prepare_predictions.brmsfit {brms}R Documentation

Prepare Predictions


This method helps in preparing brms models for certin post-processing 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'
  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, ...)



An R object typically 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.


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), each posterior sample for a new level is drawn from the posterior samples of a randomly chosen existing level. Each posterior sample for a new level may be drawn from a different existing level such that the resulting set of new posterior samples represents the variation across 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 or predicting new levels in situations where relatively few levels where observed in the old_data. If "old_levels", directly sample new levels from the existing levels, where a new level is assigned all of the posterior samples of the same (randomly chosen) existing level.


A flag indicating if correlation structures originally specified via autocor should be included in the predictions. Defaults to TRUE.


Optional indices of observations for which to compute out-of-sample rather than in-sample predictions. Only required in models that make use of response values to make predictions, that is currently only ARMA models.


Optional names of response variables. If specified, predictions are performed only for the specified response variables.


Positive integer indicating how many posterior samples should be used. If NULL (the default) all samples are used. Ignored if subset is not NULL.


A numeric vector specifying the posterior samples to be used. If NULL (the default), all samples are used.


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.


Logical; If TRUE only predictions related to the computation of smooth terms will be prepared.


Logical; Indicates if offsets should be included in the predictions. Defaults to TRUE.


A named list of objects containing new data, which cannot be passed via argument newdata. Required for some objects used in autocorrelation structures, or stanvars.


Deprecated alias of newdata2.


Shall the returned object contain only point estimates of the parameters instead of their posterior samples? Defaults to NULL in which case no point estimate is computed. Alternatively, may be set to "mean" or "median". This argument is primarily implemented to ensure compatibility with the loo_subsample method.


Further arguments passed to validate_newdata.


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

[Package brms version 2.15.0 Index]