-#' 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
-#-----------------------------------------------------------------------
-gridLambda = 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 = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
- list_EMG = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,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 = grid[grid <=1]
-
- return(grid)
-}