X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2FselectVariables.R;fp=R%2FselectVariables.R;h=0000000000000000000000000000000000000000;hp=46fb3f33165106d2d4f3a91943b81ac7709b7b74;hb=f87ff0f5116c0c1c59c5608e46563ff0f79e5d43;hpb=53fa233d8fbeaf4d51a4874ba69d8472d01d04ba

diff --git a/R/selectVariables.R b/R/selectVariables.R
deleted file mode 100644
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--- a/R/selectVariables.R
+++ /dev/null
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-#' selectVariables
-#' It is a function which construct, for a given lambda, the sets of relevant 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 a list of outputs, for each lambda in grid: selected,Rho,Pi
-#'
-#' @examples TODO
-#'
-#' @export
-selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau)
-{
-	#TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
-	cl = parallel::makeCluster( parallel::detectCores() / 4 )
-	parallel::clusterExport(cl=cl,
-		varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","seuil","tau"),
-		envir=environment())
-	#Pour chaque lambda de la grille, on calcule les coefficients
-	out = parLapply( seq_along(glambda), function(lambdaindex)
-	{
-		p = dim(phiInit)[1]
-		m = dim(phiInit)[2]
-
-		params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
-
-		#selectedVariables: list where element j contains vector of selected variables in [1,m]
-		selectedVariables = lapply(1:p, function(j) {
-			#from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
-			#and finally return the corresponding indices
-			seq_len(m)[ apply( abs(params$phi[j,,]) > seuil, 1, any ) ]
-		})
-
-		list("selected"=selectedVariables,"Rho"=params$Rho,"Pi"=params$Pi)
-	})
-	parallel::stopCluster(cl)
-	out
-}