#' 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, gamma, lambda=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 }