R: Plots of an Agglomerative Hierarchical Clustering

plot.agnes {cluster} | R Documentation

## 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 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.

### 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`.

### Side Effects

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

### 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.

### See Also

`agnes` and `agnes.object`; `bannerplot`, `pltree.twins`, 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

[Package cluster version 2.0.9 Index]