Performance of Bayesian Models
Compute indices of model performance for (general) linear models.
## S3 method for class 'stanreg' model_performance(model, metrics = "all", verbose = TRUE, ...) ## S3 method for class 'BFBayesFactor' model_performance( model, metrics = "all", verbose = TRUE, average = FALSE, prior_odds = NULL, ... )
model |
Object of class |
metrics |
Can be |
verbose |
Toggle off warnings. |
... |
Arguments passed to or from other methods. |
average |
Compute model-averaged index? See |
prior_odds |
Optional vector of prior odds for the models compared to the first model (or the denominator, for |
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_bayes
R2_LOO_adjusted adjusted r-squared, see r2_loo
RMSE root mean squared error, see performance_rmse
SIGMA residual standard deviation, see get_sigma()
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
A data frame (with one row) and one column per "index" (see metrics).
Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2018). R-squared for Bayesian regression models. The American Statistician, The American Statistician, 1-6.
## Not run:
if (require("rstanarm") && require("rstantools")) {
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)
}
if (require("BayesFactor") && require("rstantools")) {
model <- generalTestBF(carb ~ am + mpg, mtcars)
model_performance(model)
model_performance(model[3])
model_performance(model, average = TRUE)
}
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