-#' computeGridLambda
+#' computeGridLambda
#'
#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#'
#' @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
+#' @param eps 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)
+computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
+ maxi, eps, fast)
{
n <- nrow(X)
- p <- dim(phiInit)[1]
- m <- dim(phiInit)[2]
- k <- dim(phiInit)[3]
+ p <- ncol(X)
+ m <- ncol(Y)
+ k <- length(piInit)
+
+ list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
+ X, Y, eps, fast)
- 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)
{