R: Draws from the Posterior Predictive Distribution

posterior_predict.brmsfit {brms} | R Documentation

## Draws from the Posterior Predictive Distribution

### Description

Compute posterior draws 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 draws have higher variance than draws of the means of the posterior predictive distribution computed by `posterior_epred.brmsfit`. This is because the residual error is incorporated in `posterior_predict`. However, the estimated means of both methods averaged across draws should be very similar.

### Usage

    ## S3 method for class 'brmsfit'
    posterior_predict(
      object,
      newdata = NULL,
      re_formula = NULL,
      re.form = NULL,
      transform = NULL,
      resp = NULL,
      negative_rt = FALSE,
      ndraws = NULL,
      draw_ids = NULL,
      sort = FALSE,
      ntrys = 5,
      cores = NULL,
      ...
    )

### 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`.
`transform` | (Deprecated) A function or a character string naming a function to be applied on the predicted responses before summary statistics are computed.
`resp` | Optional names of response variables. If specified, predictions are performed only for the specified response variables.
`negative_rt` | Only relevant for Wiener diffusion models. A flag indicating whether response times of responses on the lower boundary should be returned as negative values. This allows to distinguish responses on the upper and lower boundary. Defaults to `FALSE`.
`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`).
`ntrys` | Parameter used in rejection sampling for truncated discrete models only (defaults to `5`). See Details for more information.
`cores` | Number of cores (defaults to `1`). On non-Windows systems, this argument can be set globally via the `mc.cores` option.
`...` | 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.

For truncated discrete models only: In the absence of any general algorithm to sample from truncated discrete distributions, rejection sampling is applied in this special case. This means that values are sampled until a value lies within the defined truncation boundaries. In practice, this procedure may be rather slow (especially in R). Thus, we try to do approximate rejection sampling by sampling each value `ntrys` times and then select a valid value. If all values are invalid, the closest boundary is used, instead. If there are more than a few of these pathological cases, a warning will occur suggesting to increase argument `ntrys`.

### Value

An `array` of predicted response values. In univariate models, the output is as an S x N matrix, where S is the number of posterior draws and N is the number of observations. 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(time | cens(censored) ~ age + sex + (1 + age || patient),
               data = kidney, family = "exponential", init = "0")

    ## predicted responses
    pp <- posterior_predict(fit)
    str(pp)

    ## predicted responses excluding the group-level effect of age
    pp <- posterior_predict(fit, re_formula = ~ (1 | patient))
    str(pp)

    ## predicted responses of patient 1 for new data
    newdata <- data.frame(
      sex = factor(c("male", "female")),
      age = c(20, 50),
      patient = c(1, 1)
    )
    pp <- posterior_predict(fit, newdata = newdata)
    str(pp)

    ## End(Not run)

[Package brms version 2.17.0 Index]