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\name{decomp2sigma}
\alias{decomp2sigma}
\title{
Convert mixture component covariances to matrix form
}
\description{
Converts covariances from a parameterization by eigenvalue decomposition
or cholesky factorization to representation as a 3-D array.
}
\usage{
decomp2sigma(d, G, scale, shape, orientation, \dots)
}
\arguments{
\item{d}{
The dimension of the data.
}
\item{G}{
The number of components in the mixture model.
}
\item{scale}{
Either a \emph{G}-vector giving the scale of the covariance (the
\emph{d}th root of its determinant) for each component in the
mixture model, or a single numeric value if the scale is the same
for each component.
}
\item{shape}{
Either a \emph{G} by \emph{d} matrix in which the \emph{k}th column
is the shape of the covariance matrix (normalized to have
determinant 1) for the \emph{k}th component, or a \emph{d}-vector
giving a common shape for all components.
}
\item{orientation}{
Either a \emph{d} by \emph{d} by \emph{G} array whose \code{[,,k]}th
entry is the orthonomal matrix whose columns are the eigenvectors
of the covariance matrix of the \emph{k}th component, or a
\emph{d} by \emph{d} orthonormal matrix if the mixture components have a common
orientation. The \code{orientation} component of \code{decomp} can
be omitted in spherical and diagonal models, for which the principal
components are parallel to the coordinate axes so that the
orientation matrix is the identity.
}
\item{\dots}{
Catches unused arguments from an indirect or list call via \code{do.call}.
}
}
\value{
A 3-D array whose \code{[,,k]}th component is the
covariance matrix of the \emph{k}th component in an MVN mixture model.
}
\seealso{
\code{\link{sigma2decomp}}
}
\examples{
meEst <- meVEV(iris[,-5], unmap(iris[,5]))
names(meEst)
meEst$parameters$variance
dec <- meEst$parameters$variance
decomp2sigma(d=dec$d, G=dec$G, shape=dec$shape, scale=dec$scale,
orientation = dec$orientation)
\donttest{
do.call("decomp2sigma", dec) ## alternative call
}
}
\keyword{cluster}
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