X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=b8ea1a07b0ba39aa42b461c3ecfa92c1a51ca729;hp=eb6c5907060287b237220c7c1c59fdf4dace1003;hb=6af1d4897dbab92a7be05068e0e15823378965d9;hpb=0ba1b11c49d7b2a0cae493200793c1ba3fb8b8e7

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index eb6c590..b8ea1a0 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -1,6 +1,6 @@
 #' selectVariables
 #'
-#' It is a function which construct, for a given lambda, the sets of relevant variables.
+#' For a given lambda, construct the sets of relevant variables for each cluster.
 #'
 #' @param phiInit an initial estimator for phi (size: p*m*k)
 #' @param rhoInit an initial estimator for rho (size: m*m*k)
@@ -15,13 +15,12 @@
 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
 #' @param eps   threshold to say that EM algorithm has converged
 #' @param ncores Number or cores for parallel execution (1 to disable)
+#' @param fast boolean to enable or not the C function call
 #'
-#' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
-#'
-#' @examples TODO
+#' @return a list, varying lambda in a grid, with selected (the indices of variables that are selected),
+#' Rho (the covariance parameter, reparametrized), Pi (the proportion parameter)
 #'
 #' @export
-#'
 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
   glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
 {
@@ -68,10 +67,7 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
   if (ncores > 1)
     parallel::stopCluster(cl)
 
-  print(out)
-  # Suppress models which are computed twice En fait, ca ca fait la comparaison de
-  # tous les parametres On veut juste supprimer ceux qui ont les memes variables
-  # sélectionnées
+  # Suppress models which are computed twice
   # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
   selec <- lapply(out, function(model) model$selected)
   ind_dup <- duplicated(selec)