R: Expected Values of the Posterior Predictive Distribution

posterior_epred.brmsfit {brms} | R Documentation

## Expected Values of the Posterior Predictive Distribution

### Description

Compute posterior draws 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 draws computed by `posterior_epred` while any residual error is ignored. 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 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.
`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 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 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)

[Package brms version 2.16.1 Index]