posterior_predict.brmsfit {brms}  R Documentation 
Compute posterior samples of the posterior predictive distribution. Can be
performed for the data used to fit the model (posterior predictive checks) or
for new data. By definition, these samples have higher variance than samples
of the means of the posterior predictive distribution computed by
posterior_epred.brmsfit
. This is because the residual error
is incorporated in posterior_predict
. However, the estimated means of
both methods averaged across samples should be very similar.
## S3 method for class 'brmsfit' posterior_predict( object, newdata = NULL, re_formula = NULL, re.form = NULL, transform = NULL, resp = NULL, negative_rt = FALSE, nsamples = NULL, subset = NULL, sort = FALSE, ntrys = 5, cores = getOption("mc.cores", 1), ... )
object 
An object of class 
newdata 
An optional data.frame for which to evaluate predictions. If

re_formula 
formula containing grouplevel effects to be considered in
the prediction. If 
re.form 
Alias of 
transform 
(Deprecated) A function or a character string naming a function to be applied on the predicted responses before summary statistics are computed. 
resp 
Optional names of response variables. If specified, predictions are performed only for the specified response variables. 
negative_rt 
Only relevant for Wiener diffusion models.
A flag indicating whether response times of responses
on the lower boundary should be returned as negative values.
This allows to distinguish responses on the upper and
lower boundary. Defaults to 
nsamples 
Positive integer indicating how many posterior samples should
be used. If 
subset 
A numeric vector specifying the posterior samples to be used.
If 
sort 
Logical. Only relevant for time series models.
Indicating whether to return predicted values in the original
order ( 
ntrys 
Parameter used in rejection sampling
for truncated discrete models only
(defaults to 
cores 
Number of cores (defaults to 
... 
Further arguments passed to 
NA
values within factors in newdata
,
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.
In multilevel models, it is possible to
allow new levels of grouping factors to be used in the predictions.
This can be controlled via argument allow_new_levels
.
New levels can be sampled in multiple ways, which can be controlled
via argument sample_new_levels
. Both of these arguments are
documented in prepare_predictions
along with several
other useful arguments to control specific aspects of the predictions.
For truncated discrete models only: In the absence of any general
algorithm to sample from truncated discrete distributions, rejection
sampling is applied in this special case. This means that values are
sampled until a value lies within the defined truncation boundaries. In
practice, this procedure may be rather slow (especially in R). Thus, we
try to do approximate rejection sampling by sampling each value
ntrys
times and then select a valid value. If all values are
invalid, the closest boundary is used, instead. If there are more than a
few of these pathological cases, a warning will occur suggesting to
increase argument ntrys
.
An array
of predicted response values. In univariate models,
the output is as an S x N matrix, where S is the number of posterior
samples and N is the number of observations. In multivariate models, an
additional dimension is added to the output which indexes along the
different response variables.
## Not run: ## fit a model fit < brm(time  cens(censored) ~ age + sex + (1 + age  patient), data = kidney, family = "exponential", inits = "0") ## predicted responses pp < posterior_predict(fit) str(pp) ## predicted responses excluding the grouplevel effect of age pp < posterior_predict(fit, re_formula = ~ (1  patient)) str(pp) ## predicted responses of patient 1 for new data newdata < data.frame( sex = factor(c("male", "female")), age = c(20, 50), patient = c(1, 1) ) pp < posterior_predict(fit, newdata = newdata) str(pp) ## End(Not run)