r2_loo: LOO-adjusted R2

In performance: Assessment of Regression Models Performance

LOO-adjusted R2

Description

Compute LOO-adjusted R2.

Usage

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().

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.

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.

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

Examples

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

performance documentation built on Nov. 2, 2023, 5:48 p.m.