Fit a prepared refinement specification

Apply the ordered steps stored in a rating_refinement object and fit the resulting pricing GLM. This evaluates the current refinement specification; it may be called repeatedly while smoothing, restrictions, shrinkage, rebasing or sublevel relativities are being reviewed.

Usage

refit(object, intercept_only = FALSE, ...)

Arguments

object: Object of class rating_refinement, usually created with prepare_refinement().
intercept_only: Logical. If FALSE (default), fit the refined model with remaining model terms still free. If TRUE, keep remaining existing relativities fixed as offsets and estimate only the intercept.
...: Additional arguments passed to stats::glm(), such as control.

Value

A fitted object inheriting from glm. Compatibility classes refitrestricted, refitsmooth, or both are added when relevant. The object stores refinement metadata used by rating_table() and rating_grid() to identify fixed relativities, smoothed variables and derived tariff factors.

Details

refit() applies the stored steps in their recorded order, constructs the required tariff variables and offsets, updates the model formula and calls stats::glm() with the original model family. Additional fitting arguments can be supplied through ....

Actuarial interpretation

The refitted model represents the combined effect of the original GLM structure and the explicit actuarial assumptions stored in the refinement. Its coefficients and predictions should be assessed against exposure, observed experience, model diagnostics and the unrestricted model. A refit does not establish that a manual restriction or curve edit is statistically estimated; it applies that assumption as specified.

Intercept-only recalibration

With intercept_only = FALSE, the refined GLM is fitted with the remaining free model terms that are still present after applying the refinement steps. With intercept_only = TRUE, remaining original model effects are fixed as offsets based on their existing fitted relativities. Only the intercept is then estimated. Consequently, relative differences between those fixed effects remain unchanged while the overall expected premium level is recalibrated to the supplied model data.

In practical actuarial work, intercept_only = TRUE is generally suitable for a controlled actuarial or commercial refinement of an accepted tariff structure. Examples include a small manual restriction, a limited curve adjustment or a final calibration in which the relativities of unaffected risk factors should remain unchanged.

Use intercept_only = FALSE when the refinement forms part of substantive model development. The remaining free model terms are then estimated again, allowing the GLM to account for dependence between risk factors and find a new joint statistical optimum conditional on the fixed refinement steps. Coefficients of risk factors that were not directly refined may therefore also change.

Printing the returned model first shows the original and refitted formulas, the model family, whether an intercept-only refit was used, and a concise description of every restriction, smoothing, shrinkage or relativity step. This is followed by the regular glm output with the model call, coefficients, degrees of freedom, deviance and AIC. The object continues to inherit from glm, so standard methods such as stats::predict.glm() and summary.glm() remain available.

The returned GLM is a fitted result, not an editable refinement specification. Retain the original rating_refinement object when further changes may be required. Passing the refitted GLM to prepare_refinement() starts a new workflow from that model and does not reconstruct the earlier sequence of refinement steps.

Author

Martin Haringa

Examples

zip_df <- data.frame(
  zip = c(0, 1, 2, 3),
  zip_adj = c(0.8, 0.9, 1.0, 1.2)
)

model <- glm(
  nclaims ~ zip + offset(log(exposure)),
  family = poisson(),
  data = MTPL
)

refinement <- prepare_refinement(model) |>
  add_restriction(zip_df)

refined_model <- refit(refinement, intercept_only = TRUE)