posteriorTP | R Documentation |
This is a wrapper of coda.samples
which in turn, is a
wrapper of jags.samples
. It extracts random samples from
the posterior distribution of the parameters of a jags model.
posteriorTP( model, variable.names = c("TP", "muDeltaN"), n.iter = 10000, burnin = NULL, thin = 10, quiet = FALSE, ... )
model |
a JAGS model object returned by any of functions
|
variable.names |
vector of characters giving the names of variables to be monitored. |
n.iter |
integer defining the number of iterations. By default is 10000 |
burnin |
number of iterations discarded as burn in. |
thin |
thinning interval to get posterior samples. |
quiet |
logical value to indicate whether messages generated during posterior sampling will be suppressed, as well as the progress bar. |
... |
additional arguments passed to |
mcmc.list object containing posterior samples of the Bayesian model.
## Not run: isotopeData <- generateTPData() model.string <- jagsBayesianModel() model <- TPmodel(data = isotopeData, model.string = model.string, n.adapt = 500) posterior.samples <- posteriorTP(model, n.iter = 500) ## End(Not run)
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