X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FcomputeGridLambda.R;h=597d5c8c5d6f5670e98a13e43314a41fe3aaa402;hp=c2e9c8c95a04fa36708b50ec861231bd438100d6;hb=ca277ac5ab51fef149014eb5e4610403fdb3227b;hpb=a3cbbaea1cc3c107e5ca62ed1ffe7b9499de0a91

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index c2e9c8c..597d5c8 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -3,8 +3,8 @@
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
 #' @param phiInit value for phi
-#' @param rhoInit\tvalue for rho
-#' @param piInit\tvalue for pi
+#' @param rhoInit  for rho
+#' @param piInit  for pi
 #' @param gamInit value for gamma
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
@@ -27,10 +27,10 @@ computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mi
   list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, 
     X, Y, tau, fast)
   grid <- array(0, dim = c(p, m, k))
-  for (i in 1:p)
+  for (j in 1:p)
   {
-    for (j in 1:m)
-      grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
+    for (mm in 1:m)
+      grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma)
   }
   sort(unique(grid))
 }