#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#'
#' @param phiInit value for phi
#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#'
#' @param phiInit value for phi
#' @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 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
#'
#' @return the grid of regularization parameters
#'
#' @export
computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
#'
#' @return the grid of regularization parameters
#'
#' @export
computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
k <- length(piInit)
list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
k <- length(piInit)
list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
grid <- array(0, dim = c(p, m, k))
grid <- array(0, dim = c(p, m, k))
- for (j in 1:m)
- grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
+ for (mm in 1:m)
+ grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma)