X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FcomputeGridLambda.R;h=c2e9c8c95a04fa36708b50ec861231bd438100d6;hp=8ec4d6604af58e3a47e2e56765d9953bd74c305d;hb=a3cbbaea1cc3c107e5ca62ed1ffe7b9499de0a91;hpb=aa480ac1fef50618978307a4df2cf9da1e285abc

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index 8ec4d66..c2e9c8c 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -1,10 +1,10 @@
-#' computeGridLambda
+#' computeGridLambda 
 #'
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
 #' @param phiInit value for phi
-#' @param rhoInit	value for rho
-#' @param piInit	value for pi
+#' @param rhoInit\tvalue for rho
+#' @param piInit\tvalue 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)
@@ -16,22 +16,21 @@
 #' @return the grid of regularization parameters
 #'
 #' @export
-computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y,
-	gamma, mini, maxi, tau, fast=TRUE)
+computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
+  maxi, tau, fast)
 {
-	n = nrow(X)
-	p = dim(phiInit)[1]
-	m = dim(phiInit)[2]
-	k = dim(phiInit)[3]
+  n <- nrow(X)
+  p <- dim(phiInit)[1]
+  m <- dim(phiInit)[2]
+  k <- dim(phiInit)[3]
 
-	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:m)
-			grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
-	}
-	grid = unique(grid)
-	grid
+  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:m)
+      grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
+  }
+  sort(unique(grid))
 }