identify.hclust            package:stats            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 made 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.
      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'.

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: 
     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):
     get(getOption("device"))() # << 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)
     ## End(Not run)

