bayes_R2.brmsfit: Compute a Bayesian version of R-squared for regression models

In brms: Bayesian Regression Models using 'Stan'

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 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.

Details

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

Value

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).

References

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)

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)

brms documentation built on Sept. 23, 2024, 5:08 p.m.