pp_average.brmsfit: Posterior predictive draws averaged across models

Description

Compute posterior predictive draws 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
pp_average(
  x,
  ...,
  weights = "stacking",
  method = "posterior_predict",
  ndraws = NULL,
  nsamples = NULL,
  summary = TRUE,
  probs = c(0.025, 0.975),
  robust = FALSE,
  model_names = NULL,
  control = list(),
  seed = NULL
)
pp_average(x, ...)

Value

Same as the output of the method specified in argument method.

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.
weights: Name of the criterion to compute weights from. Should be one of "loo", "waic", "kfold", "stacking" (current default), "bma", or "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.
method: Method used to obtain predictions to average over. Should be one of "posterior_predict" (default), "posterior_epred", "posterior_linpred" or "predictive_error".
ndraws: Total number of posterior draws to use.
nsamples: Deprecated alias of ndraws.
summary: Should summary statistics (i.e. means, sds, and 95% intervals) be returned instead of the raw values? Default is TRUE.
probs: The percentiles to be computed by the quantile function. Only used if summary is TRUE.
robust: If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.
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 predicted values
(df <- unique(inhaler[, c("treat", "period", "carry")]))
pp_average(fit1, fit2, newdata = df)

# compute model-averaged fitted values
pp_average(fit1, fit2, method = "fitted", newdata = df)
}