% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-selection.r
\name{SimilarityMeasure}
\alias{SimilarityMeasure}
\title{Similarity of a set of clusterings to a reference}
\usage{
SimilarityMeasure(
  List,
  nrclusters = NULL,
  fusionsLog = TRUE,
  weightclust = TRUE,
  names = NULL
)
}
\arguments{
\item{List}{A label matrix (rows are clusterings, columns are objects) or a
list of clustering outputs to be aligned via \code{ReorderToReference}.}

\item{nrclusters}{Number of clusters used when \code{List} is a list. Default
\code{NULL}.}

\item{fusionsLog, weightclust}{Logical controls passed to
\code{ReorderToReference} when \code{List} is a list.}

\item{names}{Optional method names passed to \code{ReorderToReference}.}
}
\value{
A numeric vector of similarities in \code{[0, 1]}, one per row, where
the first entry is the reference compared to itself (always \code{1}).
}
\description{
Given a colour/label matrix (clusterings in rows, objects in columns) or a
list of clustering outputs, the proportion of objects sharing the label of
the first (reference) row is computed for every row. When a list is supplied
it is first aligned with \code{ReorderToReference}; supplying the label
matrix directly avoids that dependency.
}
\examples{
\dontrun{
M <- rbind(c(1, 1, 2, 2, 3, 3),
           c(1, 1, 2, 3, 3, 3),
           c(2, 2, 1, 1, 3, 3))
SimilarityMeasure(M)
}
}
\references{
Fodeh, S. J., Brandt, C., Luong, T. B., Haddad, A., Schultz, M., Murphy, T.
and Krauthammer, M. (2013). Complementary ensemble clustering of biomedical
data. Journal of Biomedical Informatics, 46(3), 436-443.
}
