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. One-dimensional data is plotted against the cluster number.
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.
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. |
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/
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.
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)