man/rmd/ordinal_reg_ordinalNet.md

In parsnip: A Common API to Modeling and Analysis Functions

For this engine, there is a single mode: classification

Tuning Parameters

This model has 4 tuning parameters:

• penalty: Amount of Regularization (type: double, default: see below)

• mixture: Proportion of Lasso Penalty (type: double, default: 1.0)

• ordinal_link: Ordinal Link (type: character, default: logit)

• odds_link: Odds Link (type: character, default: cumulative)

Translation from parsnip to the original package

The ordered extension package is required to fit this model.

library(ordered)

ordinal_reg(
  penalty = double(0),
  mixture = double(0),
  ordinal_link = character(0),
  odds_link = character(0)
) %>%
  set_engine("ordinalNet") %>%
  # "classification" is the only mode
  translate()

## Ordinal Regression Model Specification (classification)
## 
## Main Arguments:
##   ordinal_link = character(0)
##   odds_link = character(0)
##   penalty = numeric(0)
##   mixture = double(0)
## 
## Computational engine: ordinalNet 
## 
## Model fit template:
## ordered::ordinalNet_wrapper(x = missing_arg(), y = missing_arg(), 
##     weights = missing_arg(), link = character(0), family = character(0), 
##     alpha = double(0), nLambda = 120L, lambdaMinRatio = 1e-08, 
##     includeLambda0 = TRUE)

Controlling penalty values

ordinalNet(), like glmnet(), simultaneously computes a set of parameter estimates for multiple penalty values. Predictions can be made at these penalty values at the same time. However, unlike glmnet(), ordinalNet() does not interpolate if you want to predict using penalty values not exactly among those it precomputed. Similarly, it cannot predict for models with penalties outside of the range of those precomputed.

The \pkg{ordered} package can interpolate between the preset penalty values but cannot predict outside of their range; this will cause an error.

We suggest that you set the collection of penalty values when fitting the model. This is important when tuning the model. Different data sets and mixture values (a.k.a. alpha) will pair best with different sets of penalties and it might be good to set a wide range.

To do this, you can use set_engine() to pass a vector of penalty values as so:

# Example of setting a wide penalty range
penalties <- 10^seq(-10, 0, length.out = 20)

ordinal_reg(penalty = tune()) |>
  set_engine("ordinalNet", path_values = !!penalties)

See [glmnet-details] for more background.

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.

Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.

By default, [ordinalNet::ordinalNet()] uses the argument standardize = TRUE to center and scale the data.

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("ordinal_reg_predict") |>
  dplyr::filter(engine == "ordinalNet") |>
  dplyr::select(mode, type)

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

References

• Wurm MJ, Rathouz PJ, Hanlon BM. 2021. Regularized Ordinal Regression and the ordinalNet R Package. Journal of Statistical Software 99(6):1-42. \doi{10.18637/jss.v099.i06}.

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