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]