R/pdisMC.R

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

pdisMC <-
function(m,MM=FALSE,cop=3,dop=1,center=NA){
#
# Compute projection distances for points in m
#
#
#
#  MM=F  Projected distance scaled
#  using interquatile range.
#  MM=T  Scale projected distances using MAD.
#
#  There are five options for computing the center of the
#  cloud of points when computing projections:
#  cop=1 uses Donoho-Gasko median
#  cop=2 uses MCD center
#  cop=3 uses median of the marginal distributions.
#  cop=4 uses MVE center
#  cop=5 uses skipped mean
#
library(parallel)
m<-elimna(m) # Remove missing values
m<-as.matrix(m)
if(ncol(m)==1){
if(is.na(center[1]))center<-median(m)
dis<-abs(m[,1]-center)
if(!MM){
temp<-idealf(dis)
pdis<-dis/(temp$qu-temp$ql)
}
if(MM)pdis<-dis/mad(dis)
}
if(ncol(m)>1){
if(is.na(center[1])){
if(cop==1)center<-dmean(m,tr=.5,dop=dop)
if(cop==2)center<-cov.mcd(m,print=FALSE)$center
if(cop==3)center<-apply(m,2,median)
if(cop==4)center<-cov.mve(m,print=FALSE)$center
if(cop==5)center<-smean(m)
}
cenmat=matrix(rep(center,nrow(m)),ncol=ncol(m),byrow=TRUE)
Amat=m-cenmat
B=listm(t(Amat))  # so rows are now in B[[1]]...B[[n]]
dis=mclapply(B,outproMC.sub,Amat,mc.preschedule=TRUE)
if(!MM){
dmat<-mclapply(dis,IQRstand,mc.preschedule=TRUE)
}
if(MM)dmat<-mclapply(dis,MADstand,mc.preschedule=TRUE)
pdis<-apply(matl(dmat),1,max,na.rm=TRUE)
}
pdis
}