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: 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: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Warning: Some Pareto k diagnostic values are slightly high. See help('pareto-k-diagnostic') for details.
#> # LOO-adjusted R2 with Compatibility Interval
#>
#> Conditional R2: 0.794 (95% CI [0.691, 0.888])
```