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\name{unmap}
\alias{unmap}
\title{
Indicator Variables given Classification
}
\description{
Converts a classification into a matrix of indicator variables.
}
\usage{
unmap(classification, groups=NULL, noise=NULL, \dots)
}
\arguments{
\item{classification}{
A numeric or character vector. Typically the distinct entries of this
vector would represent a classification of observations in a data set.
}
\item{groups}{
A numeric or character vector indicating the groups from which
\code{classification} is drawn. If not supplied, the default
is to assumed to be the unique entries of classification.
}
\item{noise}{
A single numeric or character value used to indicate the value of
\code{groups} corresponding to noise.
}
\item{\dots }{
Catches unused arguments in indirect or list calls via \code{do.call}.
}
}
\value{
An \emph{n} by \emph{m} matrix of \emph{(0,1)} indicator variables,
where \emph{n} is the length of \code{classification} and \emph{m} is
the number of unique values or symbols in \code{classification}.
Columns are labeled by the unique values in \code{classification},
and the \code{[i,j]}th entry is \emph{1} if \code{classification[i]}
is the \emph{j}th unique value or symbol in sorted order
\code{classification}.
If a \code{noise} value of symbol is designated, the corresponding indicator
variables are relocated to the last column of the matrix.
}
\seealso{
\code{\link{map}},
\code{\link{estep}},
\code{\link{me}}
}
\examples{
z <- unmap(iris[,5])
z[1:5, ]
emEst <- me(modelName = "VVV", data = iris[,-5], z = z)
emEst$z[1:5,]
map(emEst$z)
}
\keyword{cluster}
|
