Compute a LOO-adjusted R-squared for regression models

```
# S3 method for brmsfit
loo_R2(
object,
resp = NULL,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
```

## Arguments

- object
An object of class `brmsfit`

.

- resp
Optional names of response variables. If specified, predictions
are performed only for the specified response variables.

- summary
Should summary statistics be returned
instead of the raw values? Default is `TRUE`

.

- robust
If `FALSE`

(the default) the mean is used as
the measure of central tendency and the standard deviation as
the measure of variability. If `TRUE`

, the median and the
median absolute deviation (MAD) are applied instead.
Only used if `summary`

is `TRUE`

.

- probs
The percentiles to be computed by the `quantile`

function. Only used if `summary`

is `TRUE`

.

- ...
Further arguments passed to
`posterior_epred`

and
`log_lik`

,
which are used in the computation of the R-squared values.

## Value

If `summary = TRUE`

, an M x C matrix is returned
(M = number of response variables and c = `length(probs) + 2`

)
containing summary statistics of the LOO-adjusted R-squared values.
If `summary = FALSE`

, the posterior draws of the LOO-adjusted
R-squared values are returned in an S x M matrix (S is the number of draws).

## Examples

```
if (FALSE) {
fit <- brm(mpg ~ wt + cyl, data = mtcars)
summary(fit)
loo_R2(fit)
# compute R2 with new data
nd <- data.frame(mpg = c(10, 20, 30), wt = c(4, 3, 2), cyl = c(8, 6, 4))
loo_R2(fit, newdata = nd)
}
```