View source: R/bridgesampling.R
bayes_factor.brmsfit  R Documentation 
Compute Bayes factors from marginal likelihoods.
## S3 method for class 'brmsfit' bayes_factor(x1, x2, log = FALSE, ...)
x1 
A 
x2 
Another 
log 
Report Bayes factors on the logscale? 
... 
Additional arguments passed to

Computing the marginal likelihood requires samples
of all variables defined in Stan's parameters
block
to be saved. Otherwise bayes_factor
cannot be computed.
Thus, please set save_all_pars = TRUE
in the call to brm
,
if you are planning to apply bayes_factor
to your models.
The computation of Bayes factors based on bridge sampling requires
a lot more posterior samples than usual. A good conservative
rule of thumb is perhaps 10fold more samples (read: the default of 4000
samples may not be enough in many cases). If not enough posterior
samples are provided, the bridge sampling algorithm tends to be unstable,
leading to considerably different results each time it is run.
We thus recommend running bayes_factor
multiple times to check the stability of the results.
More details are provided under
bridgesampling::bayes_factor
.
bridge_sampler,
post_prob
## Not run: # model with the treatment effect fit1 < brm( count ~ zAge + zBase + Trt, data = epilepsy, family = negbinomial(), prior = prior(normal(0, 1), class = b), save_all_pars = TRUE ) summary(fit1) # model without the treatment effect fit2 < brm( count ~ zAge + zBase, data = epilepsy, family = negbinomial(), prior = prior(normal(0, 1), class = b), save_all_pars = TRUE ) summary(fit2) # compute the bayes factor bayes_factor(fit1, fit2) ## End(Not run)
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