.3 xfun_0.24
#> [28] dplyr_1.0.7 crayon_1.4.1 RCurl_1.98-1.3
#> [31] microbenchmark_1.4-7 jsonlite_1.7.2 impute_1.66.0
#> [34] brew_1.0-6 survival_3.2-11 iterators_1.0.13
#> [37] glue_1.4.2 polyclip_1.10-0 gtable_0.3.0
#> [40] zlibbioc_1.38.0 XVector_0.32.0 GetoptLong_1.0.5
#> [43] DelayedArray_0.18.0 shape_1.4.6 scales_1.1.1
#> [46] data.tree_1.0.0 DBI_1.1.1 Rcpp_1.0.7
#> [49] viridisLite_0.4.0 xtable_1.8-4 progress_1.2.2
#> [52] clue_0.3-59 reticulate_1.20 bit_4.0.4
#> [55] mclust_5.4.7 umap_0.2.7.0 httr_1.4.2
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#> [61] XML_3.99-0.6 dbplyr_2.1.1 utf8_1.2.1
#> [64] tidyselect_1.1.1 rlang_0.4.11 later_1.2.0
#> [67] AnnotationDbi_1.54.1 munsell_0.5.0 BiocVersion_3.13.1
#> [70] tools_4.1.0 cachem_1.0.5 generics_0.1.0
#> [73] RSQLite_2.2.7 ExperimentHub_2.0.0 evaluate_0.14
#> [76] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1
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#> [97] GenomicFeatures_1.44.0 RSpectra_0.16-0 lattice_0.20-44
#> [100] ProtGenerics_1.24.0 Matrix_1.3-4 vctrs_0.3.8
#> [103] pillar_1.6.1 lifecycle_1.0.0 BiocManager_1.30.16
#> [106] eulerr_6.1.0 GlobalOptions_0.1.2 bitops_1.0-7
#> [109] irlba_2.3.3 httpuv_1.6.1 rtracklayer_1.52.0
#> [112] R6_2.5.0 BiocIO_1.2.0 promises_1.2.0.1
#> [115] gridExtra_2.3 codetools_0.2-18 assertthat_0.2.1
#> [118] openssl_1.4.4 rjson_0.2.20 GenomicAlignments_1.28.0
#> [121] Rsamtools_2.8.0 GenomeInfoDbData_1.2.6 hms_1.1.0
#> [124] skmeans_0.2-13 Cairo_1.5-12.2 scatterplot3d_0.3-41
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