Plots of an Agglomerative Hierarchical Clustering

Creates plots for visualizing an agnes object.

# S3 method for 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 dendrogram 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()` (see `pltree.twins`), respectively.

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(), i.e., its method pltree.twins, respectively.

directly with corresponding arguments, e.g., xlab or ylab.

Note

In the banner plot, observation labels are only printed when the number of observations is limited less than nmax.lab (35, by default), for readability. Moreover, observation labels are truncated to maximally max.strlen (5) characters.

For the dendrogram, more flexibility than via pltree() is provided by dg <- as.dendrogram(x) and plotting dg via plot.dendrogram.

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.

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