insurancerating (version 0.8.2)

extract_model_data: Recover the portfolio data used by a fitted model

Description

Recover the estimation data and pricing metadata stored with a fitted GLM or a model produced by the refinement workflow. The result provides a reproducible basis for rating grids, coefficient tables and portfolio-level model diagnostics.

model_data() is kept as a deprecated compatibility wrapper.

Usage

extract_model_data(x)

Value

A data.frame of class "model_data" with additional attributes:

• response: response variable in the model;

• rf: names of risk factors in the model;

• offweights: weight and offset variables if present;

• terms: model terms object for plain GLMs;

• mgd_rst, mgd_smt: merged restrictions and smooths for refit objects;

• new_nm, old_nm: new and old column names for refit objects.

Arguments

x

An object of class "glm", "refitsmooth", or "refitrestricted".

Author

Martin Haringa

Details

Data represented by the result

For an ordinary GLM, the function recovers the data stored with the model or its model frame and records the response, model terms, risk factors, weights and offsets. The recovered data represent the observations available to the fitted model. Rows omitted during fitting, for example because of missing model variables, may therefore not be present.

For a refined model, technical columns used to construct smoothing and restriction terms are removed from the returned data. The mappings required to interpret the refined coefficients are retained as attributes.

Actuarial use

The extracted object is intended for downstream calculations that must remain consistent with the fitted pricing model, such as rating_grid() and rating_table(). It should not be interpreted as a replacement for the original raw portfolio extract: preprocessing, filtering and missing-value handling applied before or during model fitting remain part of the data provenance.

See Also

rating_grid(), rating_table(), prepare_refinement()

Examples

if (FALSE) {
library(insurancerating)

pmodel <- glm(
  breaks ~ wool + tension,
  data = warpbreaks,
  family = poisson(link = "log")
)

extract_model_data(pmodel)
}