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)
+ ## Computation of the loglikelihood
+ # Precompute det(rhoLambda[,,r]) for r in 1...k
+ detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
+ sumLogLLH <- 0
+ for (i in 1:n)
{
- 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)
+ # Update gam[,]; use log to avoid numerical problems
+ logGam <- sapply(1:k, function(r) {
+ log(piLambda[r]) + log(detRho[r]) - 0.5 *
+ sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+ })
+
+ logGam <- logGam - max(logGam) #adjust without changing proportions
+ gam[i, ] <- exp(logGam)
+ norm_fact <- sum(gam[i, ])
+ gam[i, ] <- gam[i, ] / norm_fact
+ sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
}
- llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1)
+ llhLambda <- c(sumLogLLH/n, (dimension + m + 1) * k - 1)
+ # densite <- vector("double", n)
+ # for (r in 1:k)
+ # {
+ # 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(-rowSums(delta^2)/2)
+ # }
+ # llhLambda <- c(mean(log(densite)), (dimension + m + 1) * k - 1)
list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
}