cluster (version 1.14.4)

clusplot.default: Bivariate Cluster Plot (clusplot) Default Method

Description

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.

Usage

## S3 method for class 'default':
clusplot(x, clus, diss = FALSE,
          s.x.2d = mkCheckX(x, diss), 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, cex = 1, cex.txt = cex,
          span = TRUE,
          add = FALSE,
          xlim = NULL, ylim = NULL,
          main = paste("CLUSPLOT(", deparse(substitute(x)),")"),
          sub = paste("These two components explain",
             round(100 * var.dec, digits = 2), "% of the point variability."),
          xlab = "Component 1", ylab = "Component 2",
          verbose = getOption("verbose"),
          ...)

Arguments

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

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

diss

logical indicating if x will be considered as a dissimilarity matrix or a matrix of observations by variables (see x arugment above).

s.x.2d

a list with components x (a $n \times 2$ matrix; typically something like principal components of original data), labs and var.dec.

stand

logical flag: if true, then the representations of the n observations in the 2-dimensional 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 an

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 shoul

labels

integer code, currently one of 0,1,2,3,4 and 5. If [object Object],[object Object],[object Object],[object Object],[object Object],[object Object] The levels of the vector clus are taken as labels for the clusters. The labels

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

add

logical indicating if ellipses (and labels if labels is true) should be added to an already existing plot. If false, neither a title or sub title, see sub, is w

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).

cex, cex.txt

character expansion (size), for the point symbols and point labels, respectively.

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.

sub

sub title for the plot; by default, one is constructed.

xlab, ylab

x- and y- axis labels for the plot, with defaults.

verbose

a logical indicating, if there should be extra diagnostic output; mainly for debugging.

...

Further graphical parameters may also be supplied, see par.

Value

• 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 .

Side Effects

a visual display of the clustering is plotted on the current graphics device.

Details

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.

References

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://www.agoras.ua.ac.be/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 S-PLUS, Computational Statistics and Data Analysis, 26, 17-37.

See Also

princomp, cmdscale, pam, clara, daisy, par, identify, cov.mve, clusplot.partition.

Examples

## plotting votes.diss(dissimilarity) in a bivariate plot and
## partitioning into 2 clusters
data(votes.repub)
votes.diss <- daisy(votes.repub)
pamv <- pam(votes.diss, 2, diss = TRUE)
clusplot(pamv, shade = TRUE)
## is the same as
votes.clus <- pamv$clustering
clusplot(votes.diss, votes.clus, diss = TRUE, shade = TRUE)

clusplot(pamv, col.p = votes.clus, labels = 4)# color points and label ellipses
# "simple" cheap ellipses: larger than minimum volume:
# here they are *added* to the previous plot:
clusplot(pamv, span = FALSE, add = TRUE, col.clus = "midnightblue")

## a work-around for setting a small label size:
clusplot(votes.diss, votes.clus, diss = TRUE)
op <- par(new=TRUE, cex = 0.6)
clusplot(votes.diss, votes.clus, diss = TRUE,
         axes=FALSE,ann=FALSE, sub="", col.p=NA, col.txt="dark green", labels=3)
par(op)
## MM: This should now be as simple as
clusplot(votes.diss, votes.clus, diss = TRUE, labels = 3, cex.txt = 0.6)


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 2-dimensional 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)