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)Arguments
save.state.contributions
Logical. If TRUE then a 3-way
array named state.contributions will be stored in the
returned object. The indices correspond to MCMC iteration, state
model number, and time. Setting save.state.contributions to
FALSE yields a smaller object, but plot will not be
able to plot the the "state", "components", or "residuals" for the
fitted model.
save.prediction.errors
Logical. If TRUE then a matrix
named 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.
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 \(X^TX\) matrix diagonal, vastly
simplifying complete data MCMC for model selection.
oda.options
If bma.method == "ODA" then these are some options
for fine tuning the ODA algorithm.
-
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.
-
eigenvalue.fudge.factor: The latent X's will be
chosen so that the complete data \(X^TX\) matrix (after
scaling) is a constant diagonal matrix equal to the largest
eigenvalue of the observed (scaled) \(X^TX\) times (1 +
eigenvalue.fudge.factor). This should be a small positive number.
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.