R: Compute a Bayesian version of R-squared for regression models

bayes_R2.brmsfit {brms} | R Documentation

## Compute a Bayesian version of R-squared for regression models

### Description

Compute a Bayesian version of R-squared for regression models

### Usage

    ## S3 method for class 'brmsfit'
    bayes_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 (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.

### 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).

### Examples

    ## Not run:
    fit <- brm(mpg ~ wt + cyl, data = mtcars)
    summary(fit)
    bayes_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))
    bayes_R2(fit, newdata = nd)

    ## End(Not run)

[Package brms version 2.6.0 Index]