X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;h=bab45cccee4f7bf1d39aa48dc8f13a33d04eaae9;hb=923ed737d0fa335b858204b813c964432488abbe;hp=bfe4042d1ec639173b38bd65ac9cb113c186b564;hpb=a3cbbaea1cc3c107e5ca62ed1ffe7b9499de0a91;p=valse.git

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index bfe4042..bab45cc 100644
--- 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
@@ -37,15 +37,22 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
     params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
       X, Y, eps, fast)
 
-    p <- dim(phiInit)[1]
-    m <- dim(phiInit)[2]
+    p <- ncol(X)
+    m <- ncol(Y)
 
     # selectedVariables: list where element j contains vector of selected variables
     # in [1,m]
     selectedVariables <- lapply(1:p, function(j) {
       # from boolean matrix mxk of selected variables obtain the corresponding boolean
       # m-vector, and finally return the corresponding indices
-      seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+      if (m>1) {
+        seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+      } else {
+        if (any(params$phi[j, , ] > thresh))
+          1
+        else
+          numeric(0)
+      }
     })
 
     list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
@@ -60,6 +67,8 @@ 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