Based on the result list from glmBayesMfp
, sample from the
Bayesian model average (BMA) over the models contained in this list.
1 2  sampleBma(object, mcmc = McmcOptions(), postProbs = posteriors(object),
nMargLikSamples = NULL, verbose = TRUE, ...)

object 
valid 
mcmc 
MCMC options object with class 
postProbs 
vector of posterior probabilites (will be normalized within
the function) for the weighting of the models in 
nMargLikSamples 
If this is non 
verbose 
should information on computation progress be given? (default) 
... 
optional further arguments already available for sampling from
a single model: 
If TBF methodology is used (which is specified within the glmBayesMfp
object), then Monte Carlo (MC) sampling is used. If the fully Bayesian,
generalized hyperg prior methodology is used, then the sampling is based on
MCMC. Therefore, instead of only specifying the required number of samples
and the model probabilities, one also needs to specify the burnin length
and the thinning parameter, which will be applied to every model from which
at least one sample is included in the average. Alternatively, you can ask
for MCMC marginal likelihood estimates for all models in the list. Then at
least nMargLikSamples
will be produced for each model, whether
included in the BMA sample or not.
The result is a list with the following elements:
data frame containing the result from the
as.data.frame
function, and in addition BMA probabilities,
BMA frequencies in the sample, acceptance ratios of the MCMC
runs and optionally marginal likelihood estimates / standard
errors.
an object of S4 class GlmBayesMfpSamples
containing the samples from the BMA.
Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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