cutree for dendrogram (by 1 height only!)
Cuts a dendrogram tree into several groups by specifying the desired cut height (only a single height!).
cutree_1h.dendrogram(
dend,
h,
order_clusters_as_data = TRUE,
use_labels_not_values = TRUE,
warn = dendextend_options("warn"),
...
)dend |
a dendrogram object |
h |
numeric scalar (NOT a vector) with a height where the dend should be cut. |
order_clusters_as_data |
logical, defaults to TRUE. There are two ways by which to order the clusters: 1) By the order of the original data. 2) by the order of the labels in the dendrogram. In order to be consistent with cutree, this is set to TRUE. |
use_labels_not_values |
logical, defaults to TRUE. If the actual labels of the clusters do not matter - and we want to gain speed (say, 10 times faster) - then use FALSE (gives the "leaves order" instead of their labels.). |
warn |
logical (default from dendextend_options("warn") is FALSE). Set if warning are to be issued, it is safer to keep this at TRUE, but for keeping the noise down, the default is FALSE. |
... |
(not currently in use) |
cutree_1h.dendrogram returns an integer vector with group memberships
Tal Galili
hc <- hclust(dist(USArrests[c(1, 6, 13, 20, 23), ]), "ave") dend <- as.dendrogram(hc) cutree(hc, h = 50) # on hclust cutree_1h.dendrogram(dend, h = 50) # on a dendrogram labels(dend) # the default (ordered by original data's order) cutree_1h.dendrogram(dend, h = 50, order_clusters_as_data = TRUE) # A different order of labels - order by their order in the tree cutree_1h.dendrogram(dend, h = 50, order_clusters_as_data = FALSE) # make it faster ## Not run: library(microbenchmark) microbenchmark( cutree_1h.dendrogram(dend, h = 50), cutree_1h.dendrogram(dend, h = 50, use_labels_not_values = FALSE) ) # 0.8 vs 0.6 sec - for 100 runs ## End(Not run)
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