| 1 | #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator |
| 2 | #' @param phiInit value for phi |
| 3 | #' @param rhoInt value for rho |
| 4 | #' @param piInit value for pi |
| 5 | #' @param gamInit value for gamma |
| 6 | #' @param mini minimum number of iterations in EM algorithm |
| 7 | #' @param maxi maximum number of iterations in EM algorithm |
| 8 | #' @param tau threshold to stop EM algorithm |
| 9 | #' @return the grid of regularization parameters |
| 10 | #' @export |
| 11 | #----------------------------------------------------------------------- |
| 12 | gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau) |
| 13 | { |
| 14 | n = nrow(X) |
| 15 | p = dim(phiInit)[1] |
| 16 | m = dim(phiInit)[2] |
| 17 | k = dim(phiInit)[3] |
| 18 | |
| 19 | list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau) |
| 20 | |
| 21 | grid = array(0, dim=c(p,m,k)) |
| 22 | for (i in 1:p) |
| 23 | { |
| 24 | for (j in 1:m) |
| 25 | grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma) |
| 26 | } |
| 27 | grid = unique(grid) |
| 28 | grid = grid[grid <=1] |
| 29 | |
| 30 | return(grid) |
| 31 | } |