X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=692fbe190121eb99399b839376ed2c8e006d535c;hp=2d04adbe74ca4255fbb5ea588615eb570bfd9aa9;hb=6af1d4897dbab92a7be05068e0e15823378965d9;hpb=f32535f2bc8d50470aa87204bbd7971805dbc9ef

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 2d04adb..692fbe1 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -17,11 +17,14 @@
 #' @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)
   {
@@ -51,8 +54,9 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       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
@@ -101,7 +105,7 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
   # 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)
     }