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# predict scores with bayes mcmcpack
predict.scores.bayes=function(Y,discretspace,map,
formula_bayes, burnin = 1000,
mcmc = 2000,verbose=0,seed = NA, beta.start = NA,
b0 = 0, B0 = 0, c0 = 0.001, d0 = 0.001, sigma.mu = NA,
sigma.var = NA){
## map is a data.frame with F1 and F2 obtained after DR on the explained data Y
notespr= nbconsos=matrix(0,nrow(discretspace),ncol(discretspace))
regs=vector("list",ncol(Y))
# preference=array(0,dim=c(nrow(discretspace),ncol(discretspace),ncol(X)))
pred.conso=preference=matrix(0,nrow(discretspace),ncol(Y))
nb.NA=vector("list",ncol(Y))
pos.NA=vector("list",ncol(Y))
## First we preform all regressions
nbconsos=c()
for(j in 1:ncol(Y)){
print(j)
dt=cbind.data.frame(Y[,j],map)
colnames(dt)[1]="Conso"
modele=as.formula(paste("Conso",formula_bayes))
dt.list=list(Conso=dt$Conso,F1=dt$F1,F2=dt$F2)
regs[[j]]=MCMCregress(modele, data=dt.list, burnin = burnin,
mcmc = mcmc,verbose=verbose,seed = seed, beta.start = beta.start,
b0 = b0, B0 = B0, c0 = c0, d0 = d0, sigma.mu = sigma.mu,
sigma.var = sigma.var )
discretspace2=cbind.data.frame(rep(1,nrow(discretspace)),discretspace)
colnames(discretspace2)[1]="y"
formula2=paste("y",formula_bayes,sep="")
m=lm(formula2,data=discretspace2)
mod.mat=model.matrix(m)
p=ncol(mod.mat)
pred1=regs[[j]][,-(p+1)]%*%t(mod.mat)
x=pred1
pred1a=x
pred.conso[,j]=colMeans(pred1a,na.rm=T)
zz=(pred1a> mean(dt$Conso))
preference[,j]=colMeans(zz,na.rm=T)
}
return(list(regression=regs,pred.conso=pred.conso,preference=preference))
}
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