% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distance.R
\name{Distance}
\alias{Distance}
\alias{Distance_v2}
\title{Compute a distance matrix}
\usage{
Distance(
  Data,
  distmeasure = c("tanimoto", "jaccard", "euclidean", "hamming", "cont tanimoto",
    "MCA_coord", "gower", "chi.squared", "cosine"),
  normalize = NULL,
  method = NULL
)

Distance_v2(Data, distmeasure = "tanimoto", normalize = NULL)
}
\arguments{
\item{Data}{A data matrix; rows are the objects.}

\item{distmeasure}{One of \code{"tanimoto"}, \code{"jaccard"},
\code{"euclidean"} (Gower), \code{"hamming"}, \code{"cont tanimoto"},
\code{"MCA_coord"}, \code{"gower"}, \code{"chi.squared"}, \code{"cosine"}.}

\item{normalize}{Optional normalisation method (see \code{\link{Normalization}}),
applied for the \code{"euclidean"} / \code{"cont tanimoto"} measures.}

\item{method}{Ignored; retained for backward compatibility.}
}
\value{
A symmetric numeric distance matrix.
}
\description{
Dispatcher computing an object-by-object distance matrix under a range of
measures suitable for continuous, binary and mixed-type data.
}
\details{
\code{"cosine"}, \code{"chi.squared"} and \code{"MCA_coord"} require
the suggested packages \pkg{lsa}, \pkg{analogue} and \pkg{FactoMineR}
respectively.
}
\examples{
m <- matrix(rbinom(40, 1, 0.5), nrow = 8)
D <- Distance(m, distmeasure = "tanimoto")
dim(D)
}
