R: Bayes Factors from Marginal Likelihoods

bayes_factor.brmsfit {brms} | R Documentation

## Bayes Factors from Marginal Likelihoods

### Description

Compute Bayes factors from marginal likelihoods.

### Usage

    ## S3 method for class 'brmsfit'
    bayes_factor(x1, x2, log = FALSE, ...)

### Arguments

`x1` | A `brmsfit` object
`x2` | Another `brmsfit` object based on the same responses.
`log` | Report Bayes factors on the log-scale?
`...` | 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 `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 10-fold 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`.

### See Also

`bridge_sampler, post_prob`

### Examples

    ## 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)

[Package brms version 2.16.3 Index]