getInfoBayesCNVassoc<-function(x, N.groups, N.cnvs, names.groups, names.CNVs, quantiles)
{
nn<-colnames(x[[1]])
# intercepts
o<-grep("alpha",nn)
alpha<-x[,o]
tt<-summary(alpha)
alpha.stats<-cbind(tt[[1]][,1],tt[[2]][,c(1,3,5)])
rownames(alpha.stats)<-names.groups
# coefficient of shared component
o<-grep("beta",nn)
beta<-x[,o]
tt<-summary(beta)
beta.stats<-cbind(tt[[1]][,1],tt[[2]][,c(1,3,5)])
rownames(beta.stats)<-names.groups
if (TRUE)
{
# predicted
o<-grep("predicted",nn)
tt<-x[,o]
predicted<-apply(tt[[1]],2,mean)
}
if (FALSE)
{
# residuos
o<-grep("residuos",nn)
tt<-x[,o]
residuos<-apply(tt[[1]],2,mean)
}
# sigma
o<-grep("sigma",nn)
tt<-x[,o]
sigma<-apply(tt[[1]],2,mean)
# specific component
index<-N.cnvs*N.groups
aux.ini<-seq(1,index,N.cnvs)
aux.end<-seq(0,index,N.cnvs)[-1]
o<-grep("^v",nn)
v<-x[,o]
tt<-summary(v, quantiles=quantiles)
v.mean<-matrix(tt[[1]][,1],N.cnvs,N.groups)
v.median<-v.median.ind<-list()
for (i in 1:N.groups)
{
v.median[[i]]<-tt[[2]][aux.ini[i]:aux.end[i],c(1,3,5)]
v.median.ind[[i]]<-cbind(v.median[[i]],sig=ifelse(v.median[[i]][,1]>0,1,ifelse(v.median[[i]][,3]<0,-1,0)))
}
# posterior probability of being <> 0
o<-grep("^p.v",nn)
temp<-x[,o]
pv<-pv.stats<-pv.mean<-v.median.select<-v.median.ind.2<-list()
for (i in 1:N.groups)
{
pv[[i]]<-temp[,aux.ini[i]:aux.end[i]]
pv.mean[[i]]<-apply(pv[[i]][[1]],2,mean)
v.median.ind.2[[i]]<-cbind(v.median[[i]],"P>1"=pv.mean[[i]])
colnames(v.median.ind.2[[i]])<-c("2.5%","Median","97.5%","P>0")
rownames(v.median.ind.2[[i]])<-names.CNVs
}
ans<-list(alpha.stats=alpha.stats, beta.stats=beta.stats, v.median.ind=v.median.ind, pv.mean=pv.mean, v.median.ind.2=v.median.ind.2, predicted=predicted, sigma=sigma)
# ans<-list(alpha.stats=alpha.stats, beta.stats=beta.stats, v.median.ind=v.median.ind, pv.mean=pv.mean, v.median.ind.2=v.median.ind.2, sigma=sigma, residuos=residuos)
ans
}
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