cluster (version 1.9.2)

plot.agnes: Plots of an Agglomerative Hierarchical Clustering

Description

Creates plots for visualizing an agnes object.

Usage

## S3 method for class 'agnes':
plot(x, ask = FALSE, which.plots = NULL, main = NULL,
           sub = paste("Agglomerative Coefficient = ",round(x$ac, digits = 2)),
           adj = 0, nmax.lab = 35, max.strlen = 5, xax.pretty = TRUE, ...)

Arguments

x

an object of class "agnes", typically created by agnes(.).

ask

logical; if true and which.plots is NULL, plot.agnes 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 banner plot or 2 for a dendrogramm or ``clustering tree''.

main, sub

main and sub title for the plot, with convenient defaults. See documentation for these arguments in plot.default.

adj

for label adjustment in bannerplot().

nmax.lab

integer indicating the number of labels which is considered too large for single-name labelling the banner plot.

max.strlen

positive integer giving the length to which strings are truncated in banner plot labeling.

xax.pretty

logical or integer indicating if pretty(*, n = xax.pretty) should be used for the x axis. xax.pretty = FALSE is for back compatibility.

...

graphical parameters (see par) may also be supplied and are passed to bannerplot() or pltree(), respec

Side Effects

Appropriate plots are produced on the current graphics device. This can be one or both of the following choices: Banner Clustering tree

Details

When ask = TRUE, rather than producing each plot sequentially, plot.agnes 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 one must set par(ask= TRUE) before invoking the plot command.

The banner displays the hierarchy of clusters, and is equivalent to a tree. See Rousseeuw (1986) or chapter 5 of Kaufman and Rousseeuw (1990). The banner plots distances at which observations and clusters are merged. The observations are listed in the order found by the agnes algorithm, and the numbers in the height vector are represented as bars between the observations.

The leaves of the clustering tree are the original observations. Two branches come together at the distance between the two clusters being merged.

For more customization of the plots, rather call bannerplot and pltree directly with corresponding arguments, e.g., xlab or ylab.

References

Kaufman, L. and Rousseeuw, P.J. (1990) Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.

Rousseeuw, P.J. (1986). A visual display for hierarchical classification, in Data Analysis and Informatics 4; edited by E. Diday, Y. Escoufier, L. Lebart, J. Pages, Y. Schektman, and R. Tomassone. North-Holland, Amsterdam, 743--748.

Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997) Integrating Robust Clustering Techniques in S-PLUS, Computational Statistics and Data Analysis, 26, 17-37.

See Also

agnes and agnes.object; bannerplot, pltree, and par.

Examples

## Can also pass `labels' to pltree() and bannerplot():
data(iris)
cS <- as.character(Sp <- iris$Species)
cS[Sp == "setosa"] <- "S"
cS[Sp == "versicolor"] <- "V"
cS[Sp == "virginica"] <- "g"
ai <- agnes(iris[, 1:4])
plot(ai, labels = cS, nmax = 150)# bannerplot labels are mess