\name{dist.variables}
\alias{dist.variables}
\title{Distance matrix for variables}

\description{Get distance matrix for variables of mixed types}

\usage{
dist.variables(data, method = c("associationMeasures", "distcor"), 
associationFun = association, check.psd = TRUE)
}

\arguments{
  \item{data}{data frame with variables of interest}
    \item{method}{method to calculate distances: combination of association measures (\code{'associationMeasures'}) or distance correlation (\code{'distcor'})}
  \item{associationFun}{only applies if \code{method = 'associationMeasures'}: 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.}
  \item{check.psd}{only applies if \code{method = 'associationMeasures'}: 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}}.}
}

\details{A distance matrix for variables can be derived by combining different measures of association or by a distance correlation approach. For the association measure approach, for each pair of variables, similarity coefficients s_ij are calculated, see \code{\link{association}} for details. Distances are then calculated as d_ij = sqrt(1 - s_ij). If the similarity matrix is (made) positive semi-definite, those distances have metric properties (Gower, 1971), which means for instance that the triangular inequality holds. 
The distance correlation approach uses generalized distance correlation based on Gower's similarity coefficient between sample elements. The distance is then defined by 1 minus the square root of the distance correlation matrix.}

\value{An object of class \code{\link[stats]{dist}}}

\references{
Hummel M, Edelmann D, Kopp-Schneider A. Clustering of samples and variables with mixed-type data. Submitted.

Gower J (1971). A general coefficient of similarity and some of its properties. Biometrics, 27:857-871.

Szekely GJ, Rizzo ML, Bakirov NK (2007). Measuring and testing dependence by correlation of distances. The Annals of Statistics, 35.6:2769-2794.

Lyons R (2013). Distance covariance in metric spaces. The Annals of Probability, 41.5:3284-3305.
}

\author{Manuela Hummel, Dominic Edelmann}

%\note{
%}

\seealso{\code{\link{association}}, \code{\link{similarity.variables}}, \code{\link{dendro.variables}}, \code{\link{dist.subjects}}, \code{\link{mix.heatmap}}}

\examples{
data(mixdata)

D1 <- dist.variables(mixdata, method="association")
D2 <- dist.variables(mixdata, method="distcor")
}

\keyword{ math }
\keyword{ cluster }