viewClusters {clusterGeneration} | R Documentation |
Plot all clusters in a 2-D projection space.
viewClusters(y, cl, outlierLabel=0, projMethod="Eigen", xlim=NULL, ylim=NULL, xlab="1st projection direction", ylab="2nd projection direction", title="Scatter plot of 2-D Projected Clusters", font=2, font.lab=2, cex=1.2, cex.lab=1.2)
y |
Data matrix. Rows correspond to observations. Columns correspond to variables. |
cl |
Cluster membership vector. |
outlierLabel |
Label for outliers. Outliers are not involved in calculating the projection
directions. Outliers will be represented by red triangles in the plot.
By default, |
projMethod |
Method to construct 2-D projection directions.
|
xlim |
Range of X axis. |
ylim |
Range of Y axis. |
xlab |
X axis label. |
ylab |
Y axis label. |
title |
Title of the plot. |
font |
An integer which specifies which font to use for text (see |
font.lab |
The font to be used for x and y labels (see |
cex |
A numerical value giving the amount by which plotting text
and symbols should be scaled relative to the default (see |
cex.lab |
The magnification to be used for x and y labels relative
to the current setting of 'cex' (see |
B |
Between cluster distance matrix measuring the between cluster variation. |
Q |
Columns of |
proj |
Projected clusters in the 2-D space spanned by the first 2 columns of
the matrix |
Weiliang Qiu stwxq@channing.harvard.edu
Harry Joe harry@stat.ubc.ca
Dhillon I. S., Modha, D. S. and Spangler, W. S. (2002) Class visualization of high-dimensional data with applications. computational Statistics and Data Analysis, 41, 59–90.
Qiu, W.-L. and Joe, H. (2006) Separation Index and Partial Membership for Clustering. Computational Statistics and Data Analysis, 50, 585–603.
plot1DProjection
plot2DProjection
n1<-50 mu1<-c(0,0) Sigma1<-matrix(c(2,1,1,5),2,2) n2<-100 mu2<-c(10,0) Sigma2<-matrix(c(5,-1,-1,2),2,2) n3<-30 mu3<-c(10,10) Sigma3<-matrix(c(3,1.5,1.5,1),2,2) n4<-10 mu4<-c(0,0) Sigma4<-50*diag(2) library(MASS) set.seed(1234) y1<-mvrnorm(n1, mu1, Sigma1) y2<-mvrnorm(n2, mu2, Sigma2) y3<-mvrnorm(n3, mu3, Sigma3) y4<-mvrnorm(n4, mu4, Sigma4) y<-rbind(y1, y2, y3, y4) cl<-rep(c(1:3, 0), c(n1, n2, n3, n4)) par(mfrow=c(2,1)) viewClusters(y, cl) viewClusters(y, cl,projMethod="DMS")