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ASCAfun1<-function (X,Design,Fac) {
n <- ncol(X) # number of genes
I <- ncol(Design) # number of levels in the factor
NK<-NULL
XK<-matrix(NA,nrow=I,ncol=n)
for (i in 1:I) {
sub<-X[Design[,i]==1,]
if(is.null(nrow(sub))){ #when there isn't replicates
NK[i] <- 1
XK[i,] <- sub
}else{
NK[i]<-nrow(sub)
XK[i,]<-apply(sub,2,mean) }
}
NK<-sqrt(NK)
# Weigh the data of the Submodel with the corresponding number of measurement occasions
XKw<- NK*XK
PCA<-PCA.GENES(XKw)
scw<-PCA$scores[,1:Fac]
ld<-PCA$loadings[,1:Fac]
ssq<-PCA$var.exp
if(Fac==1) {
scw<-as.matrix(scw)
ld<-as.matrix(ld)
}
if(Fac==0) {
scw<-as.matrix(rep(0,I))
ld<-as.matrix(rep(0,n))
}
# Re-weigth the scores
sc<-scw/NK
XKrec<-sc%*%t(ld)
Xa<-NULL
TPa<-NULL
for (i in 1:nrow(X)){
position<-which(Design[i,]==1)
Xa<-rbind(Xa,XK[position,])
TPa<-rbind(TPa,XKrec[position,])
}
Ea<-Xa-TPa
#Leverage & SPE
leverage<-apply(ld^2,1,sum)
SPE<-apply(Ea^2,2,sum)
output<-list(XK,sc,ld,ssq,Xa,TPa,Ea,leverage,SPE)
names(output)<-c("data","scores","loadings","var.exp","X","TP","E","leverage","SPE")
output
}
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