residuals.brmsfit: Posterior Draws of Residuals/Predictive Errors

In brms: Bayesian Regression Models using 'Stan'

Posterior Draws of Residuals/Predictive Errors

Description

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

Usage

## S3 method for class 'brmsfit'
residuals(
  object,
  newdata = NULL,
  re_formula = NULL,
  method = "posterior_predict",
  type = c("ordinary", "pearson"),
  resp = NULL,
  ndraws = NULL,
  draw_ids = 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 (excluding grouping variables) 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. NA values within grouping variables are treated as a new level.
re_formula	formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA or ~0, include no group-level effects.
method	Method used to obtain predictions. Can be set to "posterior_predict" (the default), "posterior_epred", or "posterior_linpred". For more details, see the respective function documentations.
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.
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).
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(Yrep), where SD(Yrep) is an estimate of the standard deviation of Yrep.

Value

An array of predictive error/residual draws. 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 draws.

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 documentation built on Sept. 23, 2024, 5:08 p.m.