Add coefficient restrictions to a refinement workflow — add

Fixes selected model levels to user-supplied relativities in a refinement workflow. This is useful when the fitted GLM coefficients need to be adjusted before the final tariff is refitted, for example to apply expert judgement, enforce a business rule, remove an implausible local effect, or make a tariff structure easier to explain.

Usage

add_restriction(model, restrictions)

Arguments

model: Object of class rating_refinement, usually created with prepare_refinement().
restrictions: Data frame with exactly two columns. The first column must have the same name as the model variable to restrict and contains the levels to adjust. The second column contains the replacement relativities. Levels that are not supplied are filled with the currently fitted GLM relativities.

Value

Object of class rating_refinement.

Details

add_restriction() stores a restriction step on a rating_refinement object. It does not refit the GLM immediately. The restrictions are applied when refit() is called.

The restrictions data frame identifies the model variable to restrict by its first column. The second column contains the relativities that should be used for those levels in the refined model. New code should use this function after prepare_refinement(); the deprecated restrict_coef() wrapper is only kept for backwards compatibility.

The restriction table may contain all levels of the model variable, or only the levels that need a manual adjustment. If only a subset is supplied, the missing levels are automatically filled with their current fitted GLM relativities. This makes it possible to fix one level explicitly while keeping the other levels at their already estimated values.

Author

Martin Haringa

Examples

portfolio <- data.frame(
  claims = c(1, 2, 1, 3, 2, 4),
  exposure = rep(1, 6),
  postal_area = factor(c("A", "B", "C", "A", "B", "C"))
)

model <- glm(
  claims ~ postal_area + offset(log(exposure)),
  family = poisson(),
  data = portfolio
)

restrictions <- data.frame(
  postal_area = "C",
  relativity = 1.10
)

refined <- prepare_refinement(model, data = portfolio) |>
  add_restriction(restrictions)