prepare_predictions.brmsfit {brms}  R Documentation 
This method helps in preparing brms models for certin postprocessing
tasks most notably various forms of predictions. Unless you are a package
developer, you will rarely need to call prepare_predictions
directly.
## S3 method for class 'brmsfit' prepare_predictions( x, newdata = NULL, re_formula = NULL, allow_new_levels = FALSE, sample_new_levels = "uncertainty", incl_autocor = TRUE, oos = NULL, resp = NULL, nsamples = NULL, subset = NULL, nug = NULL, smooths_only = FALSE, offset = TRUE, newdata2 = NULL, new_objects = NULL, point_estimate = NULL, ... ) prepare_predictions(x, ...)
x 
An R object typically 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 
allow_new_levels 
A flag indicating if new levels of grouplevel
effects are allowed (defaults to 
sample_new_levels 
Indicates how to sample new levels for grouping
factors specified in 
incl_autocor 
A flag indicating if correlation structures originally
specified via 
oos 
Optional indices of observations for which to compute outofsample rather than insample predictions. Only required in models that make use of response values to make predictions, that is currently only ARMA models. 
resp 
Optional names of response variables. If specified, predictions are performed only for the specified response variables. 
nsamples 
Positive integer indicating how many posterior samples should
be used. If 
subset 
A numeric vector specifying the posterior samples to be used.
If 
nug 
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 
smooths_only 
Logical; If 
offset 
Logical; Indicates if offsets should be included in the
predictions. Defaults to 
newdata2 
A named 
new_objects 
Deprecated alias of 
point_estimate 
Shall the returned object contain only point estimates
of the parameters instead of their posterior samples? Defaults to

... 
Further arguments passed to 
An object of class 'brmsprep'
or 'mvbrmsprep'
,
depending on whether a univariate or multivariate model is passed.