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"),
...
)a dendrogram object
numeric scalar (NOT a vector) with a height where the dend should be cut.
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.
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.).
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
hc <- hclust(dist(USArrests[c(1, 6, 13, 20, 23), ]), "ave")
dend <- as.dendrogram(hc)
cutree(hc, h = 50) # on hclust
#> Alabama Colorado Illinois Maryland Minnesota
#> 1 1 1 2 3
cutree_1h.dendrogram(dend, h = 50) # on a dendrogram
#> Alabama Colorado Illinois Maryland Minnesota
#> 1 1 1 2 3
labels(dend)
#> [1] "Minnesota" "Maryland" "Colorado" "Alabama" "Illinois"
# the default (ordered by original data's order)
cutree_1h.dendrogram(dend, h = 50, order_clusters_as_data = TRUE)
#> Alabama Colorado Illinois Maryland Minnesota
#> 1 1 1 2 3
# A different order of labels - order by their order in the tree
cutree_1h.dendrogram(dend, h = 50, order_clusters_as_data = FALSE)
#> Minnesota Maryland Colorado Alabama Illinois
#> 3 2 1 1 1
# make it faster
if (FALSE) { # \dontrun{
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
} # }