X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FgridLambda.R;h=7b82f6316cc3f0f323fe220fbece4fc13a787d73;hp=66b6cc2eba16aed17403d0f9df8ce377c609abf2;hb=d1531659214edd6eaef0ac9ec835455614bba16c;hpb=6bd2e869a17f3980d52820643c1c1b5f3725738e

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 66b6cc2..7b82f63 100644
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,20 +1,31 @@
+#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
+#' @param phiInit value for phi
+#' @param rhoInt  value for rho
+#' @param piInit  value for pi
+#' @param gamInit value for gamma
+#' @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
+#' @return the grid of regularization parameters
+#' @export
+#-----------------------------------------------------------------------
 gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
 {
-	n = nrow(X)
-	p = dim(phiInit)[1]
-	m = dim(phiInit)[2]
-	k = dim(phiInit)[3]
-
-	list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
-
-	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 = grid[grid <=1]
-
-	return(grid)
+  n = nrow(X)
+  p = dim(phiInit)[1]
+  m = dim(phiInit)[2]
+  k = dim(phiInit)[3]
+  
+  list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+  
+  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 = grid[grid <=1]
+  
+  return(grid)
 }