X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FgridLambda.R;fp=pkg%2FR%2FgridLambda.R;h=35c412a8282068722fde6255cd4180a4b4ddfc24;hb=f87ff0f5116c0c1c59c5608e46563ff0f79e5d43;hp=0000000000000000000000000000000000000000;hpb=53fa233d8fbeaf4d51a4874ba69d8472d01d04ba;p=valse.git

diff --git a/pkg/R/gridLambda.R b/pkg/R/gridLambda.R
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+#' 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)
+}