R: Extract Data and Posterior Draws

extract_draws.brmsfit {brms}

R Documentation

Extract Data and Posterior Draws

Description

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 extract_draws directly.

Usage

## S3 method for class 'brmsfit'
extract_draws(x, newdata = NULL, re_formula = NULL,
  allow_new_levels = FALSE, sample_new_levels = "uncertainty",
  incl_autocor = TRUE, resp = NULL, nsamples = NULL, subset = NULL,
  nug = NULL, smooths_only = FALSE, offset = TRUE,
  new_objects = list(), ...)

extract_draws(x, ...)

Arguments

`x`	An R object typically of class `'brmsfit'`.
`newdata`	An optional data.frame for which to evaluate predictions. If `NULL` (default), the original data of the model is used.
`re_formula`	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.
`allow_new_levels`	A flag indicating if new levels of group-level effects are allowed (defaults to `FALSE`). Only relevant if `newdata` is provided.
`sample_new_levels`	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.
`incl_autocor`	A flag indicating if ARMA autocorrelation parameters should be included in the predictions. Defaults to `TRUE`. Setting it to `FALSE` will not affect other correlation structures such as `cor_bsts`, or `cor_fixed`.
`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 `NULL` (the default) all samples are used. Ignored if `subset` is not `NULL`.
`subset`	A numeric vector specifying the posterior samples to be used. If `NULL` (the default), all samples are used.
`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 `NULL` (the default), `nug` is chosen internally.
`smooths_only`	Logical; If `TRUE` only draws related to the computation of smooth terms will be extracted.
`offset`	Logical; Indicates if offsets should be included in the predictions. Defaults to `TRUE`.
`new_objects`	A named `list` of objects containing new data, which cannot be passed via argument `newdata`. Currently, only required for objects passed to `cor_sar` and `cor_fixed`.
`...`	Further arguments passed to `validate_newdata`.

Value

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

[Package brms version 2.6.0 Index]