posterior_epred.brmsfit: Draws from the Expected Value of the Posterior Predictive...

In brms: Bayesian Regression Models using 'Stan'

Draws from the Expected Value of the Posterior Predictive Distribution

Description

Compute posterior draws of the expected value of the posterior predictive distribution. Can be performed for the data used to fit the model (posterior predictive checks) or for new data. By definition, these predictions have smaller variance than the posterior predictions performed by the posterior_predict.brmsfit method. This is because only the uncertainty in the expected value of the posterior predictive distribution is incorporated in the draws computed by posterior_epred while the residual error is ignored there. However, the estimated means of both methods averaged across draws should be very similar.

Usage

## S3 method for class 'brmsfit'
posterior_epred(
  object,
  newdata = NULL,
  re_formula = NULL,
  re.form = NULL,
  resp = NULL,
  dpar = NULL,
  nlpar = NULL,
  ndraws = NULL,
  draw_ids = NULL,
  sort = FALSE,
  ...
)

Arguments

object	An object of class brmsfit.
newdata	An optional data.frame for which to evaluate predictions. If NULL (default), the original data of the model is used. NA values within factors (excluding grouping variables) are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding. NA values within grouping variables are treated as a new level.
re_formula	formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA or ~0, include no group-level effects.
re.form	Alias of re_formula.
resp	Optional names of response variables. If specified, predictions are performed only for the specified response variables.
dpar	Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned.
nlpar	Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned.
ndraws	Positive integer indicating how many posterior draws should be used. If NULL (the default) all draws are used. Ignored if draw_ids is not NULL.
draw_ids	An integer vector specifying the posterior draws to be used. If NULL (the default), all draws are used.
sort	Logical. Only relevant for time series models. Indicating whether to return predicted values in the original order (FALSE; default) or in the order of the time series (TRUE).
...	Further arguments passed to prepare_predictions that control several aspects of data validation and prediction.

Details

NA values within factors in newdata, are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding.

In multilevel models, it is possible to allow new levels of grouping factors to be used in the predictions. This can be controlled via argument allow_new_levels. New levels can be sampled in multiple ways, which can be controlled via argument sample_new_levels. Both of these arguments are documented in prepare_predictions along with several other useful arguments to control specific aspects of the predictions.

Value

An array of draws. For categorical and ordinal models, the output is an S x N x C array. Otherwise, the output is an S x N matrix, where S is the number of posterior draws, N is the number of observations, and C is the number of categories. In multivariate models, an additional dimension is added to the output which indexes along the different response variables.

Examples

## Not run: 
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
           data = inhaler)

## compute expected predictions
ppe <- posterior_epred(fit)
str(ppe)

## End(Not run)

brms documentation built on Sept. 23, 2024, 5:08 p.m.