performance (version 0.5.0)

model_performance.stanreg: Performance of Bayesian Models

Description

Compute indices of model performance for (general) linear models.

Usage

# S3 method for stanreg
model_performance(model, metrics = "all", verbose = TRUE, ...)

Arguments

model

Object of class stanreg or brmsfit.

metrics

Can be "all", "common" or a character vector of metrics to be computed (some of c("LOOIC", "WAIC", "R2", "R2_adj", "RMSE", "LOGLOSS", "SCORE")). "common" will compute LOOIC, WAIC, R2 and RMSE.

verbose

Toggle off warnings.

...

Arguments passed to or from other methods.

Value

A data frame (with one row) and one column per "index" (see metrics).

Details

Depending on model, following indices are computed:

• ELPD expected log predictive density, see looic

• LOOIC leave-one-out cross-validation (LOO) information criterion, see looic

• WAIC widely applicable information criterion, see ?loo::waic

• R2 r-squared value, see r2

• R2_LOO_adjusted adjusted r-squared, see r2

• RMSE root mean squared error, see performance_rmse

• LOGLOSS Log-loss, see performance_logloss

• SCORE_LOG score of logarithmic proper scoring rule, see performance_score

• SCORE_SPHERICAL score of spherical proper scoring rule, see performance_score

• PCP percentage of correct predictions, see performance_pcp

References

Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2018). R-squared for Bayesian regression models. The American Statistician, The American Statistician, 1-6.

See Also

r2_bayes

Examples

# NOT RUN {
if (require("rstanarm")) {
  model <- stan_glm(mpg ~ wt + cyl, data = mtcars, chains = 1, iter = 500, refresh = 0)
  model_performance(model)

  model <- stan_glmer(
    mpg ~ wt + cyl + (1 | gear),
    data = mtcars,
    chains = 1,
    iter = 500,
    refresh = 0
  )
  model_performance(model)
}
# }