p = dim(phiInit)[1]
m = dim(phiInit)[2]
k = dim(phiInit)[3]
-
sel.lambda = S[[lambda]]$selected
# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-
if (length(col.sel) == 0)
return (NULL)
piLambda = res$pi
phiLambda = array(0, dim = c(p,m,k))
for (j in seq_along(col.sel))
- phiLambda[col.sel[j],,] = phiLambda2[j,,]
+ phiLambda[col.sel[j],sel.lambda[[j]],] = phiLambda2[j,sel.lambda[[j]],]
dimension = length(unlist(sel.lambda))
# Computation of the loglikelihood
densite = vector("double",n)
for (r in 1:k)
{
- delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))
+ if (length(col.sel)==1){
+ delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%t(phiLambda[col.sel,,r])))
+ } else delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))
densite = densite + piLambda[r] *
det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-diag(tcrossprod(delta))/2.0)
}