plot.agnes {cluster} | R Documentation |
Creates plots for visualizing an agnes
object.
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, ...)
x |
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. |
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.
An appropriate plot is produced on the current graphics device. This can be one or both of the following choices: Banner Clustering tree
In the banner plot, observation labels are only printed when the number of observations is limited to less than 35, for readability.
Moreover, observation labels are truncated to at most 5 characters.
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.