if (verbose)
print(paste("Computations for lambda=", lambda))
- n <- dim(X)[1]
- p <- dim(phiInit)[1]
- m <- dim(phiInit)[2]
- k <- dim(phiInit)[3]
+ n <- nrow(X)
+ p <- ncol(X)
+ m <- ncol(Y)
+ k <- length(piInit)
sel.lambda <- S[[lambda]]$selected
# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
col.sel <- which(sapply(sel.lambda, length) > 0) #if list of selected vars
return(NULL)
# lambda == 0 because we compute the EMV: no penalization here
- res <- EMGLLF(array(phiInit[col.sel, , ],dim=c(length(col.sel),m,k)), rhoInit,
- piInit, gamInit, mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
+ res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
+ mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
# Eval dimension from the result + selected
phiLambda2 <- res$phi
## Computation of the loglikelihood
# Precompute det(rhoLambda[,,r]) for r in 1...k
- detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
+ detRho <- sapply(1:k, function(r) gdet(rhoLambda[, , r]))
sumLogLLH <- 0
for (i in 1:n)
{
norm_fact <- sum(gam)
sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
}
- llhLambda <- c(sumLogLLH/n, (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)
}