X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FcomputeGridLambda.R;h=8449d1074a75a32b310f6e11147f08d2c9e0324e;hb=23b9fb13bc6e82d7ca43bfb83aa85b6cd69c52c0;hp=4b68bcdb9997ab135cbf1a5e8af12dc0b062c489;hpb=ffdf94474d96cdd3e9d304ce809df7e62aa957ed;p=valse.git

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index 4b68bcd..8449d10 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)
@@ -17,19 +17,20 @@
 #'
 #' @export
 computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
-  maxi, tau, fast = TRUE)
-  {
+  maxi, tau, fast)
+{
   n <- nrow(X)
-  p <- dim(phiInit)[1]
-  m <- dim(phiInit)[2]
-  k <- dim(phiInit)[3]
-  
+  p <- ncol(X)
+  m <- ncol(Y)
+  k <- length(piInit)
+
   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))
 }