#' computeGridLambda #' #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator #' #' @param phiInit value for phi #' @param rhoInit for rho #' @param piInit 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 (j in 1:p) { for (mm in 1:m) grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma) } sort(unique(grid)) }