Nothing
estploidy <-
function(alphas=NA,het=NA,depth=NA,train=FALSE,pl=NA,set=NA,nclasses=2,ids=NA,pcs=1:2){
## create data structure
## regress het. on depth
outH<-lm(het ~ depth)
H<-outH$residuals
Nind<-length(alphas)
Nprops<-dim(alphas[[1]])[1]
## extrac point estimates for allelic proportions
ad<-matrix(NA,nrow=Nind,ncol=Nprops)
for(i in 1:Nind){
ad[i,]<-alphas[[i]][,3]
}
datamatrix<-cbind(H,ad)
## pca
pcout<-prcomp(datamatrix,center=TRUE,scale=TRUE)
## kmeans and LDA without training set
if(train==FALSE){
## kmeans clustering, using pcs
kout<-kmeans(x=pcout$x[,pcs],centers=nclasses)
## DA for classification
lout<-lda(kout$cluster ~ pcout$x[,pcs],CV=TRUE,prior=rep(1/nclasses,nclasses))
pp<-cbind(ids,lout$posterior)
}
## LDA with training set
if(train==TRUE){
df<-data.frame(pl=pl,pcout$x[,pcs])
ltrn<-lda(pl ~ ., df,subset=set,prior=rep(1/nclasses,nclasses))
lout<-predict(object=ltrn,newdata=df[-set,])
pp<-lout$posterior
}
out<-list(pp=pp,pcwghts=pcout$rotation,pcscrs=pcout$x)
out
}
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