X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FgridLambda.R;h=35c412a8282068722fde6255cd4180a4b4ddfc24;hb=f227455a1604906b255ef366d64c10a93e796983;hp=2c66e4ca23572eea99adcb0c76d7200b1c09e43a;hpb=b4899af94061fca34163bdef9ae3ff2155038bb7;p=valse.git

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 2c66e4c..35c412a 100644
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,8 +1,11 @@
 #' 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 rhoInit	value for rho
 #' @param piInit	value 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
@@ -16,8 +19,8 @@ gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi
 	m = dim(phiInit)[2]
 	k = dim(phiInit)[3]
 
-	list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
-
+	#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)
 	{