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.
Value
A data.frame of class "model_data" with additional attributes:
response: response variable in the model;rf: risk factors used for default rating-grid grouping, including the output factors of split refinements;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.
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.
A factor created by add_relativities() replaces its parent in the
default rating-grid grouping. Its numeric relativity columns are mapped to
the new segment levels using the stored refinement specification; the
original portfolio columns remain available for explicit grouping and audits.
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.
Examples
if (FALSE) { # \dontrun{
library(insurancerating)
pmodel <- glm(
breaks ~ wool + tension,
data = warpbreaks,
family = poisson(link = "log")
)
extract_model_data(pmodel)
} # }