`R/bayes_R2.R`

`bayes_R2.brmsfit.Rd`

Compute a Bayesian version of R-squared for regression models

```
# S3 method for brmsfit
bayes_R2(
object,
resp = NULL,
summary = TRUE,
robust = FALSE,
probs = c(0.025, 0.975),
...
)
```

- 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`

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

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 Bayesian R-squared values.
If `summary = FALSE`

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

For an introduction to the approach, see Gelman et al. (2018) and https://github.com/jgabry/bayes_R2/.

Andrew Gelman, Ben Goodrich, Jonah Gabry & Aki Vehtari. (2018).
R-squared for Bayesian regression models, *The American Statistician*.
`10.1080/00031305.2018.1549100`

(Preprint available at
https://stat.columbia.edu/~gelman/research/published/bayes_R2_v3.pdf)