X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FcomputeGridLambda.R;h=597d5c8c5d6f5670e98a13e43314a41fe3aaa402;hp=c2e9c8c95a04fa36708b50ec861231bd438100d6;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=a3cbbaea1cc3c107e5ca62ed1ffe7b9499de0a91

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
deleted file mode 100644
index c2e9c8c..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\tvalue for rho
-#' @param piInit\tvalue 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)
-{
-  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))
-}