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

    identify.hclust(HCOBJ, FUN=NULL, N=20, MAXCLUSTER=20, DEV.FUN=NULL, ...)

### Arguments

`HCOBJ` | 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`.

### Examples

    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)

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