X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FgridLambda.R;fp=R%2FgridLambda.R;h=0000000000000000000000000000000000000000;hp=35c412a8282068722fde6255cd4180a4b4ddfc24;hb=f33f35efc9a01f93bb61959522d90ee6a76b892e;hpb=f1b0e0abc369eed5b917a5fe70a05592067a78d2

diff --git a/R/gridLambda.R b/R/gridLambda.R
deleted file mode 100644
index 35c412a..0000000
--- a/R/gridLambda.R
+++ /dev/null
@@ -1,34 +0,0 @@
-#' 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 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
-#' @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_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)
-}