ordered

ordered is a parsnip extension to enable additional classification models for ordinal outcomes (e.g., “low”, “medium”, “high”). While there are several model/engine combinations in the parsnip package that can be used, this package adds:

• cumulative link (cumulative logit) ordinal regression via MASS::polr()
• regularized elastic net ordinal regression via ordinalNet::ordinalNet() (Wurm, Hanlon, and Rathouz, 2021)
• ordinal classification trees via rpartScore::rpartScore() (Galimberti, Soffritti, and Di Maso, 2012)
• latent variable ordinal forests via ordinalForest::ordfor() (Hornung, 2020)

More will be added. Under consideration are:

• cumulative link, adjacent categories, continuation ratio, and stopping ratio, ordinal regression via VGAM::cumulative(), VGAM::acat(), VGAM::cratio(), and VGAM::sratio() (Yee, 2015)
• regularized cumulative probability (cumulative logit) ordinal regression via rms::lrm() and rms::orm() (Harrell, 2015)
• continuation ratio (stopping ratio) ordinal regression using elastic net regularization via glmnetcr::glmnetcr() and using regularization path computation via glmpathcr::glmpathcr() (Archer and Williams, 2012)
• generalized monotone incremental forward stagewise regularized ordinal regression via ordinalgmifs::ordinalgmifs() (Archer, Hou, Zhou, Ferber, Layne, and Gentry, 2014; Gentry, Jackson-Cook, Lyon, and Archer, 2015)
• Bayesian LASSO ordinal regression via ordinalbayes::ordinalbayes() (Zhang and Archer, 2021)
• cumulative link ordinal regression via ordinal::clm() (Christensen, 2023)

There are some existing features in tidymodels packages that are useful for ordinal outcomes:

• The partykit engines for parsnip::decision_tree() and parsnip::rand_forest() use the ordered nature of the factors to train the model.
• The yardstick package has yardstick::kap() for weighted and unweighted Kappa statistics (the former being of more interest). Also, yardstick::classification_cost() can utilize more complex cost structures and uses the class probabilities for estimation.

Installation

You can install the development version of ordered like so:

# install.packages("pak")
pak::pak("corybrunson/ordered", dependencies = FALSE)

Currently, ordered relies on engine and dial registration in the following forks:

pak::pak("corybrunson/parsnip@ordered", dependencies = FALSE)
pak::pak("corybrunson/dials@ordered", dependencies = FALSE)

Example

Here is a simple example using computational chemistry data to predict the permeability of a molecule:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(ordered)
#> Loading required package: parsnip

data(caco, package = "QSARdata")

caco_dat <-
  inner_join(caco_Outcome, caco_Dragon, by = "Molecule") %>%
  as_tibble() %>%
  select(
    class = Class,
    mol_weight = QikProp_mol_MW,
    volume = QikProp_volume,
    ClogP
  )
caco_train <- caco_dat[-(1:10), ]
caco_test  <- caco_dat[ (1:10), ]

ord_rf_spec <- 
  # you should really use many more trees and score sets
  rand_forest(mtry = 2, trees = 100, num_scores = 100) %>%
  set_mode("classification") %>%
  set_engine("ordinalForest")

set.seed(382)
ord_rf_fit <- ord_rf_spec %>% fit(class ~ ., data = caco_train)
augment(ord_rf_fit, new_data = caco_test)
#> # A tibble: 10 × 8
#>    .pred_class .pred_L .pred_M .pred_H class mol_weight volume  ClogP
#>    <ord>         <dbl>   <dbl>   <dbl> <ord>      <dbl>  <dbl>  <dbl>
#>  1 M            0.370    0.384  0.246  M           123.   445.  0.799
#>  2 M            0.250    0.533  0.217  L           290.   856.  0.534
#>  3 M            0.178    0.801  0.0212 M           519.  1576.  1.02 
#>  4 M            0.221    0.736  0.0431 M           533.  1606.  1.58 
#>  5 M            0.135    0.762  0.103  M           505.  1517.  1.71 
#>  6 M            0.0698   0.913  0.0176 M           519.  1547.  2.27 
#>  7 M            0.220    0.738  0.0417 M           517.  1600.  1.78 
#>  8 M            0.109    0.868  0.0229 M           531.  1631.  2.34 
#>  9 M            0.0307   0.952  0.0177 M           517.  1572.  2.81 
#> 10 L            0.603    0.394  0.003  L           588.  1799. -1.85

Code of Conduct

Please note that the ordered project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.