identify.hclust {mva}

R Documentation

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 class '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 amde active before FUN is applied.
...	further arguments to 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. If 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, or by clicking outside the graphics window.

Value

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

See Also

hclust, rect.hclust

Examples

## Not run: 
library(mva)
data(USArrests)
hca <- hclust(dist(USArrests))
plot(hca)
x <- identify.hclust(hca)
x

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

x11()
dev.set(2)
plot(hci)
identify.hclust(hci, function(k) barplot(table(iris[k,5])), DEV.FUN=3)

## End(Not run)