From: devijvee <emilie.devijver@univ-grenoble-alpes.fr>
Date: Sun, 9 Feb 2020 19:52:22 +0000 (+0100)
Subject: Comment each function in R
X-Git-Url: https://git.auder.net/doc/html/%7B%7B%20asset%28%27mixstore/css/base.css%27%29%20%7D%7D?a=commitdiff_plain;h=e9db79707709c10947e89756eb5655c0747a2a1d;p=valse.git

Comment each function in R
---

diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R
index c30b023..e393ec8 100644
--- a/pkg/R/EMGLLF.R
+++ b/pkg/R/EMGLLF.R
@@ -1,6 +1,9 @@
 #' EMGLLF
 #'
-#' Description de EMGLLF
+#' Run a generalized EM algorithm developped for mixture of Gaussian regression 
+#' models with variable selection by an extension of the Lasso estimator (regularization parameter lambda).
+#' Reparametrization is done to ensure invariance by homothetic transformation.
+#' It returns a collection of models, varying the number of clusters and the sparsity in the regression mean.
 #'
 #' @param phiInit an initialization for phi
 #' @param rhoInit an initialization for rho
@@ -14,13 +17,13 @@
 #' @param Y matrix of responses (of size n*m)
 #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
 #'
-#' @return A list ... phi,rho,pi,LLF,S,affec:
-#'   phi : parametre de moyenne renormalisé, calculé par l'EM
-#'   rho : parametre de variance renormalisé, calculé par l'EM
-#'   pi : parametre des proportions renormalisé, calculé par l'EM
-#'   LLF : log vraisemblance associée à cet échantillon, pour les valeurs estimées des paramètres
-#'   S : ...
-#'   affec : ...
+#' @return A list (corresponding to the model collection) defined by (phi,rho,pi,LLF,S,affec):
+#'   phi : regression mean for each cluster
+#'   rho : variance (homothetic) for each cluster
+#'   pi : proportion for each cluster
+#'   LLF : log likelihood with respect to the training set
+#'   S : selected variables indexes
+#'   affec : cluster affectation for each observation (of the training set)
 #'
 #' @export
 EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
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)
diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index eb6c590..f991f6d 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -1,6 +1,6 @@
 #' selectVariables
 #'
-#' It is a function which construct, for a given lambda, the sets of relevant variables.
+#' It is a function which constructs, for a given lambda, the sets for each cluster 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)