performance: r2_bayes – R documentation

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r2_bayes

Bayesian R2

Description

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.

Usage

r2_bayes(model, robust = TRUE, ci = 0.89, verbose = TRUE, ...)

r2_posterior(model, ...)

## S3 method for class 'brmsfit'
r2_posterior(model, verbose = TRUE, ...)

## S3 method for class 'stanreg'
r2_posterior(model, verbose = TRUE, ...)

## S3 method for class 'BFBayesFactor'
r2_posterior(model, average = FALSE, prior_odds = NULL, verbose = TRUE, ...)

Arguments

`model`	A Bayesian regression model (from brms, rstanarm, BayesFactor, etc).
`robust`	Logical, if `TRUE`, the median instead of mean is used to calculate the central tendency of the variances.
`ci`	Value or vector of probability of the CI (between 0 and 1) to be estimated.
`verbose`	Toggle off warnings.
`...`	Arguments passed to `r2_posterior()`.
`average`	Compute model-averaged index? See `bayestestR::weighted_posteriors()`.
`prior_odds`	Optional vector of prior odds for the models compared to the first model (or the denominator, for `BFBayesFactor` objects). For `data.frame`s, this will be used as the basis of weighting.

Details

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.

Value

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.

References

Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2018). R-squared for Bayesian regression models. The American Statistician, 1–6. doi: 10.1080/00031305.2018.1549100

Examples

library(performance)
if (require("rstanarm") && require("rstantools")) {
  model <- stan_glm(mpg ~ wt + cyl, data = mtcars, chains = 1, iter = 500, refresh = 0)
  r2_bayes(model)

  model <- stan_lmer(
    Petal.Length ~ Petal.Width + (1 | Species),
    data = iris,
    chains = 1,
    iter = 500,
    refresh = 0
  )
  r2_bayes(model)
}
## Not run: 
if (require("BayesFactor")) {
  data(mtcars)

  BFM <- generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
  FM <- lm(mpg ~ qsec + gear, data = mtcars)

  r2_bayes(FM)
  r2_bayes(BFM[3])
  r2_bayes(BFM, average = TRUE) # across all models


  # with random effects:
  mtcars$gear <- factor(mtcars$gear)
  model <- lmBF(
    mpg ~ hp + cyl + gear + gear:wt,
    mtcars,
    progress = FALSE,
    whichRandom = c("gear", "gear:wt")
  )
  r2_bayes(model)
}

if (require("brms")) {
  model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
  r2_bayes(model)

  model <- brms::brm(Petal.Length ~ Petal.Width + (1 | Species), data = iris)
  r2_bayes(model)
}

## End(Not run)

performance

Assessment of Regression Models Performance

v0.7.1

GPL-3

Authors

Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>), Dominique Makowski [aut, ctb] (<https://orcid.org/0000-0001-5375-9967>), Mattan S. Ben-Shachar [aut, ctb] (<https://orcid.org/0000-0002-4287-4801>), Indrajeet Patil [aut, ctb] (<https://orcid.org/0000-0003-1995-6531>), Philip Waggoner [aut, ctb] (<https://orcid.org/0000-0002-7825-7573>), Vincent Arel-Bundock [ctb] (<https://orcid.org/0000-0003-2042-7063>)

Initial release