R: Generic function and default method for Bayesian R-squared

bayes_R2 {rstantools} | R Documentation

## Generic function and default method for Bayesian R-squared

### Description

Generic function and default method for Bayesian version of R-squared for regression models. A generic for LOO-adjusted R-squared is also provided. See the bayes_R2.stanreg() method in the rstanarm package for an example of defining a method.

### Usage

    bayes_R2(object, ...)

    ## Default S3 method:
    bayes_R2(object, y, ...)

    loo_R2(object, ...)

### Arguments

`object` | The object to use.
`...` | Arguments passed to methods. See the methods in the rstanarm package for examples.
`y` | For the default method, a vector of `y` values the same length as the number of columns in the matrix used as `object`.

### Value

`bayes_R2()` and `loo_R2()` methods should return a vector of length equal to the posterior sample size.

The default `bayes_R2()` method just takes `object` to be a matrix of y-hat values (one column per observation, one row per posterior draw) and `y` to be a vector with length equal to `ncol(object)`.

### References

Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari (2018). R-squared for Bayesian regression models. The American Statistician, to appear. DOI: 10.1080/00031305.2018.1549100. (Preprint, Notebook)

### See Also

- The rstanarm package (mc-stan.org/rstanarm) for example methods (CRAN, GitHub).

- Guidelines and recommendations for developers of R packages interfacing with Stan and a demonstration getting a simple package working can be found in the vignettes included with rstantools and at mc-stan.org/rstantools/articles.

[Package rstantools version 2.0.0 Index]