started to look at EMGLLF.c
[valse.git] / pkg / R / computeGridLambda.R
CommitLineData
228ee602 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 for rho
7#' @param piInit 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
19computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
20 maxi, tau, fast)
21{
22 n <- nrow(X)
23 p <- ncol(X)
24 m <- ncol(Y)
25 k <- length(piInit)
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 (j in 1:p)
31 {
32 for (mm in 1:m)
33 grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma)
34 }
35 sort(unique(grid))
36}