X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FselectVariables.R;fp=pkg%2FR%2FselectVariables.R;h=0000000000000000000000000000000000000000;hp=cdc0ec00f672d534d65c8a9b5dfb951b79a5e468;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=ca277ac5ab51fef149014eb5e4610403fdb3227b

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
deleted file mode 100644
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--- a/pkg/R/selectVariables.R
+++ /dev/null
@@ -1,74 +0,0 @@
-#' 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 thresh real, threshold to say a variable is relevant, by default = 1e-8
-#' @param eps   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
-#'
-#' @examples TODO
-#'
-#' @export
-#'
-selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
-  glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
-{
-  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())
-  }
-
-  # Computation for a fixed lambda
-  computeCoefs <- function(lambda)
-  {
-    params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
-      X, Y, eps, fast)
-
-    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$phi[j, , ]) > thresh, 1, any)]
-    })
-
-    list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
-  }
-
-  # For each lambda in the grid, we compute the coefficients
-  out <-
-    if (ncores > 1) {
-      parLapply(cl, glambda, computeCoefs)
-    } else {
-      lapply(glambda, computeCoefs)
-    }
-  if (ncores > 1) 
-    parallel::stopCluster(cl)
-  # Suppress models which are computed twice En fait, ca ca fait la comparaison de
-  # tous les parametres On veut juste supprimer ceux qui ont les memes variables
-  # sélectionnées
-  # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
-  selec <- lapply(out, function(model) model$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]]]
-  out2
-}