X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FcomputeGridLambda.R;h=597d5c8c5d6f5670e98a13e43314a41fe3aaa402;hp=b29553505d361f0c0916b666bdcacdf017f49402;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=0930b5d395ef0a48d1f97f88ee533c13d0962759

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
deleted file mode 100644
index b295535..0000000
--- a/pkg/R/computeGridLambda.R
+++ /dev/null
@@ -1,36 +0,0 @@
-#' computeGridLambda
-#'
-#' 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
-computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y,
-	gamma, mini, maxi, tau, fast=TRUE)
-{
-	n = nrow(X)
-	p = dim(phiInit)[1]
-	m = dim(phiInit)[2]
-	k = dim(phiInit)[3]
-
-	list_EMG = EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-		gamma, lambda=0, X, Y, tau, fast)
-	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)
-	}
-	sort( unique(grid) )
-}