X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FcomputeGridLambda.R;h=f4073d0881742f6c94af92d83854df4fef1a2ab5;hp=8449d1074a75a32b310f6e11147f08d2c9e0324e;hb=6af1d4897dbab92a7be05068e0e15823378965d9;hpb=228ee602a972fcac6177db0d539bf9d0c5fa477f

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index 8449d10..f4073d0 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -1,4 +1,4 @@
-#' computeGridLambda 
+#' computeGridLambda
 #'
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
@@ -11,21 +11,24 @@
 #' @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
+#' @param fast boolean to enable or not the C function call
 #'
-#' @return the grid of regularization parameters
+#' @return the grid of regularization parameters for the Lasso estimator. The output is a vector with nonnegative values that are relevant
+#' to be considered as regularization parameter as they are equivalent to a 0 in the regression parameter.
 #'
 #' @export
-computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
-  maxi, tau, fast)
+computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
+  maxi, eps, fast)
 {
   n <- nrow(X)
   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)
+  list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
+    X, Y, eps, fast)
+
   grid <- array(0, dim = c(p, m, k))
   for (j in 1:p)
   {