https://github.com/cran/CluMix
Revision a006880878209b1a96d9cdde0332d96fa86036af authored by Manuela Hummel on 03 June 2016, 18:47:22 UTC, committed by cran-robot on 03 June 2016, 18:47:22 UTC
1 parent 0d1b46c
Tip revision: a006880878209b1a96d9cdde0332d96fa86036af authored by Manuela Hummel on 03 June 2016, 18:47:22 UTC
version 1.1
version 1.1
Tip revision: a006880
dendro.variables.Rd
\name{dendro.variables}
\alias{dendro.variables}
\title{Variables dendrogram}
\description{Get dendrogram for variables of mixed types}
\usage{
dendro.variables(data, dist.variables.method = c("associationMeasures", "ClustOfVar"),
associationFun = association, check.psd = TRUE)
}
\arguments{
\item{data}{data frame with variables of interest}
\item{dist.variables.method}{If \code{"associationMeasures"}, similarities between variables are assessed by combination of appropriate measures of association for different pairs of data types. Then a dendrogram is derived by standard hierarchical clustering (\code{\link[stats]{hclust}} with default options). If \code{"ClustOfVar"}, variables are clustered by the \code{\link{ClustOfVar}{ClustOfVar}} approach.}
\item{associationFun}{By default, appropriate association measures are chosen for each pair of variables, see \code{\link{association}} for details. But the user can also define a function that for any two variables calculates a similarity measure. Ignored if \code{dist.variables.method = "ClustOfVar"}}
\item{check.psd}{If \code{TRUE}, it is checked if the variable's similarity matrix S is positive semi-definite (p.s.d.), and if not it is transformed to a p.s.d. one by \code{\link[Matrix]{nearPD}}, see \code{\link{dist.variables}} for details. Ignored if \code{dist.variables.method = "ClustOfVar"}}
}
\details{Clustering of variables can either be done similarity-based or by the ClustOfVar approach, which uses principal components analysis for mixed data.}
\value{An object of class \code{\link[stats]{dendrogram}}}
\references{
Chavent M, Kuentz-Simonet V, Liquet B, Saracco J (2012). ClustOfVar: An R Package for the Clustering of Variables. Journal of Statistical Software, 50:1-16.
}
\author{Manuela Hummel}
%\note{
%% ~~further notes~~
%}
\seealso{\code{\link{association}}, \code{\link{similarity.variables}}, \code{\link{dist.variables}}, \code{\link{dendro.subjects}}, \code{\link{mix.heatmap}}}
\examples{
data(mixdata)
dend <- dendro.variables(mixdata)
plot(dend)
}
\keyword{ cluster }
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