+++ /dev/null
-#' 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 = 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))
-}