Last chance! 50% off unlimited learning
Sale ends in
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.
# S3 method for brmsfit
predictive_error(
object,
newdata = NULL,
re_formula = NULL,
re.form = NULL,
resp = NULL,
nsamples = NULL,
subset = NULL,
sort = FALSE,
...
)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.
Alias of re_formula.
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).
Further arguments passed to prepare_predictions
that control several aspects of data validation and prediction.
An S x N array of predictive error samples, where S is the
number of posterior samples and N is the number of observations.
# 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)
# }
# NOT RUN {
# }
Run the code above in your browser using DataLab