X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FselectVariables.R;fp=R%2FselectVariables.R;h=be53d855e3a05e7790062533085aa05bf2195362;hp=0000000000000000000000000000000000000000;hb=e01c9b1fc45d307b00a9a8a6f6395850107a1d60;hpb=0b216f854a21821f9be375d07c2932b31e227e78

diff --git a/R/selectVariables.R b/R/selectVariables.R
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+#' 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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