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, …)
```

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.

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

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`

.

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.

`agnes`

and `agnes.object`

;
`bannerplot`

, `pltree.twins`

,
and `par`

.

```
# NOT RUN {
## 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
# }
```

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