Compute LOO-adjusted R2.
Usage
r2_loo(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...)
r2_loo_posterior(model, ...)
# S3 method for brmsfit
r2_loo_posterior(model, verbose = TRUE, ...)
# S3 method for 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().
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.
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
if (require("rstanarm")) {
model <- stan_glm(mpg ~ wt + cyl, data = mtcars, chains = 1, iter = 500, refresh = 0)
r2_loo(model)
}
#> Warning: The largest R-hat is 1.05, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> # LOO-adjusted R2 with Compatibility Interval
#>
#> Conditional R2: 0.794 (95% CI [0.696, 0.876])