plot.agnes: Plots of an Agglomerative Hierarchical Clustering

Description

Creates plots for visualizing an agnes object.

Usage

plot.agnes(x, ask = FALSE, which.plots = NULL, 
           main = paste("Banner of ", deparse(attr(x, "Call"))),
           sub = paste("Agglomerative Coefficient = ",round(x$ac, digits = 2)),
           adj = 0, nmax.lab = 35, max.strlen = 5, ...)

Arguments

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

ask

if TRUE, plot.agnes operates in interactive mode.

...

Graphical parameters (see par) may also be supplied as arguments to this function.

Side Effects

An appropriate plot is 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.

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. pp. 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.