Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free
Documentation
R
brms

loo_R2.brmsfit

Compute a LOO-adjusted R-squared for regression models

Description

Compute a LOO-adjusted R-squared for regression models

Usage

## S3 method for class 'brmsfit'
loo_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 and log_lik, which are used in the computation of the R-squared values.

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 LOO-adjusted R-squared values. If summary = FALSE, the posterior samples of the LOO-adjusted R-squared values are returned in an S x M matrix (S is the number of samples).

Examples

## Not run: 
fit <- brm(mpg ~ wt + cyl, data = mtcars)
summary(fit)
loo_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))
loo_R2(fit, newdata = nd)

## End(Not run)
brms

Bayesian Regression Models using 'Stan'

v2.15.0
GPL-2
Authors
Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]
Initial release
2021-03-10

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.