clusplot.default {cluster}  R Documentation 
Creates a bivariate plot visualizing a partition (clustering) of the data. All observation are represented by points in the plot, using principal components or multidimensional scaling. Around each cluster an ellipse is drawn.
## Default S3 method: clusplot(x, clus, diss = FALSE, cor = TRUE, stand = FALSE, lines = 2, shade = FALSE, color = FALSE, labels= 0, plotchar = TRUE, col.p = "dark green", col.txt = col.p, col.clus = if(color) c(2, 4, 6, 3) else 5, span = TRUE, xlim = NULL, ylim = NULL, main = paste("CLUSPLOT(", deparse(substitute(x)),")"), verbose = getOption("verbose"), ...)
x 
matrix or data frame, or dissimilarity matrix, depending on
the value of the diss argument.
In case of a matrix (alike), each row corresponds to an observation, and each column corresponds to a variable. All variables must be numeric. Missing values ( NA s) are allowed. They are
replaced by the median of the corresponding variable. When some
variables or some observations contain only missing values, the
function stops with a warning message.
In case of a dissimilarity matrix, x is the output of
daisy or dist or a symmetric matrix. Also,
a vector of length n*(n1)/2 is allowed (where n is the
number of observations), and will be interpreted in the same way as
the output of the abovementioned functions. Missing values (NAs)
are not allowed.

clus 
a vector of length n representing a clustering of x . For
each observation the vector lists the number or name of the cluster
to which it has been assigned. clus is often the clustering
component of the output of pam , fanny or
clara . 
diss 
logical indicating if x will be considered as a dissimilarity
matrix or a matrix of observations by variables (see x
arugment above). 
cor 
logical flag, only used when working with a data matrix (diss
= FALSE ). If TRUE, then the variables are scaled to unit variance. 
stand 
logical flag: if true, then the representations of the n observations in the 2dimensional plot are standardized. 
lines 
integer out of 0, 1, 2 , used to obtain an idea of the
distances between ellipses. The distance between two ellipses E1
and E2 is measured along the line connecting the centers m1
and m2 of the two ellipses.
In case E1 and E2 overlap on the line through m1 and m2, no line is drawn. Otherwise, the result depends on the value of lines : If

shade 
logical flag: if TRUE, then the ellipses are shaded in relation to their density. The density is the number of points in the cluster divided by the area of the ellipse. 
color 
logical flag: if TRUE, then the ellipses are colored with respect to their density. With increasing density, the colors are light blue, light green, red and purple. To see these colors on the graphics device, an appropriate color scheme should be selected (we recommend a white background). 
labels 
integer code, currently one of 0,1,2,3,4 and 5. If
clus are taken as labels for the
clusters. The labels
of the points are the rownames of x if x is matrix like.
Otherwise (diss = TRUE ), x is a vector, point labels
can be attached to x as a "Labels" attribute
(attr(x,"Labels") ), as is done for the output of
daisy .
A possible names attribute of clus will not
be taken into account.

plotchar 
logical flag: if TRUE, then the plotting symbols differ for points belonging to different clusters. 
span 
logical flag: if TRUE, then each cluster is represented by the ellipse with
smallest area containing all its points. (This is a special case of the
minimum volume ellipsoid.) If FALSE, the ellipse is based on the mean and covariance matrix of the same points. While this is faster to compute, it often yields a much larger ellipse. There are also some special cases: When a cluster consists of only one point, a tiny circle is drawn around it. When the points of a cluster fall on a straight line, span=FALSE draws a narrow
ellipse around it and span=TRUE gives the exact line segment.

col.p 
color code(s) used for the observation points. 
col.txt 
color code(s) used for the labels (if labels >= 2 ). 
col.clus 
color code for the ellipses (and their labels); only one if color is false (as per default). 
xlim, ylim 
numeric vectors of length 2, giving the x and y
ranges as in plot.default . 
main 
main title for the plot; by default, one is constructed. 
verbose 
a logical indicating, if there should be extra diagnostic output; mainly for `debugging'. 
... 
Further graphical parameters may also be supplied, see
par . 
clusplot
uses the functions princomp
and
cmdscale
. These functions are
data reduction techniques. They will represent the data in a bivariate plot.
Ellipses are then drawn to indicate the clusters. The further layout of the
plot is determined by the optional arguments.
An invisible list with components:
Distances 
When lines is 1 or 2 we optain a k by k matrix (k is the number of
clusters). The element in [i,j] is the distance between ellipse
i and ellipse j.If lines = 0 , then the value of this component is NA .

Shading 
A vector of length k (where k is the number of clusters), containing the
amount of shading per cluster. Let y be a vector where element i is the
ratio between the number of points in cluster i and the area of ellipse i.
When the cluster i is a line segment, y[i] and the density of the cluster are
set to NA . Let z be the sum of all the elements of y without the NAs.
Then we put shading = y/z *37 + 3 .

a visual display of the clustering is plotted on the current graphics device.
When we have 4 or fewer clusters, then the color=TRUE
gives
every cluster a different color. When there are more than 4 clusters,
clusplot uses the function pam
to cluster the
densities into 4 groups such that ellipses with nearly the same
density get the same color. col.clus
specifies the colors used.
The col.p
and col.txt
arguments, added for R,
are recycled to have length the number of observations.
If col.p
has more than one value, using color = TRUE
can
be confusing because of a mix of point and ellipse colors.
Pison, G., Struyf, A. and Rousseeuw, P.J. (1999)
Displaying a Clustering with CLUSPLOT,
Computational Statistics and Data Analysis, 30, 381–392.
A version of this is available as technical report from
http://winwww.uia.ac.be/u/statis/abstract/Disclu99.htm
Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997). Integrating Robust Clustering Techniques in SPLUS, Computational Statistics and Data Analysis, 26, 1737.
princomp
, cmdscale
, pam
,
clara
, daisy
, par
,
identify
, cov.mve
,
clusplot.partition
.
## plotting votes.diss(dissimilarity) in a bivariate plot and ## partitioning into 2 clusters data(votes.repub) votes.diss < daisy(votes.repub) votes.clus < pam(votes.diss, 2, diss = TRUE)$clustering clusplot(votes.diss, votes.clus, diss = TRUE, shade = TRUE) clusplot(votes.diss, votes.clus, diss = TRUE, col.p = votes.clus, labels = 4)# color points and label ellipses clusplot(votes.diss, votes.clus, diss = TRUE, span = FALSE)# simple ellipses if(interactive()) { # uses identify() *interactively* : clusplot(votes.diss, votes.clus, diss = TRUE, shade = TRUE, labels = 1) clusplot(votes.diss, votes.clus, diss = TRUE, labels = 5)# ident. only points } ## plotting iris (data frame) in a 2dimensional plot and partitioning ## into 3 clusters. data(iris) iris.x < iris[, 1:4] cl3 < pam(iris.x, 3)$clustering op < par(mfrow= c(2,2)) clusplot(iris.x, cl3, color = TRUE) U < par("usr") ## zoom in : rect(0,1, 2,1, border = "orange", lwd=2) clusplot(iris.x, cl3, color = TRUE, xlim = c(0,2), ylim = c(1,1)) box(col="orange",lwd=2); mtext("sub region", font = 4, cex = 2) ## or zoom out : clusplot(iris.x, cl3, color = TRUE, xlim = c(4,4), ylim = c(4,4)) mtext("`super' region", font = 4, cex = 2) rect(U[1],U[3], U[2],U[4], lwd=2, lty = 3) # reset graphics par(op)