R: Posterior Model Probabilities from Marginal Likelihoods

post_prob.brmsfit {brms} | R Documentation

## 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 class 'brmsfit'
    post_prob(x, ..., prior_prob = NULL, model_names = NULL)

### Arguments

`x` | A `brmsfit` object.
`...` | More `brmsfit` objects or further arguments passed to the underlying post-processing functions. In particular, see `prepare_predictions` for further supported arguments.
`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.

### 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.

The computation of model probabilities based on bridge sampling requires a lot more posterior samples than usual. A good conservative rule of thump 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 `post_prob` multiple times to check the stability of the results.

More details are provided under `bridgesampling::post_prob`.

### See Also

`bridge_sampler, bayes_factor`

### 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 treatent 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 posterior model probabilities
    post_prob(fit1, fit2)

    # specify prior model probabilities
    post_prob(fit1, fit2, prior_prob = c(0.8, 0.2))

    ## End(Not run)

[Package brms version 2.17.0 Index]