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
phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, sel.lambda[[j]], ]
dimension <- length(unlist(sel.lambda))
- ## Computation of the loglikelihood
- # Precompute det(rhoLambda[,,r]) for r in 1...k
- detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
- sumLogLLH <- 0
+ ## Affectations
+ Gam <- matrix(0, ncol = length(piLambda), nrow = n)
for (i in 1:n)
{
- # 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 <- exp(logGam)
- norm_fact <- sum(gam)
- sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
+ for (r in 1:length(piLambda))
+ {
+ sqNorm2 <- sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+ Gam[i, r] <- piLambda[r] * exp(-0.5 * sqNorm2) * det(rhoLambda[, , r])
+ }
}
- llhLambda <- c(sumLogLLH/n, (dimension + m + 1) * k - 1)
- # densite <- vector("double", n)
- # for (r in 1:k)
+ Gam2 <- Gam/rowSums(Gam)
+ affec <- apply(Gam2, 1, which.max)
+ proba <- Gam2
+ LLH <- c(sum(log(apply(Gam,1,sum))), (dimension + m + 1) * k - 1)
+ # ## Computation of the loglikelihood
+ # # Precompute det(rhoLambda[,,r]) for r in 1...k
+ # detRho <- sapply(1:k, function(r) gdet(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(-rowSums(delta^2)/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 -> change the LLH
+ # gam <- exp(logGam)
+ # norm_fact <- sum(gam)
+ # sumLogLLH <- sumLogLLH + log(norm_fact) - m/2* log(2 * base::pi)
# }
- # llhLambda <- c(mean(log(densite)), (dimension + m + 1) * k - 1)
- list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
+ #llhLambda <- c(-sumLogLLH/n, (dimension + m + 1) * k - 1)
+ list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = LLH, affec = affec, proba = proba)
}
# For each lambda, computation of the parameters