From: devijvee Date: Sun, 9 Feb 2020 19:52:22 +0000 (+0100) Subject: Comment each function in R X-Git-Url: https://git.auder.net/variants/Chakart/css/doc/pieces/img/cross.svg?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)