pkg/R/gridLambda.R

   1 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
   2 #' @param phiInit value for phi
   3 #' @param rhoInit   value for rho
   4 #' @param piInit    value for pi
   5 #' @param gamInit value for gamma
   6 #' @param X matrix of covariates (of size n*p)
   7 #' @param Y matrix of responses (of size n*m)
   8 #' @param gamma power of weights in the penalty
   9 #' @param mini      minimum number of iterations in EM algorithm
  10 #' @param maxi      maximum number of iterations in EM algorithm
  11 #' @param tau       threshold to stop EM algorithm
  12 #' @return the grid of regularization parameters
  13 #' @export
  14 #-----------------------------------------------------------------------
  15 gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
  16 {
  17     n = nrow(X)
  18     p = dim(phiInit)[1]
  19     m = dim(phiInit)[2]
  20     k = dim(phiInit)[3]
  21
  22     #list_EMG = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
  23   list_EMG = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
  24     grid = array(0, dim=c(p,m,k))
  25     for (i in 1:p)
  26     {
  27         for (j in 1:m)
  28             grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
  29     }
  30     grid = unique(grid)
  31     grid = grid[grid <=1]
  32
  33     return(grid)
  34 }