Compute a Bayesian version of R-squared or LOO-adjusted R-squared for regression models.
Compute a Bayesian version of R-squared or LOO-adjusted R-squared for regression models.
## S3 method for class 'stanreg' bayes_R2(object, ..., re.form = NULL) ## S3 method for class 'stanreg' loo_R2(object, ...)
object |
A fitted model object returned by one of the
rstanarm modeling functions. See |
... |
Currently ignored. |
re.form |
For models with group-level terms, |
A vector of R-squared values with length equal to the posterior sample size (the posterior distribution of R-squared).
fit <- stan_glm(
mpg ~ wt + cyl,
data = mtcars,
QR = TRUE,
chains = 2,
refresh = 0
)
rsq <- bayes_R2(fit)
print(median(rsq))
hist(rsq)
loo_rsq <- loo_R2(fit)
print(median(loo_rsq))
# multilevel binomial model
if (!exists("example_model")) example(example_model)
print(example_model)
median(bayes_R2(example_model))
median(bayes_R2(example_model, re.form = NA)) # exclude group-levelPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.