Creates plots for visualizing an
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an object of class
logical; if true and
integer vector or NULL (default), the latter
producing both plots. Otherwise,
main and sub title for the plot, with convenient
defaults. See documentation for these arguments in
for label adjustment in
integer indicating the number of labels which is considered too large for single-name labelling the banner plot.
positive integer giving the length to which strings are truncated in banner plot labeling.
logical or integer indicating if
graphical parameters (see
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
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
pltree(), i.e., its method
corresponding arguments, e.g.,
Appropriate plots are produced on the current graphics device. This can
be one or both of the following choices:
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
max.strlen (5) characters.
For the dendrogram, more flexibility than via
dg <- as.dendrogram(x) and
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.
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