mvdcoord {fpc} | R Documentation |
Discriminant projections as defined in Young, Marco and Odell (1987). The principle is to maximize the projection of a matrix consisting of the differences between the means of all classes and the first mean and the differences between the covariance matrices of all classes and the forst covariance matrix.
mvdcoord(xd, clvecd, clnum=1, sphere="mcd", ...)
xd |
the data matrix; a numerical object which can be coerced to a matrix. |
clvecd |
integer vector of class numbers; length must equal
nrow(xd) . |
clnum |
integer. Number of the class to which all differences are computed. |
sphere |
a covariance matrix or one of
"mve", "mcd", "classical", "none". The matrix used for sphering the
data. "mcd" and "mve" are robust covariance matrices as implemented
in cov.rob . "classical" refers to the classical
covariance matrix. "none" means no sphering and use of the raw
data. |
... |
no effect |
List with the following components
ev |
eigenvalues in descending order. |
units |
columns are coordinates of projection basis vectors.
New points x can be projected onto the projection basis vectors
by x %*% units |
proj |
projections of xd onto units . |
Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/
Young, D. M., Marco, V. R. and Odell, P. L. (1987). Quadratic discrimination: some results on optimal low-dimensional representation, Journal of Statistical Planning and Inference, 17, 307-319.
plotcluster
for straight forward discriminant plots.
discrproj
for alternatives.
rFace
for generation of the example data used below.
set.seed(4634) face <- rFace(300,dMoNo=2,dNoEy=0,p=3) grface <- as.integer(attr(face,"grouping")) mcf <- mvdcoord(face,grface) plot(mcf$proj,col=grface) # ...done in one step by function plotcluster.