brmsfit ObjectsPredict responses based on the fitted model.
Can be performed for the data used to fit the model
(posterior predictive checks) or for new data.
By definition, these predictions have higher variance than
predictions of the fitted values (i.e., the 'regression line')
performed by the fitted
method. This is because the measurement error is incorporated.
The estimated means of both methods should, however, be very similar.
# S3 method for brmsfit
predict(object, newdata = NULL, re_formula = NULL,
transform = NULL, allow_new_levels = FALSE,
sample_new_levels = "uncertainty", new_objects = list(),
incl_autocor = TRUE, negative_rt = FALSE, subset = NULL,
nsamples = NULL, sort = FALSE, nug = NULL, ntrys = 5,
summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)# S3 method for brmsfit
posterior_predict(object, newdata = NULL,
re_formula = NULL, transform = NULL, allow_new_levels = FALSE,
sample_new_levels = "uncertainty", new_objects = list(),
incl_autocor = TRUE, negative_rt = FALSE, subset = NULL,
nsamples = NULL, sort = FALSE, nug = NULL, ntrys = 5,
robust = FALSE, probs = c(0.025, 0.975), ...)
An object of class brmsfit
An optional data.frame for which to evaluate predictions.
If NULL (default), the orginal data of the model is used.
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.
A function or a character string naming a function to be applied on the predicted responses before summary statistics are computed.
A flag indicating if new
levels of group-level effects are allowed
(defaults to FALSE).
Only relevant if newdata is provided.
Indicates how to sample new levels
for grouping factors specified in re_formula.
This argument is only relevant if newdata is provided and
allow_new_levels is set to TRUE.
If "uncertainty" (default), include group-level uncertainty
in the predictions based on the variation of the existing levels.
If "gaussian", sample new levels from the (multivariate)
normal distribution implied by the group-level standard deviations
and correlations. This options may be useful for conducting
Bayesian power analysis.
If "old_levels", directly sample new levels from the
existing levels.
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 distinquish responses on the upper and
lower boundary. Defaults to FALSE.
A numeric vector specifying
the posterior samples to be used.
If NULL (the default), all samples are used.
Positive integer indicating how many
posterior samples should be used.
If NULL (the default) all samples are used.
Ignored if subset is not NULL.
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).
Small positive number for Gaussian process terms only.
For numerical reasons, the covariance matrix of a Gaussian
process might not be positive definite. Adding a very small
number to the matrix's diagonal often solves this problem.
If NULL (the default), nug is chosen internally.
Parameter used in rejection sampling
for truncated discrete models only
(defaults to 5). See Details for more information.
Should summary statistics
(i.e. means, sds, and 95% intervals) 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 deivation (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.
Currently ignored.
Predicted values of the response variable.
If summary = TRUE the output depends on the family:
For catagorical and ordinal families, it is a N x C matrix,
where N is the number of observations and
C is the number of categories.
For all other families, it is a N x E matrix where E is equal
to length(probs) + 2.
If summary = FALSE, the output is as a S x N matrix,
where S is the number of samples.
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.
Method posterior_predict.brmsfit is an alias of
predict.brmsfit with summary = FALSE.
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 occure suggesting to increase argument ntrys.
# NOT RUN {
## fit a model
fit <- brm(time | cens(censored) ~ age + sex + (1+age||patient),
data = kidney, family = "exponential", inits = "0")
## predicted responses
pp <- predict(fit)
head(pp)
## predicted responses excluding the group-level effect of age
pp2 <- predict(fit, re_formula = ~ (1|patient))
head(pp2)
## 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)
# }
# NOT RUN {
# }
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