f717caeb46125472a1565b3024f7385c43ec952d
1 #' selectVariables
2 #'
3 #' It is a function which construct, for a given lambda, the sets of relevant variables.
4 #'
5 #' @param phiInit an initial estimator for phi (size: p*m*k)
6 #' @param rhoInit an initial estimator for rho (size: m*m*k)
7 #' @param piInit\tan initial estimator for pi (size : k)
8 #' @param gamInit an initial estimator for gamma
9 #' @param mini\t\tminimum number of iterations in EM algorithm
10 #' @param maxi\t\tmaximum number of iterations in EM algorithm
11 #' @param gamma\t power in the penalty
12 #' @param glambda grid of regularization parameters
13 #' @param X\t\t\t matrix of regressors
14 #' @param Y\t\t\t matrix of responses
15 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
16 #' @param eps\t\t threshold to say that EM algorithm has converged
17 #' @param ncores Number or cores for parallel execution (1 to disable)
18 #'
19 #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
20 #'
21 #' @examples TODO
22 #'
23 #' @export
24 #'
25 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
26 glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
27 {
28 if (ncores > 1) {
29 cl <- parallel::makeCluster(ncores, outfile = "")
30 parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
31 "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
32 }
34 # Computation for a fixed lambda
35 computeCoefs <- function(lambda)
36 {
37 params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
38 X, Y, eps, fast)
40 p <- dim(phiInit)[1]
41 m <- dim(phiInit)[2]
43 # selectedVariables: list where element j contains vector of selected variables
44 # in [1,m]
45 selectedVariables <- lapply(1:p, function(j) {
46 # from boolean matrix mxk of selected variables obtain the corresponding boolean
47 # m-vector, and finally return the corresponding indices
48 seq_len(m)[apply(abs(params\$phi[j, , ]) > thresh, 1, any)]
49 })
51 list(selected = selectedVariables, Rho = params\$rho, Pi = params\$pi)
52 }
54 # For each lambda in the grid, we compute the coefficients
55 out <- if (ncores > 1)
56 parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
57 if (ncores > 1)
58 parallel::stopCluster(cl)
59 # Suppress models which are computed twice En fait, ca ca fait la comparaison de
60 # tous les parametres On veut juste supprimer ceux qui ont les memes variables