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bsts.options.Rd
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bsts.options.Rd
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\name{bsts.options.Rd}
\alias{BstsOptions}
\title{Bsts Model Options}
\Rdversion{1.0}
\description{
Rarely used modeling options for bsts models.
}
\usage{
BstsOptions(save.state.contributions = TRUE,
save.prediction.errors = TRUE,
bma.method = c("SSVS", "ODA"),
oda.options = list(
fallback.probability = 0.0,
eigenvalue.fudge.factor = 0.01),
timeout.seconds = Inf,
save.full.state = FALSE)
}
\arguments{
\item{save.state.contributions}{Logical. If \code{TRUE} then a 3-way
array named \code{state.contributions} will be stored in the
returned object. The indices correspond to MCMC iteration, state
model number, and time. Setting \code{save.state.contributions} to
\code{FALSE} yields a smaller object, but \code{plot} will not be
able to plot the the "state", "components", or "residuals" for the
fitted model.}
\item{save.prediction.errors}{Logical. If \code{TRUE} then a matrix
named \code{one.step.prediction.errors} will be saved as part of the
model object. The rows of the matrix represent MCMC iterations, and
the columns represent time. The matrix entries are the
one-step-ahead prediction errors from the Kalman filter. }
\item{bma.method}{If the model contains a regression component, this
argument specifies the method to use for Bayesian model averaging.
"SSVS" is stochastic search variable selection, which is the classic
approach from George and McCulloch (1997). "ODA" is orthoganal data
augmentation, from Ghosh and Clyde (2011). It adds a set of latent
observations that make the \eqn{X^TX}{X'X} matrix diagonal, vastly
simplifying complete data MCMC for model selection.}
\item{oda.options}{If bma.method == "ODA" then these are some options
for fine tuning the ODA algorithm.
\itemize{
\item \code{fallback.probability}: Each MCMC iteration will use
SSVS instead of ODA with this probability. In cases where
the latent data have high leverage, ODA mixing can suffer.
Mixing in a few SSVS steps can help keep an errant algorithm
on track.
\item \code{eigenvalue.fudge.factor}: The latent X's will be
chosen so that the complete data \eqn{X^TX}{X'X} matrix (after
scaling) is a constant diagonal matrix equal to the largest
eigenvalue of the observed (scaled) \eqn{X^TX}{X'X} times (1 +
eigenvalue.fudge.factor). This should be a small positive number.
} }
\item{timeout.seconds}{The number of seconds that sampler will be
allowed to run. If the timeout is exceeded the returned object will
be truncated to the final draw that took place before the timeout
occurred, as if that had been the requested number of iterations.}
\item{save.full.state}{Logical. If \code{TRUE} then the full
distribution of the state vector will be preserved. It will be
stored in the model under the name \code{full.state}, which is a
3-way array with dimenions corresponding to MCMC iteration, state
dimension, and time.}
}
\value{
The arguments are checked to make sure they have legal types and
values, then a list is returned containing the arguments.
}
\author{
Steven L. Scott \email{[email protected]}
}
\keyword{chron}