brms: pp_average.brmsfit – R documentation

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pp_average.brmsfit

Posterior predictive samples averaged across models

Description

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

pp_average(x, ...)

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), 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.
`method`	Method used to obtain predictions to average over. Should be one of `"posterior_predict"` (default), `"pp_expect"`, or `"predictive_error"`.
`nsamples`	Total number of posterior samples to use.
`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.

Value

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

Examples

## 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 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)

## End(Not run)

brms

Bayesian Regression Models using 'Stan'

v2.15.0

GPL-2

Authors

Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]

Initial release

2021-03-10