R/Ppca.summary.R

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

Ppca.summary <-
function(x,MC=FALSE,SCALE=TRUE,p=NULL){
#
#   x is assumed to be a matrix with p columns
#   Using robust principal components (Ppca)
#   compute generalized variance for each dimension reduction
#   from 1 to p.
#
#   report values plus proportion relative to largest value found
#
x=as.matrix(x)
if(!is.matrix(x))stop("x should be a matrix")
x=elimna(x)
gv=NA
if(is.null(p))p=ncol(x)
if(!MC)for(j in 1:p)gv[j]=Ppca(x,p=j,pr=FALSE,SCALE=SCALE)$gen.sd
if(MC){
library(parallel)
y=list()
for(j in 1:p)y[[j]]=j
gv=mclapply(y,Ppca.sum.sub,x,SCALE=SCALE,mc.preschedule=TRUE)
gv=as.vector(matl(gv))
}
res=matrix(NA,nrow=3,ncol=p)
res[1,]=c(1:p)
res[2,]=gv
res[3,]=gv/max(gv)
dimnames(res)=list(c("Num. of Comp.","Gen.Stand.Dev","Relative Size"),NULL)
list(summary=res)
}