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, ... )
Object of class
Toggle off warnings.
Arguments passed to or from other methods.
Compute model-averaged index? See
Optional vector of prior odds for the models compared to
the first model (or the denominator, for
model, the following indices are computed:
ELPD: expected log predictive density. Larger ELPD values
mean better fit. See
LOOIC: leave-one-out cross-validation (LOO) information
criterion. Lower LOOIC values mean better fit. See
WAIC: widely applicable information criterion. Lower WAIC
values mean better fit. See
R2: r-squared value, see
R2_adjusted: LOO-adjusted r-squared, see
RMSE: root mean squared error, see
SIGMA: residual standard deviation, see
LOGLOSS: Log-loss, see
SCORE_LOG: score of logarithmic proper scoring rule, see
SCORE_SPHERICAL: score of spherical proper scoring rule, see
PCP: percentage of correct predictions, see
A data frame (with one row) and one column per "index" (see
Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018). R-squared for Bayesian regression models. The American Statistician, The American Statistician, 1-6.
model <- suppressWarnings(rstanarm::stan_glm( mpg ~ wt + cyl, data = mtcars, chains = 1, iter = 500, refresh = 0 )) model_performance(model) model <- suppressWarnings(rstanarm::stan_glmer( mpg ~ wt + cyl + (1 | gear), data = mtcars, chains = 1, iter = 500, refresh = 0 )) model_performance(model) model <- BayesFactor::generalTestBF(carb ~ am + mpg, mtcars) model_performance(model) model_performance(model) model_performance(model, average = TRUE)
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