In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("ordinal_reg", "vglm")

Tuning Parameters

defaults <- 
  tibble::tibble(
    parsnip = c("ordinal_link", "odds_link"),
    default = c("logit", "cumulative")
  )

param <-
  ordinal_reg() |>
  set_engine("ordinalNet") |>
  set_mode("classification") |>
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

param$item

Translation from parsnip to the original package

r uses_extension("ordinal_reg", "vglm", "classification")

library(ordered)

ordinal_reg(ordinal_link = character(0), odds_link = character(0)) %>% 
  set_engine("vglm") %>% 
  set_mode("classification") %>% 
  translate()

Preprocessing requirements

#| child: template-makes-dummies.Rmd

Case weights

#| child: template-uses-case-weights.Rmd

However, the documentation in [VGAM::vglm()] notes that matrix of case weights can be passed so that different classes have different weights. tidymodels assumes vector of a weights; a matrix cannot be passed in.

Also, the engine documentation notes that: "The values of weights must be positive; try setting a very small value such as 1.0e-8 to effectively delete an observation."

Prediction types

#| label: predict-types
parsnip:::get_from_env("ordinal_reg_predict") |>
  dplyr::filter(engine == "vglm") |>
  dplyr::select(mode, type)

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.