869e7bfc99967f5f4e79b131200c625de39e0a4e
[valse.git] / pkg / R / selectVariables.R
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 an initial estimator for pi (size : k)
8 #' @param gamInit an initial estimator for gamma
9 #' @param mini minimum number of iterations in EM algorithm
10 #' @param maxi maximum number of iterations in EM algorithm
11 #' @param gamma power in the penalty
12 #' @param glambda grid of regularization parameters
13 #' @param X matrix of regressors
14 #' @param Y matrix of responses
15 #' @param thres threshold to consider a coefficient to be equal to 0
16 #' @param tau 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,glambda,
26 X,Y,thresh,tau, ncores=3)
27 {
28 if (ncores > 1)
29 {
30 cl = parallel::makeCluster(ncores)
31 parallel::clusterExport(cl=cl,
32 varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
33 envir=environment())
34 }
35
36 # Calcul pour un lambda
37 computeCoefs <-function(lambda)
38 {
39 params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
40
41 p = dim(phiInit)[1]
42 m = dim(phiInit)[2]
43
44 #selectedVariables: list where element j contains vector of selected variables in [1,m]
45 selectedVariables = lapply(1:p, function(j) {
46 #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
47 #and finally return the corresponding indices
48 seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
49 })
50
51 list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
52 }
53
54 # Pour chaque lambda de la grille, on calcule les coefficients
55 out <-
56 if (ncores > 1)
57 parLapply(cl, glambda, computeCoefs)
58 else
59 lapply(glambda, computeCoefs)
60 if (ncores > 1)
61 parallel::stopCluster(cl)
62
63 # Suppression doublons
64 sha1_array <- lapply(out, digest::sha1)
65 out[ !duplicated(sha1_array) ]
66
67 out
68 }