This method is an alias of posterior_epred.brmsfit
with additional arguments for obtaining summaries of the computed draws.
# S3 method for 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),
...
)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.
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.
Either "response" or "linear".
If "response", results are returned on the scale
of the response variable. If "linear",
results are returned on the scale of the linear predictor term,
that is without applying the inverse link function or
other transformations.
Optional names of response variables. If specified, predictions are performed only for the specified response variables.
Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned.
Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned.
Positive integer indicating how many posterior draws should
be used. If NULL (the default) all draws are used. Ignored if
draw_ids is not NULL.
An integer vector specifying the posterior draws to be used.
If NULL (the default), all draws 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.
posterior_epred.brmsfit
if (FALSE) {
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
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))
}
Run the code above in your browser using DataLab