R: Plot of a Partition of the Data Set

plot.partition {cluster}

R Documentation

Plot of a Partition of the Data Set

Description

Creates plots for visualizing a partition object.

Usage

## S3 method for class 'partition'
plot(x, ask = FALSE, which.plots = NULL,
     nmax.lab = 40, max.strlen = 5, data = x$data, dist = NULL,
     stand = FALSE, lines = 2,
     shade = FALSE, color = FALSE, labels = 0, plotchar = TRUE,
     span = TRUE, xlim = NULL, ylim = NULL, main = NULL, ...)

Arguments

`x`	an object of class `"partition"`, typically created by the functions `pam`, `clara`, or `fanny`.
`ask`	logical; if true and `which.plots` is `NULL`, `plot.partition` operates in interactive mode, via `menu`.
`which.plots`	integer vector or NULL (default), the latter producing both plots. Otherwise, `which.plots` must contain integers of `1` for a clusplot or `2` for silhouette.
`nmax.lab`	integer indicating the number of labels which is considered too large for single-name labeling the silhouette plot.
`max.strlen`	positive integer giving the length to which strings are truncated in silhouette plot labeling.
`data`	numeric matrix with the scaled data; per default taken from the partition object `x`, but can be specified explicitly.
`dist`	when `x` does not have a `diss` component as for `pam(*, keep.diss=FALSE)`, `dist` must be the dissimilarity if a clusplot is desired.
`stand,lines,shade,color,labels,plotchar,span,xlim,ylim,main, ...`	All optional arguments available for the `clusplot.default` function (except for the `diss` one) and graphical parameters (see `par`) may also be supplied as arguments to this function.

Details

When ask= TRUE, rather than producing each plot sequentially, plot.partition displays a menu listing all the plots that can be produced. If the menu is not desired but a pause between plots is still wanted, call par(ask= TRUE) before invoking the plot command.

The clusplot of a cluster partition consists of a two-dimensional representation of the observations, in which the clusters are indicated by ellipses (see clusplot.partition for more details).

The silhouette plot of a nonhierarchical clustering is fully described in Rousseeuw (1987) and in chapter 2 of Kaufman and Rousseeuw (1990). For each observation i, a bar is drawn, representing its silhouette width s(i), see silhouette for details. Observations are grouped per cluster, starting with cluster 1 at the top. Observations with a large s(i) (almost 1) are very well clustered, a small s(i) (around 0) means that the observation lies between two clusters, and observations with a negative s(i) are probably placed in the wrong cluster.

A clustering can be performed for several values of k (the number of clusters). Finally, choose the value of k with the largest overall average silhouette width.

Side Effects

An appropriate plot is produced on the current graphics device. This can be one or both of the following choices:
Clusplot
Silhouette plot

Note

In the silhouette plot, observation labels are only printed when the number of observations is less than nmax.lab (40, by default), for readability. Moreover, observation labels are truncated to maximally max.strlen (5) characters.
For more flexibility, use plot(silhouette(x), ...), see plot.silhouette.

References

Rousseeuw, P.J. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53–65.

Further, the references in plot.agnes.

Examples

## generate 25 objects, divided into 2 clusters.
x <- rbind(cbind(rnorm(10,0,0.5), rnorm(10,0,0.5)),
           cbind(rnorm(15,5,0.5), rnorm(15,5,0.5)))
plot(pam(x, 2))

## Save space not keeping data in clus.object, and still clusplot() it:
data(xclara)
cx <- clara(xclara, 3, keep.data = FALSE)
cx$data # is NULL
plot(cx, data = xclara)

[Package cluster version 2.0.6 Index]