fitted.brmsfit {brms}  R Documentation 
Expected Values of the Posterior Predictive Distribution
Description
This method is an alias of posterior_epred.brmsfit
with additional arguments for obtaining summaries of the computed draws.
Usage
## S3 method for class 'brmsfit'
fitted(
object,
newdata = NULL,
re_formula = NULL,
scale = c("response", "linear"),
resp = NULL,
dpar = NULL,
nlpar = NULL,
ndraws = NULL,
draw_ids = NULL,
sort = FALSE,
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 
scale 
Either 
resp 
Optional names of response variables. If specified, predictions are performed only for the specified response variables. 
dpar 
Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned. 
nlpar 
Optional name of a predicted nonlinear parameter. If specified, expected predictions of this parameters are returned. 
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 ( 
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 mean response values.
If summary = FALSE
the output resembles those of
posterior_epred.brmsfit
.
If summary = TRUE
the output depends on the family: For categorical
and ordinal families, the output is an N x E x C array, where N is the
number of observations, E is the number of summary statistics, and C is the
number of categories. For all other families, the output is an N x E
matrix. The number of summary statistics E is equal to 2 +
length(probs)
: 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
.
In multivariate models, an additional dimension is added to the output which indexes along the different response variables.
See Also
Examples
## Not run:
## fit a model
fit < brm(rating ~ treat + period + carry + (1subject),
data = inhaler)
## compute expected predictions
fitted_values < fitted(fit)
head(fitted_values)
## plot expected predictions against actual response
dat < as.data.frame(cbind(Y = standata(fit)$Y, fitted_values))
ggplot(dat) + geom_point(aes(x = Estimate, y = Y))
## End(Not run)