plotcluster {fpc} | R Documentation |
Plots to distinguish given classes by ten available projection methods. Includes classical discriminant coordinates, methods to project differences in mean and covariance structure, asymmetric methods (separation of a homogeneous class from a heterogeneous one), local neighborhood-based methods and methods based on robust covariance matrices.
plotcluster(x, clvecd, clnum=1, method=ifelse(identical(range(as.integer(clvecd)), as.integer(c(0,1))),"awc","dc"), bw=FALSE, xlab=NULL, ylab=NULL, pch=NULL, col=NULL, ...)
x |
the data matrix; a numerical object which can be coerced to a matrix. |
clvecd |
vector of class numbers which can be coerced into
integers; length must equal
nrow(xd) . |
method |
one of
|
clnum |
integer. Number of the class which is attempted to plot homogeneously by "asymmetric methods", which are the methods assuming that there are only two classes, as indicated above. |
bw |
logical. If TRUE , the classes are distinguished by
symbols, and the default color is black/white.
If FALSE , the classes are distinguished by
colors, and the default symbol is pch=1 . |
xlab |
label for x-axis. If NULL , a default text is used. |
ylab |
label for y-axis. If NULL , a default text is used. |
pch |
plotting symbol, see par .
If NULL , the default is used. |
col |
plotting color, see par .
If NULL , the default is used. |
... |
additional parameters passed to plot or the
projection methods. |
Christian Hennig hennig@math.uni-hamburg.de http://www.math.uni-hamburg.de/home/hennig/
Hennig, C. (2003) Symmetric, asymmetric, and robust linear dimension reduction for classification, submitted, http://stat.ethz.ch/Research-Reports/108.html.
Seber, G. A. F. (1984). Multivariate Observations. New York: Wiley.
Fukunaga (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Boston: Academic Press.
discrcoord
, batcoord
,
mvdcoord
, adcoord
,
awcoord
, ncoord
,
ancoord
.
discrproj
is an interface to all these projection methods.
rFace
for generation of the example data used below.
set.seed(4634) face <- rFace(600,dMoNo=2,dNoEy=0) grface <- as.integer(attr(face,"grouping")) plotcluster(face,grface) plotcluster(face,grface==1)