insurancerating (version 0.8.2)

model_performance: Compare fitted GLMs using common performance measures

Description

Compare one or more fitted GLMs using AIC, BIC and response-scale RMSE. The resulting table provides a concise first comparison of alternative pricing-model specifications fitted to the same portfolio outcome.

Usage

model_performance(...)

Value

A data frame of class "model_performance", with columns:

Model

Name of the model object as passed to the function.

AIC

AIC value.

BIC

BIC value.

RMSE

Root mean squared error.

Arguments

...

One or more objects of class "glm".

Author

Martin Haringa

Details

The following measures are reported:

AIC

Akaike information criterion, balancing likelihood fit and model complexity.

BIC

Bayesian information criterion, applying a stronger sample-size-dependent complexity penalty.

RMSE

Root mean squared error between observed and response-scale predicted values.

Lower values are preferred within each measure, but the measures answer different questions. AIC and BIC depend on the model likelihood, whereas RMSE measures error on the response scale. Comparisons are therefore most meaningful when models use the same response, estimation records, weights and offsets.

The table does not select a pricing model automatically. In actuarial model assessment, statistical fit should be considered together with portfolio calibration, residual behaviour, coefficient stability, exposure by level and the practical interpretability of the resulting tariff structure.

The implementation is adapted from performance::model_performance().

See Also

rmse(), bootstrap_performance(), check_overdispersion(), check_residuals()

Examples

m1 <- glm(nclaims ~ area, offset = log(exposure), family = poisson(),
          data = MTPL2)
m2 <- glm(nclaims ~ area + premium, offset = log(exposure), family = poisson(),
          data = MTPL2)
model_performance(m1, m2)