X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FcomputeGridLambda.R;h=ac0788a138dd4c28f61dcbf5de3b68da619c0f35;hp=cf762ec8d30981a731177e2abbfd11a770b989ef;hb=6279ba8656582370e7242ff9e77d22c23fe8ca04;hpb=ea5860f1b4fc91f06e371a0b26915198474a849d

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index cf762ec..ac0788a 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -3,21 +3,21 @@
 #' 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)
 #' @param gamma power of weights in the penalty
 #' @param mini minimum number of iterations in EM algorithm
 #' @param maxi maximum number of iterations in EM algorithm
-#' @param tau threshold to stop EM algorithm
+#' @param eps threshold to stop EM algorithm
 #'
 #' @return the grid of regularization parameters
 #'
 #' @export
 computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
-  maxi, tau, fast)
+  maxi, eps, fast)
 {
   n <- nrow(X)
   p <- ncol(X)
@@ -25,12 +25,13 @@ computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mi
   k <- length(piInit)
 
   list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, 
-    X, Y, tau, fast)
+    X, Y, eps, 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))
 }