#' 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 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 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)
{
#' @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",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)
+ n = nrow(X)
+ p = dim(phiInit)[1]
+ m = dim(phiInit)[2]
+ k = dim(phiInit)[3]
+
+ list_EMG = .Call("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)