X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FgridLambda.R;h=35c412a8282068722fde6255cd4180a4b4ddfc24;hb=f227455a1604906b255ef366d64c10a93e796983;hp=7b82f6316cc3f0f323fe220fbece4fc13a787d73;hpb=d1531659214edd6eaef0ac9ec835455614bba16c;p=valse.git

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 7b82f63..35c412a 100644
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,31 +1,34 @@
 #' 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 rhoInit	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
+#' @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
 #' @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_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+  list_EMG = 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)
 }