`R/bridgesampling.R`

`post_prob.brmsfit.Rd`

Compute posterior model probabilities from marginal likelihoods.
The `brmsfit`

method is just a thin wrapper around
the corresponding method for `bridge`

objects.

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

- 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.

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`

.

```
if (FALSE) {
# 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))
}
```