r2_loo function

LOO-adjusted R2

Compute LOO-adjusted R2.

r2_loo(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...)

r2_loo_posterior(model, ...)

## S3 method for class 'brmsfit'
r2_loo_posterior(model, verbose = TRUE, ...)

## S3 method for class 'stanreg'
r2_loo_posterior(model, 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().

Returns

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 list with the LOO-adjusted R2 value. The standard errors and credible intervals for the R2 values are saved as attributes.

Details

r2_loo() returns an "adjusted" R2 value computed using a leave-one-out-adjusted posterior distribution. This is conceptually similar to an adjusted/unbiased R2 estimate in classical regression modeling. See r2_bayes() for an "unadjusted" R2.

Mixed models are not currently fully supported.

r2_loo_posterior() is the actual workhorse for r2_loo() and returns a posterior sample of LOO-adjusted Bayesian R2 values.

Examples

model <- suppressWarnings(rstanarm::stan_glm(
  mpg ~ wt + cyl,
  data = mtcars,
  chains = 1,
  iter = 500,
  refresh = 0,
  show_messages = FALSE
))
r2_loo(model)