R/Rpca.R

In musto101/wilcox_R: What the package does (short line)

Rpca <-
function(x,p=ncol(x)-1,locfun=llocv2,loc.val=NULL,iter=100,SCORES=FALSE,
gvar.fun=cov.mba,SEED=TRUE,...){
#
# Robust PCA using random orthogonal matrices and
# robust generalized variance method
#
#  locfun, by default, use the marginal medians
#  alternatives are mcd, tauloc, spat,...
#
if(SEED)set.seed(2)
x<-elimna(x)
n<-nrow(x)
m<-ncol(x)
if(is.null(loc.val))info<-locfun(x,...)$center
if(!is.null(loc.val))info<-loc.val
for(i in 1:n)x[i,]<-x[i,]-info
vals<-NA
z<-matrix(nrow=n,ncol=p)
bval<-array(NA,c(p,m,iter))
for(it in 1:iter){
B<-matrix(runif(p*m),nrow=p,ncol=m)
B <- t(ortho(t(B))) # so rows are orthogonal
bval[,,it]<-B
for(i in 1:n)z[i,]<-B%*%as.matrix(x[i,])
#vals[it]<-gvar(z)
vals[it]<-gvarg(z,var.fun=gvar.fun)
}
iord<-order(vals)
Bop<-0-bval[,,iord[iter]]
zval<-NULL
if(SCORES){
for(i in 1:n)z[i,]<-Bop%*%as.matrix(x[i,])
zval<-z
}
list(B=Bop,gen.var=vals[iord[iter]],scores=zval)
}