man/rmd/gen_additive_mod_vgam.md

In parsnip: A Common API to Modeling and Analysis Functions

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][parsnip::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 \code{\link[=fit.model_spec]{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. \doi{10.18637/jss.v032.i10}.

parsnip documentation built on May 14, 2026, 5:08 p.m.