Commit | Line | Data |
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ffdf9447 | 1 | #' computeGridLambda |
086ca318 | 2 | #' |
d1531659 | 3 | #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator |
086ca318 | 4 | #' |
d1531659 | 5 | #' @param phiInit value for phi |
ca277ac5 | 6 | #' @param rhoInit for rho |
7 | #' @param piInit for pi | |
d1531659 | 8 | #' @param gamInit value for gamma |
e3f2fe8a | 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 | |
086ca318 BA |
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 | #' | |
d1531659 | 16 | #' @return the grid of regularization parameters |
086ca318 | 17 | #' |
d1531659 | 18 | #' @export |
ffdf9447 | 19 | computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, |
a3cbbaea | 20 | maxi, tau, fast) |
1b698c16 | 21 | { |
ffdf9447 BA |
22 | n <- nrow(X) |
23 | p <- dim(phiInit)[1] | |
24 | m <- dim(phiInit)[2] | |
25 | k <- dim(phiInit)[3] | |
1b698c16 | 26 | |
ffdf9447 BA |
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)) | |
ca277ac5 | 30 | for (j in 1:p) |
ffdf9447 | 31 | { |
ca277ac5 | 32 | for (mm in 1:m) |
33 | grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma) | |
ffdf9447 BA |
34 | } |
35 | sort(unique(grid)) | |
39046da6 | 36 | } |