-#' selectVariables
+#' selectVariables
#'
-#' It is a function which construct, for a given lambda, the sets of relevant variables.
+#' For a given lambda, construct the sets of relevant variables for each cluster.
#'
#' @param phiInit an initial estimator for phi (size: p*m*k)
#' @param rhoInit an initial estimator for rho (size: m*m*k)
-#' @param piInit\tan initial estimator for pi (size : k)
+#' @param piInit an initial estimator for pi (size : k)
#' @param gamInit an initial estimator for gamma
-#' @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 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\t\t\t matrix of regressors
-#' @param Y\t\t\t matrix of responses
+#' @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\t\t threshold to say that EM algorithm has converged
+#' @param eps threshold to say that EM algorithm has converged
#' @param ncores Number or cores for parallel execution (1 to disable)
+#' @param fast boolean to enable or not the C function call
#'
-#' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
-#'
-#' @examples TODO
+#' @return a list, varying lambda in a grid, with selected (the indices of variables that are selected),
+#' Rho (the covariance parameter, reparametrized), Pi (the proportion parameter)
#'
#' @export
-#'
-selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
+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",
+ 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,
+ params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
X, Y, eps, fast)
- p <- dim(phiInit)[1]
- m <- dim(phiInit)[2]
+ p <- ncol(X)
+ m <- ncol(Y)
# 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$phi[j, , ]) > thresh, 1, any)]
+ if (m>1) {
+ seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
+ } else {
+ if (any(params$phi[j, , ] > thresh))
+ 1
+ else
+ numeric(0)
+ }
})
list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
} else {
lapply(glambda, computeCoefs)
}
- if (ncores > 1)
+ if (ncores > 1)
parallel::stopCluster(cl)
- # Suppress models which are computed twice En fait, ca ca fait la comparaison de
- # tous les parametres On veut juste supprimer ceux qui ont les memes variables
- # sélectionnées
+
+ # Suppress models which are computed twice
# sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
selec <- lapply(out, function(model) model$selected)
ind_dup <- duplicated(selec)