Compute indices of model performance for regression models.

# 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 (some of c("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.

Value

A data frame (with one row) and one column per "index" (see metrics).

Details

Depending on model, following indices are computed:

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