IBLM (version 1.0.2)

correction_corridor: Plot GLM vs IBLM Predictions with Different Corridors

Description

Creates a faceted scatter plot comparing GLM predictions to ensemble predictions across different trim values, showing how the ensemble corrects the base GLM model.

Usage

correction_corridor(
  iblm_model,
  data,
  trim_vals = c(NA_real_, 4, 1, 0.2, 0.1, 0),
  sample_perc = 0.2,
  color = NA,
  ...
)

Value

A ggplot object showing GLM vs IBLM predictions faceted by trim value. The diagonal line (y = x) represents perfect agreement between models

Arguments

iblm_model

An IBLM model object of class "iblm".

data

Data frame. If you have used `split_into_train_validate_test()` this will usually be the "test" portion of your data.

trim_vals

Numeric vector of trim values to compare. The length of this vector will dictate the no. of facets shown in plot output

sample_perc

Proportion of data to randomly sample for plotting (0 to 1). Default is 0.2 to improve performance with large datasets

color

Optional. Name of a variable in `data` to color points by

...

Additional arguments passed to `geom_point()`

Examples

df_list <- freMTPLmini |> split_into_train_validate_test(seed = 9000)

iblm_model <- train_iblm_xgb(
  df_list,
  response_var = "ClaimRate",
  family = "poisson"
)

correction_corridor(iblm_model = iblm_model, data = df_list$test, color = "DrivAge")