brms (version 1.10.2)

bayes_R2.brmsfit: Compute a Bayesian version of R-squared for regression models


Compute a Bayesian version of R-squared for regression models


# S3 method for brmsfit
bayes_R2(object, newdata = NULL, re_formula = NULL,
  allow_new_levels = FALSE, sample_new_levels = "uncertainty",
  new_objects = list(), incl_autocor = TRUE, subset = NULL,
  nsamples = NULL, nug = NULL, summary = TRUE, 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 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.


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.


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.


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.


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.


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.


If summary = TRUE a 1 x C matrix is returned (C = length(probs) + 2) containing summary statistics of Bayesian R-squared values. If summary = FALSE the posterior samples of the R-squared values are returned in a S x 1 matrix (S is the number of samples).


For an introduction to the approach, see


Run this code
fit <- brm(mpg ~ wt + cyl, data = mtcars)

# compute R2 with new data
nd <- data.frame(mpg = c(10, 20, 30), wt = c(4, 3, 2), cyl = c(8, 6, 4))
bayes_R2(fit, newdata = nd)
# }
# }

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