stats (version 3.5.0)

identify.hclust: Identify Clusters in a Dendrogram

Description

identify.hclust reads the position of the graphics pointer when the (first) mouse button is pressed. It then cuts the tree at the vertical position of the pointer and highlights the cluster containing the horizontal position of the pointer. Optionally a function is applied to the index of data points contained in the cluster.

Usage

# S3 method for hclust
identify(x, FUN = NULL, N = 20, MAXCLUSTER = 20, DEV.FUN = NULL,
          …)

Arguments

x

an object of the type produced by hclust.

FUN

(optional) function to be applied to the index numbers of the data points in a cluster (see ‘Details’ below).

N

the maximum number of clusters to be identified.

MAXCLUSTER

the maximum number of clusters that can be produced by a cut (limits the effective vertical range of the pointer).

DEV.FUN

(optional) integer scalar. If specified, the corresponding graphics device is made active before FUN is applied.

…

further arguments to FUN.

Value

Either a list of data point index vectors or a list of return values of FUN.

Details

By default clusters can be identified using the mouse and an invisible list of indices of the respective data points is returned.

If FUN is not NULL, then the index vector of data points is passed to this function as first argument, see the examples below. The active graphics device for FUN can be specified using DEV.FUN.

The identification process is terminated by pressing any mouse button other than the first, see also identify.

See Also

hclust, rect.hclust

Examples

# NOT RUN {
require(graphics)

hca <- hclust(dist(USArrests))
plot(hca)
(x <- identify(hca)) ##  Terminate with 2nd mouse button !!

hci <- hclust(dist(iris[,1:4]))
plot(hci)
identify(hci, function(k) print(table(iris[k,5])))

# open a new device (one for dendrogram, one for bars):
dev.new() # << make that narrow (& small)
          # and *beside* 1st one
nD <- dev.cur()            # to be for the barplot
dev.set(dev.prev())  # old one for dendrogram
plot(hci)
## select subtrees in dendrogram and "see" the species distribution:
identify(hci, function(k) barplot(table(iris[k,5]), col = 2:4), DEV.FUN = nD)
# }