Compute indices of model performance for regression models.
Usage
# S3 method for lm
model_performance(model, metrics = "all", verbose = TRUE, ...)
Arguments
- model
A model.
- metrics
Can be
"all"
,"common"
or a character vector of metrics to be computed (one or more of"AIC"
,"AICc"
,"BIC"
,"R2"
,"R2_adj"
,"RMSE"
,"SIGMA"
,"LOGLOSS"
,"PCP"
,"SCORE"
)."common"
will compute AIC, BIC, R2 and RMSE.- verbose
Toggle off warnings.
- ...
Arguments passed to or from other methods.
Details
Depending on model
, following indices are computed:
AIC Akaike's Information Criterion, see
?stats::AIC
AICc Second-order (or small sample) AIC with a correction for small sample sizes
BIC Bayesian Information Criterion, see
?stats::BIC
R2 r-squared value, see
r2()
R2_adj adjusted r-squared, see
r2()
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()
model_performance()
correctly detects transformed response and
returns the "corrected" AIC and BIC value on the original scale. To get back
to the original scale, the likelihood of the model is multiplied by the
Jacobian/derivative of the transformation.
Examples
model <- lm(mpg ~ wt + cyl, data = mtcars)
model_performance(model)
#> # Indices of model performance
#>
#> AIC | BIC | R2 | R2 (adj.) | RMSE | Sigma
#> -----------------------------------------------------
#> 156.010 | 161.873 | 0.830 | 0.819 | 2.444 | 2.568
model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
model_performance(model)
#> # Indices of model performance
#>
#> AIC | BIC | Tjur's R2 | RMSE | Sigma | Log_loss | Score_log | Score_spherical | PCP
#> --------------------------------------------------------------------------------------------
#> 31.298 | 35.695 | 0.478 | 0.359 | 0.934 | 0.395 | -14.903 | 0.095 | 0.743