posterior_epred.brmsfit {brms}R Documentation

Expected Values of the Posterior Predictive Distribution

Description

Compute posterior samples of the expected value/mean 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 mean is incorporated in the samples computed by posterior_epred while any residual error is ignored. However, the estimated means of both methods averaged across samples 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,
  nsamples = NULL,
  subset = 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 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.

re_formula

formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA, 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.

nsamples

Positive integer indicating how many posterior samples should be used. If NULL (the default) all samples are used. Ignored if subset is not NULL.

subset

A numeric vector specifying the posterior samples to be used. If NULL (the default), all samples 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 predicted mean response values. 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 samples, 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)


[Package brms version 2.15.0 Index]