--- /dev/null
+#' selectVaribles
+#' It is a function which construct, for a given lambda, the sets of
+#' relevant variables and irrelevant variables.
+#'
+#' @param phiInit an initial estimator for phi (size: p*m*k)
+#' @param rhoInit an initial estimator for rho (size: m*m*k)
+#' @param piInit an initial estimator for pi (size : k)
+#' @param gamInit an initial estimator for gamma
+#' @param mini minimum number of iterations in EM algorithm
+#' @param maxi maximum number of iterations in EM algorithm
+#' @param gamma power in the penalty
+#' @param glambda grid of regularization parameters
+#' @param X matrix of regressors
+#' @param Y matrix of responses
+#' @param thres threshold to consider a coefficient to be equal to 0
+#' @param tau threshold to say that EM algorithm has converged
+#'
+#' @return
+#' @export
+#'
+#' @examples
+selectVariables <- function(phiInit,rhoInit,piInit,gamInit,
+ mini,maxi,gamma,glambda,X,Y,thres,tau){
+
+ dimphi <- dim(phiInit)
+ p <- dimPhi[1]
+ m <- dimPhi[2]
+ k <- dimPhi[3]
+ L <- length(glambda);
+ A1 <- array(0, dim <- c(p,m+1,L))
+ A2 <- array(0, dim <- c(p,m+1,L))
+ Rho <- array(0, dim <- c(m,m,k,L))
+ Pi <- array(0, dim <- c(k,L));
+
+ # For every lambda in gridLambda, comutation of the coefficients
+ for (lambdaIndex in c(1:L)) {
+ Res <- EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,
+ gamma,glambda[lambdaIndex],X,Y,tau);
+ phi <- Res$phi
+ rho <- Res$rho
+ pi <- Res$pi
+
+ # If a coefficient is larger than the threshold, we keep it
+ selectedVariables <- array(0, dim = c(p,m))
+ discardedVariables <- array(0, dim = c(p,m))
+ atLeastOneSelectedVariable <- false
+ for (j in c(1:p)){
+ cpt <- 1
+ cpt2 <-1
+ for (mm in c(1:m)){
+ if (max(abs(phi[j,mm,])) > thres){
+ selectedVariables[j,cpt] <- mm
+ cpt <- cpt+1
+ atLeastOneSelectedVariable <- true
+ } else{
+ discardedVariables[j,cpt2] <- mm
+ cpt2 <- cpt2+1
+ }
+ }
+ }
+
+ # If no coefficients have been selected, we provide the zero matrix
+ # We delete zero coefficients: vec = indices of zero values
+ if atLeastOneSelectedVariable{
+ vec <- c()
+ for (j in c(1:p)){
+ if (selectedVariables(j,1) =! 0){
+ vec <- c(vec,j)
+ }
+ }
+ # Else, we provide the indices of relevant coefficients
+ A1[,1,lambdaIndex] <- c(vec,rep(0,p-length(vec)))
+ A1[1:length(vec),2:(m+1),lambdaIndex] <- selectedVariables[vec,]
+ A2[,1,lambdaIndex] <- 1:p
+ A2[,2:(m+1),lambdaIndex] <- discardedVariables
+ Rho[,,,lambdaIndex] <- rho
+ Pi[,lambdaIndex] <- pi
+ }
+
+ }
+ return(res = list(A1 = A1, A2 = A2 , Rho = Rho, Pi = Pi))
+}
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