This method is an alias of predictive_error.brmsfit
with additional arguments for obtaining summaries of the computed samples.
# S3 method for 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),
...
)An object of class brmsfit.
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.
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 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.
The type of the residuals,
either "ordinary" or "pearson".
More information is provided under 'Details'.
Optional names of response variables. If specified, predictions are performed only for the specified response variables.
Positive integer indicating how many posterior samples should
be used. If NULL (the default) all samples are used. Ignored if
subset is not NULL.
A numeric vector specifying the posterior samples to be used.
If NULL (the default), all samples are used.
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).
Should summary statistics be returned
instead of the raw values? Default is TRUE..
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.
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.
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.
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
\(Y\).
# 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)
# }
# NOT RUN {
# }
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