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\name{adapt}
\alias{adapt}
\title{Adaptive phase for JAGS models}
\description{
Update the model in adaptive mode.
}
\usage{
adapt(object, n.iter, end.adaptation=TRUE, \dots)
}
\arguments{
\item{object}{a \code{jags} model object}
\item{n.iter}{length of the adaptive phase}
\item{end.adaptation}{logical flag. If \code{TRUE} then adaptive
mode will be automatically on exit.
\item{\ldots}{additional arguments to the update method}
}
\value{
Returns \code{TRUE} if all the samplers in the model have successfully
adapted their behaviour to optimum performance and \code{FALSE}
otherwise.
}
\details{
This function is not normally called by the user. It is called by the
\code{jags.model} function when the model object is created.
When a JAGS model is compiled, it may require an initial sampling
phase during which the samplers adapt their behaviour to maximize
their efficiency (e.g. a Metropolis-Hastings random walk algorithm may
change its step size). The sequence of samples generated during this
adaptive phase is not a Markov chain, and therefore may not be used
for posterior inference on the model.
The \code{adapt} function updates the model for \code{n.iter}
iterations in adaptive mode. Then each sampler reports whether it
has acheived optimal performance (e.g. whether the rejection rate of a
Metropolis-Hasting sampler is close to the theoretical optimum). If
any sampler reports failure of this test then \code{adapt} returns
\code{FALSE}.
By default, the \code{adapt()} function turns off adaptive mode on
exit. Further calls to \code{adapt()} do nothing. The model may be
maintained in adaptive mode with the option \code{end.adaptation =
FALSE} so that successive calls to \code{adapt()} may be made until
adaptation is satisfactory.
}
\author{Martyn Plummer}
\keyword{models}

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