#' @param fast TRUE to use compiled C code, FALSE for R code only
#' @param verbose TRUE to show some execution traces
#'
-#' @return a list with several models, defined by phi, rho, pi, llh
+#' @return a list with several models, defined by phi (the regression parameter reparametrized),
+#' rho (the covariance parameter reparametrized), pi (the proportion parameter is the mixture model), llh
+#' (the value of the loglikelihood function for this estimator on the training dataset). The list is given
+#' for several levels of sparsity, given by several regularization parameters computed automatically.
#'
#' @export
constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
- maxi, gamma, X, Y, eps, S, ncores = 3, fast, verbose)
+ maxi, gamma, X, Y, eps, S, ncores, fast, verbose)
{
if (ncores > 1)
{
return(NULL)
# lambda == 0 because we compute the EMV: no penalization here
- 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)
+ 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)
# Eval dimension from the result + selected
phiLambda2 <- res$phi
# For each lambda, computation of the parameters
out <-
if (ncores > 1) {
- parLapply(cl, 1:length(S), computeAtLambda)
+ parallel::parLapply(cl, 1:length(S), computeAtLambda)
} else {
lapply(1:length(S), computeAtLambda)
}