essai fusion
[valse.git] / 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 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
19 computeGridLambda <- 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 }