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Posterior Draws of Residuals/Predictive Errors

Source: R/predictive_error.R
residuals.brmsfit.Rd

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.

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.

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 \(Yrep\).

Examples

# \dontrun{
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
           data = inhaler, cores = 2)
#> Compiling Stan program...
#> Start sampling
#> : 
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

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