Nothing
rm(list=ls())
library(BLR)
data(wheat) #Loads the wheat dataset
nIter<-1500 #For real data sets more samples are needed
burnIn<-500
thin<-10
folds<-10
y<-Y[,1]
priorBL<-list(
varE=list(df=3,S=2.5),
varU=list(df=3,S=0.63),
lambda = list(shape=0.52,rate=1e-5,value=20,type='random')
)
set.seed(123) #Set seed for the random number generator
sets<-rep(1:10,60)[-1]
sets<-sets[order(runif(nrow(A)))]
COR.CV<-rep(NA,times=(folds+1))
names(COR.CV)<-c(paste('fold=',1:folds,sep=''),'Pooled')
w<-rep(1/nrow(A),folds) ## weights for pooled correlations and MSE
yHatCV<-numeric()
for(fold in 1:folds)
{
yNa<-y
whichNa<-which(sets==fold)
yNa[whichNa]<-NA
prefix<-paste('PM_BL','_fold_',fold,'_',sep='')
fm<-BLR(y=yNa,XL=X,GF=list(ID=(1:nrow(A)),A=A),prior=priorBL,
nIter=nIter,burnIn=burnIn,thin=thin)
yHatCV[whichNa]<-fm$yHat[fm$whichNa]
w[fold]<-w[fold]*length(fm$whichNa)
COR.CV[fold]<-cor(fm$yHat[fm$whichNa],y[whichNa])
}
COR.CV[11]<-mean(COR.CV[1:10])
COR.CV
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