r2_loo {performance} | R Documentation |
Compute LOO-adjusted R2.
r2_loo(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...) r2_loo_posterior(model, ...) ## S3 method for class 'brmsfit' r2_loo_posterior(model, verbose = TRUE, ...) ## S3 method for class 'stanreg' r2_loo_posterior(model, verbose = TRUE, ...)
model |
A Bayesian regression model (from brms, rstanarm, BayesFactor, etc). |
robust |
Logical, if |
ci |
Value or vector of probability of the CI (between 0 and 1) to be estimated. |
verbose |
Toggle off warnings. |
... |
Arguments passed to |
r2_loo()
returns an "adjusted" R2 value computed using a
leave-one-out-adjusted posterior distribution. This is conceptually similar
to an adjusted/unbiased R2 estimate in classical regression modeling. See
r2_bayes()
for an "unadjusted" R2.
Mixed models are not currently fully supported.
r2_loo_posterior()
is the actual workhorse for r2_loo()
and
returns a posterior sample of LOO-adjusted Bayesian R2 values.
A list with the Bayesian R2 value. For mixed models, a list with the Bayesian R2 value and the marginal Bayesian R2 value. The standard errors and credible intervals for the R2 values are saved as attributes.
A list with the LOO-adjusted R2 value. The standard errors and credible intervals for the R2 values are saved as attributes.
if (require("rstanarm")) { model <- stan_glm(mpg ~ wt + cyl, data = mtcars, chains = 1, iter = 500, refresh = 0) r2_loo(model) }