| get_subdendrograms {dendextend} | R Documentation |
Extracts a list of subdendrogram structures based on the cutree cutree.dendrogram function
from a given dendrogram object. It can be useful in case of more exact visual
investigation of clustering results.
get_subdendrograms(dend, k, ...)
dend |
a dendrogram object |
k |
the number of subdendrograms that should be extracted |
... |
parameters that should be passed to the cutree
|
A list of k subdendrograms, based on the cutree
cutree.dendrogram clustering
clusters.
# needed packages:
# install.packages(gplots)
# install.packages(viridis)
# install.packages(devtools)
# devtools::install_github('talgalili/dendextend') #' dendextend from github
# define dendrogram object to play with:
dend <- iris[,-5] %>% dist %>% hclust %>% as.dendrogram %>%
set("labels_to_character") %>% color_branches(k=5)
dend_list <- get_subdendrograms(dend, 5)
# Plotting the result
par(mfrow = c(2,3))
plot(dend, main = "Original dendrogram")
sapply(dend_list, plot)
# plot a heatmap of only one of the sub dendrograms
par(mfrow = c(1,1))
library(gplots)
sub_dend <- dend_list[[1]] #' get the sub dendrogram
# make sure of the size of the dend
nleaves(sub_dend)
length(order.dendrogram(sub_dend))
# get the subset of the data
subset_iris <- as.matrix(iris[order.dendrogram(sub_dend),-5])
# update the dendrogram's internal order so to not cause an error in heatmap.2
order.dendrogram(sub_dend) <- rank(order.dendrogram(sub_dend))
heatmap.2(subset_iris, Rowv = sub_dend, trace = "none", col = viridis::viridis(100))