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 brmsfit
posterior_average(
x,
...,
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).
Some method, 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.
A data.frame of posterior samples. Samples are rows
and parameters are columns.
Weights are computed with the model_weights method.
# NOT RUN {
# 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")
# }
# NOT RUN {
# }
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