From: Benjamin Auder Date: Wed, 26 May 2021 11:18:57 +0000 (+0200) Subject: Update to conform to CRAN rules (unfinished) X-Git-Url: https://git.auder.net/?a=commitdiff_plain;h=6382130f19d2de72fed32c91c5431caa6481dbf3;p=valse.git Update to conform to CRAN rules (unfinished) --- diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION index 13fee37..ed3eb2f 100644 --- a/pkg/DESCRIPTION +++ b/pkg/DESCRIPTION @@ -8,9 +8,10 @@ Description: Two methods are implemented to cluster data with finite mixture A low-rank constraint could be added, computed for the Lasso-Rank procedure. A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected using a model selection criterion - (slope heuristic, BIC or AIC). Details of the procedure are provided in 'Model- - based clustering for high-dimensional data. Application to functional data' by - Emilie Devijver, published in Advances in Data Analysis and Clustering (2016). + (slope heuristic, BIC or AIC). Details of the procedure are provided in + "Model-based clustering for high-dimensional data. Application to functional data" + by Emilie Devijver (2016) , + published in Advances in Data Analysis and Clustering. Author: Benjamin Auder [aut,cre], Emilie Devijver [aut], Benjamin Goehry [ctb] diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R index 10cb191..cf31c63 100644 --- a/pkg/R/initSmallEM.R +++ b/pkg/R/initSmallEM.R @@ -1,3 +1,5 @@ +#' initSmallEM +#' #' initialization of the EM algorithm #' #' @param k number of components @@ -8,6 +10,7 @@ #' @return a list with phiInit, rhoInit, piInit, gamInit #' #' @importFrom stats cutree dist hclust runif +#' #' @export initSmallEM <- function(k, X, Y, fast) { diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R index e3fd38e..0ef5f72 100644 --- a/pkg/R/plot_valse.R +++ b/pkg/R/plot_valse.R @@ -1,7 +1,8 @@ utils::globalVariables(c("Var1","Var2","X1","X2","value")) #, package="valse") + #' Plot #' -#' It is a function which plots relevant parameters +#' A function which plots relevant parameters. #' #' @param X matrix of covariates (of size n*p) #' @param Y matrix of responses (of size n*m) @@ -14,6 +15,8 @@ utils::globalVariables(c("Var1","Var2","X1","X2","value")) #, package="valse") #' @importFrom cowplot background_grid #' @importFrom reshape2 melt #' +#' @return No return value (only plotting). +#' #' @export plot_valse <- function(X, Y, model, comp = FALSE, k1 = NA, k2 = NA) {