brms (version 1.10.2)

post_prob.brmsfit: Posterior Model Probabilities from Marginal Likelihoods

Description

Compute posterior model probabilities from marginal likelihoods. The brmsfit method is just a thin wrapper around the corresponding method for bridge objects.

Usage

# S3 method for brmsfit
post_prob(x, ..., prior_prob = NULL, model_names = NULL,
  bs_args = list())

Arguments

x

A brmsfit object.

...

More brmsfit objects.

prior_prob

Numeric vector with prior model probabilities. If omitted, a uniform prior is used (i.e., all models are equally likely a priori). The default NULL corresponds to equal prior model weights.

model_names

If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.

bs_args

A list of additional arguments passed to bridge_sampler.

Details

Computing the marginal likelihood requires samples of all variables defined in Stan's parameters block to be saved. Otherwise post_prob cannot be computed. Thus, please set save_all_pars = TRUE in the call to brm, if you are planning to apply post_prob to your models.

More details are provided under post_prob.

See Also

bridge_sampler, bayes_factor

Examples

# NOT RUN {
# model with the treatment effect
fit1 <- brm(
  count ~ log_Age_c + log_Base4_c + Trt_c,
  data = epilepsy, family = negbinomial(), 
  prior = prior(normal(0, 1), class = b),
  save_all_pars = TRUE
)
summary(fit1)

# model without the treatent effect
fit2 <- brm(
  count ~ log_Age_c + log_Base4_c,
  data = epilepsy, family = negbinomial(), 
  prior = prior(normal(0, 1), class = b),
  save_all_pars = TRUE
)
summary(fit2)

# compute the posterior model probabilities
post_prob(fit1, fit2)

# specify prior model probabilities
post_prob(fit1, fit2, prior_prob = c(0.8, 0.2))
# }
# NOT RUN {
# }