Mean/variance differences discriminant coordinates
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
|
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 |
... |
no effect |
List with the following components
ev |
eigenvalues in descending order. |
units |
columns are coordinates of projection basis vectors.
New points |
proj |
projections of |
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.
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