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:

• ordinal regression via MASS::polr()
• ordinal classification trees via rpartScore::rpartScore() (Galimberti, Soffritti, and Di Maso, 2012)
• ordinal forests via ordinalForest::ordfor() (Hornung, 2020)

More will be added. Under consideration are:

• ordinal regression via VGAM::vglm() (Yee, 2015)
• ordinal regression via ordinalNet::ordinalNet() (Wurm, Hanlon, and Rathouz, 2021)
• Bayesian ordinal regression via ordinalbayes::ordinalbayes() (Zhang and Archer, 2021)
• 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(ordered)
#> Loading required package: parsnip
#> Warning: S3 method 'tunable.multinomial_reg' was declared in NAMESPACE but not
#> found
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

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[-(1:5), ]
  caco_test  <- caco_dat[ (1:5), ]

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

set.seed(382)
ord_rf_fit <- ord_rf_spec %>% fit(class ~ ., data = caco_train)
augment(ord_rf_fit, caco_test)
#> # A tibble: 5 × 8
#>   .pred_class .pred_L .pred_M .pred_H class mol_weight volume ClogP
#>   <ord>         <dbl>   <dbl>   <dbl> <ord>      <dbl>  <dbl> <dbl>
#> 1 M             0.346   0.404  0.250  M           123.   445. 0.799
#> 2 M             0.362   0.514  0.124  L           290.   856. 0.534
#> 3 M             0.204   0.786  0.01   M           519.  1576. 1.02 
#> 4 M             0.190   0.788  0.0221 M           533.  1606. 1.58 
#> 5 M             0.177   0.780  0.0435 M           505.  1517. 1.71

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.