X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FEMGrank.R;fp=pkg%2FR%2FEMGrank.R;h=09171ac56976a72b5e10af0bdd863ade4003150d;hp=4054e25dae40f7f082c55838d6add635be51ef5a;hb=e9db79707709c10947e89756eb5655c0747a2a1d;hpb=0ba1b11c49d7b2a0cae493200793c1ba3fb8b8e7

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index 4054e25..09171ac 100644
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -1,19 +1,22 @@
 #' EMGrank
 #'
-#' Description de EMGrank
+#' Run an generalized EM algorithm developped for mixture of Gaussian regression 
+#' models with variable selection by an extension of the low rank estimator.
+#' Reparametrization is done to ensure invariance by homothetic transformation.
+#' It returns a collection of models, varying the number of clusters and the rank of the regression mean.
 #'
-#' @param Pi Parametre de proportion
-#' @param Rho Parametre initial de variance renormalisé
-#' @param mini Nombre minimal d'itérations dans l'algorithme EM
-#' @param maxi Nombre maximal d'itérations dans l'algorithme EM
-#' @param X Régresseurs
-#' @param Y Réponse
-#' @param eps Seuil pour accepter la convergence
-#' @param rank Vecteur des rangs possibles
+#' @param Pi An initialization for pi
+#' @param Rho An initialization for rho, the variance parameter
+#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
+#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
+#' @param X matrix of covariates (of size n*p)
+#' @param Y matrix of responses (of size n*m)
+#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
+#' @param rank vector of possible ranks
 #'
-#' @return A list ...
-#'   phi : parametre de moyenne renormalisé, calculé par l'EM
-#'   LLF : log vraisemblance associé à cet échantillon, pour les valeurs estimées des paramètres
+#' @return A list (corresponding to the model collection) defined by (phi,LLF):
+#'   phi : regression mean for each cluster
+#'   LLF : log likelihood with respect to the training set
 #'
 #' @export
 EMGrank <- function(Pi, Rho, mini, maxi, X, Y, eps, rank, fast = TRUE)