fix few things for the LLF

[valse.git] / pkg / R / selectVariables.R

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index f717cae..cdc0ec0 100644 (file)
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -4,16 +4,16 @@
 #'
 #' @param phiInit an initial estimator for phi (size: p*m*k)
 #' @param rhoInit an initial estimator for rho (size: m*m*k)
-#' @param piInit\tan initial estimator for pi (size : k)
+#' @param piInit an initial estimator for pi (size : k)
 #' @param gamInit an initial estimator for gamma
-#' @param mini\t\tminimum number of iterations in EM algorithm
-#' @param maxi\t\tmaximum number of iterations in EM algorithm
-#' @param gamma\t power in the penalty
+#' @param mini  minimum number of iterations in EM algorithm
+#' @param maxi  maximum number of iterations in EM algorithm
+#' @param gamma  power in the penalty
 #' @param glambda grid of regularization parameters
-#' @param X\t\t\t matrix of regressors
-#' @param Y\t\t\t matrix of responses
+#' @param X    matrix of regressors
+#' @param Y    matrix of responses
 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
-#' @param eps\t\t threshold to say that EM algorithm has converged
+#' @param eps   threshold to say that EM algorithm has converged
 #' @param ncores Number or cores for parallel execution (1 to disable)
 #'
 #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
@@ -23,7 +23,7 @@
 #' @export
 #'
 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
-  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
+  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
 {
   if (ncores > 1) {
     cl <- parallel::makeCluster(ncores, outfile = "")
@@ -52,14 +52,18 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
   }
 
   # For each lambda in the grid, we compute the coefficients
-  out <- if (ncores > 1) 
-    parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, glambda, computeCoefs)
+    } else {
+      lapply(glambda, computeCoefs)
+    }
   if (ncores > 1) 
     parallel::stopCluster(cl)
   # 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 sha1_array <- lapply(out, digest::sha1) out[
-  # duplicated(sha1_array) ]
+  # sélectionnées
+  # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
   selec <- lapply(out, function(model) model$selected)
   ind_dup <- duplicated(selec)
   ind_uniq <- which(!ind_dup)