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\name{emControl}
\alias{emControl}
\title{Set control values for use with the EM algorithm}
\description{
Supplies a list of values including tolerances for singularity and
convergence assessment, for use functions involving EM within \emph{MCLUST}.
}
\usage{
emControl(eps, tol, itmax, equalPro)
}
\arguments{
\item{eps}{
A scalar tolerance associated with deciding when to terminate
computations due to computational singularity in
covariances. Smaller values of \code{eps} allow computations to
proceed nearer to singularity. The default is the relative machine
precision \code{.Machine$double.eps}, which is approximately
\eqn{2e-16} on IEEE-compliant machines.
}
\item{tol}{
A vector of length two giving relative convergence tolerances for the
log-likelihood and for parameter convergence in the inner loop for models
with iterative M-step ("VEI", "VEE", "EVE", "VVE", "VEV"), respectively.
The default is \code{c(1.e-5, sqrt(.Machine$double.eps))}.
If only one number is supplied, it is used as the tolerance
for the outer iterations and the tolerance for the inner
iterations is as in the default.
}
\item{itmax}{
A vector of length two giving integer limits on the number of EM
iterations and on the number of iterations in the inner loop for
models with iterative M-step ("VEI", "VEE", "EVE", "VVE", "VEV"),
respectively. The default is
\code{c(.Machine$integer.max, .Machine$integer.max)}
allowing termination to be completely governed by \code{tol}.
If only one number is supplied, it is used as the iteration
limit for the outer iteration only.
}
\item{equalPro}{
Logical variable indicating whether or not the mixing proportions are
equal in the model. Default: \code{equalPro = FALSE}.
}
}
\value{
A named list in which the names are the names of the arguments
and the values are the values supplied to the arguments.
}
\details{
\code{emControl} is provided for assigning values and defaults
for EM within \emph{MCLUST}.
}
\seealso{
\code{\link{em}},
\code{\link{estep}},
\code{\link{me}},
\code{\link{mstep}},
\code{\link{mclustBIC}}
}
\examples{
irisBIC <- mclustBIC(iris[,-5], control = emControl(tol = 1.e-6))
summary(irisBIC, iris[,-5])
}
\keyword{cluster}
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