essai fusion

[valse.git] / pkg / R / computeGridLambda.R

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
deleted file mode 100644 (file)
index f89b2a3..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)
-{
-       n = nrow(X)
-       p = dim(phiInit)[1]
-       m = dim(phiInit)[2]
-       k = dim(phiInit)[3]
-
-  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]
-       grid
-}