bayes_R2.brmsfit.Rmd

---
title: " ``brms::bayes_R2.brmsfit``"
output: html_document
---


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(brms)
```



Compute a Bayesian version of R-squared for regression models

#### Description 

Compute a Bayesian version of R-squared for regression models

#### Usage

<pre><code>
## S3 method for class 'brmsfit'
bayes_R2(object, resp = NULL, summary = TRUE,
robust = FALSE, probs = c(0.025, 0.975), ...)
</code></pre>

#### 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 (i.e. means, sds, and 95% intervals) 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 fitted, which is used in the computation of the R-squared values.



#### Examples 

```{r}
## Not run:
fit <- brm(mpg ~ wt + cyl, data = mtcars)
summary(fit)
bayes_R2(fit)
```
```{r}
# compute R2 with new data
nd <- data.frame(mpg = c(10, 20, 30), wt = c(4, 3, 2), cyl = c(8, 6, 4))
bayes_R2(fit, newdata = nd)
## End(Not run)

```
#### Details
For an introduction to the approach, see https://github.com/jgabry/bayes_R2/blob/master/
bayes_R2.pdf.

#### Value

If summary = TRUE a 1 x C matrix is returned (C = length(probs) + 2) containing summary
statistics of Bayesian R-squared values. If summary = FALSE the posterior samples of the R-squared values are returned in a S x 1 matrix (S is the number of samples).