fpc (version 1.2-2)

plotcluster: Discriminant projection plot.

Description

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. One-dimensional data is plotted against the cluster number.

Usage

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, ...)

Arguments

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 [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object] Note that "bc", "vbc", "adc", "awc", "arc" and "anc" assume that there are o

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. clnum is ignored for methods "dc" and "nc".

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.

References

Hennig, C. (2004) Asymmetric linear dimension reduction for classification. Journal of Computational and Graphical Statistics 13, 930-945 . Hennig, C. (2005) A method for visual cluster validation. In: Weihs, C. and Gaul, W. (eds.): Classification - The Ubiquitous Challenge. Springer, Heidelberg 2005, 153-160. Seber, G. A. F. (1984). Multivariate Observations. New York: Wiley.

Fukunaga (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Boston: Academic Press.

See Also

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.

Examples

set.seed(4634)
face <- rFace(600,dMoNo=2,dNoEy=0)
grface <- as.integer(attr(face,"grouping"))
plotcluster(face,grface)
plotcluster(face,grface==1)