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
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[3])
model_performance(model, average = TRUE)
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