brms: predictive_error.brmsfit – R documentation

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predictive_error.brmsfit

Posterior Samples of Predictive Errors

Description

Compute posterior samples of predictive errors, that is, observed minus predicted responses. Can be performed for the data used to fit the model (posterior predictive checks) or for new data.

Usage

## S3 method for class 'brmsfit'
predictive_error(
  object,
  newdata = NULL,
  re_formula = NULL,
  re.form = NULL,
  resp = 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.
`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.

Value

An S x N array of predictive error samples, where S is the number of posterior samples and N is the number of observations.

Examples

## Not run: 
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject), 
           data = inhaler, cores = 2)

## extract predictive errors
pe <- predictive_error(fit)
str(pe)

## 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