pkg/R/computeGridLambda.R

   1 #' computeGridLambda
   2 #'
   3 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
   4 #'
   5 #' @param phiInit value for phi
   6 #' @param rhoInit\tvalue for rho
   7 #' @param piInit\tvalue for pi
   8 #' @param gamInit value for gamma
   9 #' @param X matrix of covariates (of size n*p)
  10 #' @param Y matrix of responses (of size n*m)
  11 #' @param gamma power of weights in the penalty
  12 #' @param mini minimum number of iterations in EM algorithm
  13 #' @param maxi maximum number of iterations in EM algorithm
  14 #' @param tau threshold to stop EM algorithm
  15 #'
  16 #' @return the grid of regularization parameters
  17 #'
  18 #' @export
  19 computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
  20   maxi, tau, fast = TRUE)
  21 {
  22   n <- nrow(X)
  23   p <- dim(phiInit)[1]
  24   m <- dim(phiInit)[2]
  25   k <- dim(phiInit)[3]
  26
  27   list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
  28     X, Y, tau, fast)
  29   grid <- array(0, dim = c(p, m, k))
  30   for (i in 1:p)
  31   {
  32     for (j in 1:m)
  33       grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
  34   }
  35   sort(unique(grid))
  36 }