Commit | Line | Data |
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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 | |
6279ba86 | 14 | #' @param eps threshold to stop EM algorithm |
228ee602 | 15 | #' |
16 | #' @return the grid of regularization parameters | |
17 | #' | |
18 | #' @export | |
19 | computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, | |
6279ba86 | 20 | maxi, eps, fast) |
228ee602 | 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, | |
6279ba86 BA |
28 | X, Y, eps, fast) |
29 | ||
228ee602 | 30 | grid <- array(0, dim = c(p, m, k)) |
31 | for (j in 1:p) | |
32 | { | |
33 | for (mm in 1:m) | |
34 | grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma) | |
35 | } | |
36 | sort(unique(grid)) | |
37 | } |