Folder reorganization

[valse.git] / R / selectVariables.R

diff --git a/R/selectVariables.R b/R/selectVariables.R
deleted file mode 100644 (file)
index 46fb3f3..0000000
--- 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
-}