Compute R2 for Bayesian models. For mixed models (including a
random part), it additionally computes the R2 related to the fixed effects
only (marginal R2). While r2_bayes() returns a single R2 value,
r2_posterior() returns a posterior sample of Bayesian R2 values.
r2_bayes(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...)r2_posterior(model, ...)
# S3 method for brmsfit
r2_posterior(model, verbose = TRUE, ...)
# S3 method for stanreg
r2_posterior(model, verbose = TRUE, ...)
# S3 method for BFBayesFactor
r2_posterior(model, average = FALSE, prior_odds = NULL, verbose = TRUE, ...)
A list with the Bayesian R2 value. For mixed models, a list with the Bayesian R2 value and the marginal Bayesian R2 value. The standard errors and credible intervals for the R2 values are saved as attributes.
A Bayesian regression model (from brms, rstanarm, BayesFactor, etc).
Logical, if TRUE, the median instead of mean is used to
calculate the central tendency of the variances.
Value or vector of probability of the CI (between 0 and 1) to be estimated.
Toggle off warnings.
Arguments passed to r2_posterior().
Compute model-averaged index? See bayestestR::weighted_posteriors().
Optional vector of prior odds for the models compared to
the first model (or the denominator, for BFBayesFactor objects). For
data.frames, this will be used as the basis of weighting.
r2_bayes() returns an "unadjusted" R2 value. See
r2_loo() to calculate a LOO-adjusted R2, which comes
conceptually closer to an adjusted R2 measure.
For mixed models, the conditional and marginal R2 are returned. The marginal R2 considers only the variance of the fixed effects, while the conditional R2 takes both the fixed and random effects into account.
r2_posterior() is the actual workhorse for r2_bayes() and
returns a posterior sample of Bayesian R2 values.
Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018). R-squared for Bayesian regression models. The American Statistician, 1–6. tools:::Rd_expr_doi("10.1080/00031305.2018.1549100")