brms: residuals.brmsfit – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

residuals.brmsfit

Posterior Samples of Residuals/Predictive Errors

Description

This method is an alias of predictive_error.brmsfit with additional arguments for obtaining summaries of the computed samples.

Usage

## S3 method for class 'brmsfit'
residuals(
  object,
  newdata = NULL,
  re_formula = NULL,
  method = "pp_expect",
  type = c("ordinary", "pearson"),
  resp = NULL,
  nsamples = NULL,
  subset = NULL,
  sort = FALSE,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

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.
`method`	Method use to obtain predictions. Either `"pp_expect"` (the default) or `"posterior_predict"`. Using `"posterior_predict"` is recommended but `"pp_expect"` is the current default for reasons of backwards compatibility.
`type`	The type of the residuals, either `"ordinary"` or `"pearson"`. More information is provided under 'Details'.
`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`).
`summary`	Should summary statistics be returned instead of the raw values? Default is `TRUE`..
`robust`	If `FALSE` (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If `TRUE`, the median and the median absolute deviation (MAD) are applied instead. Only used if `summary` is `TRUE`.
`probs`	The percentiles to be computed by the `quantile` function. Only used if `summary` is `TRUE`.
`...`	Further arguments passed to `prepare_predictions` that control several aspects of data validation and prediction.

Details

Residuals of type 'ordinary' are of the form R = Y - Yrep, where Y is the observed and Yrep is the predicted response. Residuals of type pearson are of the form R = (Y - Yrep) / SD(Y), where SD(Y) is an estimation of the standard deviation of Y.

Value

An array of predictive error/residual samples. If summary = FALSE the output resembles those of predictive_error.brmsfit. If summary = TRUE the output is an N x E matrix, where N is the number of observations and E denotes the summary statistics computed from the samples.

Examples

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

## extract residuals/predictive errors
res <- residuals(fit)
head(res)

## 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