-#' selectVariables
+#' 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 piInit\tan 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 mini\t\tminimum number of iterations in EM algorithm
+#' @param maxi\t\tmaximum number of iterations in EM algorithm
+#' @param gamma\t power in the penalty
#' @param glambda grid of regularization parameters
-#' @param X matrix of regressors
-#' @param Y matrix of responses
+#' @param X\t\t\t matrix of regressors
+#' @param Y\t\t\t matrix of responses
#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
-#' @param eps threshold to say that EM algorithm has converged
+#' @param eps\t\t threshold to say that EM algorithm has converged
#' @param ncores Number or cores for parallel execution (1 to disable)
#'
#' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
#'
#' @export
#'
-selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
- X,Y,thresh=1e-8,eps, ncores=3, fast=TRUE)
-{
+selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
+ glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
+ {
if (ncores > 1)
{
- cl = parallel::makeCluster(ncores, outfile='')
- parallel::clusterExport(cl=cl,
- varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","eps"),
- envir=environment())
+ cl <- parallel::makeCluster(ncores, outfile = "")
+ parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
+ "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
}
# Computation for a fixed lambda
computeCoefs <- function(lambda)
{
- params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,eps,fast)
+ params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
+ X, Y, eps, fast)
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
+ p <- dim(phiInit)[1]
+ m <- dim(phiInit)[2]
- #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$phi[j,,]) > thresh, 1, any ) ]
+ # selectedVariables: list where element j contains vector of selected variables
+ # in [1,m]
+ selectedVariables <- lapply(1:p, function(j)
+ {
+ # from boolean matrix mxk of selected variables obtain the corresponding boolean
+ # m-vector, and finally return the corresponding indices
+ seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
})
- list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)
+ list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
}
# For each lambda in the grid, we compute the coefficients
- out <-
- if (ncores > 1)
- parLapply(cl, glambda, computeCoefs)
- else
- lapply(glambda, computeCoefs)
- if (ncores > 1)
+ out <- if (ncores > 1)
+ parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
+ if (ncores > 1)
parallel::stopCluster(cl)
- # Suppress models which are computed twice
- #En fait, ca ca fait la comparaison de tous les parametres
- #On veut juste supprimer ceux qui ont les memes variables sélectionnées
- #sha1_array <- lapply(out, digest::sha1)
- #out[ duplicated(sha1_array) ]
- selec = lapply(out, function(model) model$selected)
- ind_dup = duplicated(selec)
- ind_uniq = which(!ind_dup)
- out2 = list()
- for (l in 1:length(ind_uniq)){
- out2[[l]] = out[[ind_uniq[l]]]
+ # Suppress models which are computed twice En fait, ca ca fait la comparaison de
+ # tous les parametres On veut juste supprimer ceux qui ont les memes variables
+ # sélectionnées sha1_array <- lapply(out, digest::sha1) out[
+ # duplicated(sha1_array) ]
+ selec <- lapply(out, function(model) model$selected)
+ ind_dup <- duplicated(selec)
+ ind_uniq <- which(!ind_dup)
+ out2 <- list()
+ for (l in 1:length(ind_uniq))
+ {
+ out2[[l]] <- out[[ind_uniq[l]]]
}
out2
}