View source: R/model_performance.bayesian.R
model_performance.stanreg | R Documentation |
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
, the following indices are computed:
ELPD expected log predictive density. Larger ELPD values
mean better fit. See looic()
.
LOOIC leave-one-out cross-validation (LOO) information
criterion. Lower LOOIC values mean better fit. See looic()
.
WAIC widely applicable information criterion. Lower WAIC
values mean better fit. See ?loo::waic
.
R2 r-squared value, see r2_bayes()
.
R2_adjusted LOO-adjusted r-squared, see
r2_loo()
.
RMSE root mean squared error, see
performance_rmse()
.
SIGMA residual standard deviation, see
insight::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., and Vehtari, A. (2018). R-squared for Bayesian regression models. The American Statistician, The American Statistician, 1-6.
r2_bayes
## 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) } ## End(Not run)
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