Ordinal generalized additive model via vector GAMs

VGAM::vgam() fits vector generalized additive models, which specialize to several families of ordinal regression models.

Details

For this engine, there is a single mode: classification

Tuning Parameters

There are no main tuning parameters for this model. Two relevant engine parameters are:

• link: the link function such as logistic, probit, loglog, cloglog, or cauchit.

• family: the function to contrast levels such as cumulative_link, adjacent_categories, continuation_ratio, or stopping_ratio

Translation from parsnip to the original package

The ordered extension package is required to fit this model.

library(ordered)

gen_additive_mod() %>%
  set_engine("vgam") %>%
  set_mode("classification") %>%
  translate()

## GAM Model Specification (classification)
##
## Computational engine: vgam
##
## Model fit template:
## ordered::VGAM_vgam_wrapper(formula = missing_arg(), data = missing_arg(),
##     weights = missing_arg(), parallel = TRUE)

Model fitting

This model should be used with a model formula so that smooth terms can be specified. For example:

library(VGAM)
# Make number of cylinders and ordered factor
ord_cars <- mtcars[, -1]
ord_cars$cyl <- as.ordered(ord_cars$cyl)

car_fit <-
  gen_additive_mod() |>
  set_engine("vgam") |>
  set_mode("classification") |>
  fit(cyl ~ disp + s(wt) + am, data = ord_cars)

The smoothness of the terms will need to be manually specified (e.g., using s(x, df = 10)) in the formula.

When using a workflow, pass the model formula to workflows::add_model()’s formula argument, and a simplified preprocessing formula elsewhere.

spec <-
  gen_additive_mod() |>
  set_engine("vgam") |>
  set_mode("classification")

workflow() |>
  add_model(spec, formula = cyl ~ disp + s(wt) + am) |>
  add_formula(cyl ~ disp + wt + am) |>
  fit(data = ord_cars) |>
  extract_fit_engine()

To learn more about the differences between these formulas, see ?model_formula.

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), parsnip will convert factor columns to indicators.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in case_weights and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

Prediction types

parsnip:::get_from_env("gen_additive_mod_predict") |>
  dplyr::filter(engine == "vgam") |>
  dplyr::select(mode, type)

## # A tibble: 2 x 2
##   mode           type
##   <chr>          <chr>
## 1 classification class
## 2 classification prob

References

• Yee T. 2010. The VGAM Package for Categorical Data Analysis. Journal of Statistical Software 32(10):1–34. .