3 #' It is a function which construct, for a given lambda, the sets of relevant variables.
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
18 #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
24 selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
25 X,Y,thresh,tau, ncores=1) #ncores==1 ==> no //
29 cl = parallel::makeCluster(ncores)
30 parallel::clusterExport(cl=cl,
31 varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
35 # Calcul pour un lambda
36 computeCoefs <-function(lambda)
38 params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
43 #selectedVariables: list where element j contains vector of selected variables in [1,m]
44 selectedVariables = sapply(1:p, function(j) { ## je me suis permise de changer le type,
45 ##une liste de liste ca devenait compliqué je trouve pour choper ce qui nous intéresse
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$phi[j,,]) > thresh, 1, any ) ]
49 c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ],
50 rep(0, m-length(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ] ) ))
53 list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
56 # Pour chaque lambda de la grille, on calcule les coefficients
59 parLapply(cl, seq_along(glambda, computeCoefs))}
60 else lapply(seq_along(glambda), computeCoefs)
62 parallel::stopCluster(cl)}