as_mixed_posteriors {BayesTools} | R Documentation |
Export BayesTools JAGS model posterior distribution as model-average posterior distributions via mix_posteriors
Description
Creates a model-averages posterior distributions on a single model that allows mimicking the mix_posteriors functionality. This function is useful when the model-averaged ensemble is based on prior_spike_and_slab or prior_mixture priors - the model-averaging is done within the model.
Usage
as_mixed_posteriors(
model,
parameters,
conditional = NULL,
conditional_rule = "AND",
force_plots = FALSE
)
Arguments
model |
model fit via the JAGS_fit function |
parameters |
vector of parameters names for which inference should be drawn |
conditional |
a character vector of parameters to be conditioned on |
conditional_rule |
a character string specifying the rule for conditioning. Either "AND" or "OR". Defaults to "AND". |
force_plots |
temporal argument allowing to generate conditional posterior samples suitable for prior and posterior plots. Only available when conditioning on a single parameter. |
Value
as_mix_posteriors
returns a named list of mixed posterior
distributions (either a vector of matrix).