posterior_average.brmsfit: Posterior draws of parameters averaged across models

Description

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

Usage

# S3 method for brmsfit
posterior_average(
  x,
  ...,
  variable = NULL,
  pars = NULL,
  weights = "stacking",
  ndraws = NULL,
  nsamples = NULL,
  missing = NULL,
  model_names = NULL,
  control = list(),
  seed = NULL
)
posterior_average(x, ...)

Value

A data.frame of posterior draws.

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.
variable: Names of variables (parameters) for which to average across models. Only those variables can be averaged that appear in every model. Defaults to all overlapping variables.
pars: Deprecated alias of variable.
weights: 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.
ndraws: Total number of posterior draws to use.
nsamples: Deprecated alias of ndraws.
missing: An optional numeric value or a named list of numeric values to use if a model does not contain a variable for which posterior draws should be averaged. Defaults to NULL, in which case only those variables can be averaged that are present in all of the models.
model_names: If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.
control: Optional list of further arguments passed to the function specified in weights.
seed: A single numeric value passed to set.seed to make results reproducible.

Details

Weights are computed with the model_weights method.

Examples

Run this code

if (FALSE) {
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)
summary(fit1)

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

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