posterior_average.brmsfit {brms}R Documentation

Posterior samples of parameters averaged across models


Extract posterior samples of parameters averaged across models. Weighting can be done in various ways, for instance using Akaike weights based on information criteria or marginal likelihoods.


## S3 method for class 'brmsfit'
  pars = NULL,
  weights = "stacking",
  nsamples = NULL,
  missing = NULL,
  model_names = NULL,
  control = list(),
  seed = NULL

posterior_average(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.


Names of parameters for which to average across models. Only those parameters can be averaged that appear in every model. Defaults to all overlapping parameters.


Name of the criterion to compute weights from. Should be one of "loo", "waic", "kfold", "stacking" (current default), or "bma", "pseudobma", For the former three options, Akaike weights will be computed based on the information criterion values returned by the respective methods. For "stacking" and "pseudobma", method loo_model_weights will be used to obtain weights. For "bma", method post_prob will be used to compute Bayesian model averaging weights based on log marginal likelihood values (make sure to specify reasonable priors in this case). For some methods, weights may also be a numeric vector of pre-specified weights.


Total number of posterior samples to use.


An optional numeric value or a named list of numeric values to use if a model does not contain a parameter for which posterior samples should be averaged. Defaults to NULL, in which case only those parameters can be averaged that are present in all of the models.


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


Optional list of further arguments passed to the function specified in weights.


A single numeric value passed to set.seed to make results reproducible.


Weights are computed with the model_weights method.


A data.frame of posterior samples. Samples are rows and parameters are columns.

See Also

model_weights, pp_average


## Not run: 
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)

# model without 'treat' as predictor
fit2 <- brm(rating ~ period + carry, data = inhaler)

# compute model-averaged posteriors of overlapping parameters
posterior_average(fit1, fit2, weights = "waic")

## End(Not run)

[Package brms version 2.15.0 Index]