| identify.hclust {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 |
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 |
... |
further arguments to |
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
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)
## End(Not run)