predict.brmsfit {brms}  R Documentation 
Draws from the Posterior Predictive Distribution
Description
This method is an alias of posterior_predict.brmsfit
with additional arguments for obtaining summaries of the computed draws.
Usage
## S3 method for class 'brmsfit'
predict(
object,
newdata = NULL,
re_formula = NULL,
transform = NULL,
resp = NULL,
negative_rt = FALSE,
ndraws = NULL,
draw_ids = NULL,
sort = FALSE,
ntrys = 5,
cores = NULL,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
Arguments
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 
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 
ndraws 
Positive integer indicating how many posterior draws should
be used. If 
draw_ids 
An integer vector specifying the posterior draws 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 
summary 
Should summary statistics be returned
instead of the raw values? Default is 
robust 
If 
probs 
The percentiles to be computed by the 
... 
Further arguments passed to 
Value
An array
of predicted response values.
If summary = FALSE
the output resembles those of
posterior_predict.brmsfit
.
If summary = TRUE
the output depends on the family: For categorical
and ordinal families, the output is an N x C matrix, where N is the number
of observations, C is the number of categories, and the values are
predicted category probabilities. For all other families, the output is a N
x E matrix where E = 2 + length(probs)
is the number of summary
statistics: The Estimate
column contains point estimates (either
mean or median depending on argument robust
), while the
Est.Error
column contains uncertainty estimates (either standard
deviation or median absolute deviation depending on argument
robust
). The remaining columns starting with Q
contain
quantile estimates as specified via argument probs
.
See Also
Examples
## Not run:
## fit a model
fit < brm(time  cens(censored) ~ age + sex + (1 + age  patient),
data = kidney, family = "exponential", init = "0")
## predicted responses
pp < predict(fit)
head(pp)
## predicted responses excluding the grouplevel effect of age
pp < predict(fit, re_formula = ~ (1  patient))
head(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)
)
predict(fit, newdata = newdata)
## End(Not run)