rand_forest_aorsf.md
In parsnip: A Common API to Modeling and Analysis Functions

For this engine, there is a single mode: censored regression

This model has 3 tuning parameters:

trees: # Trees (type: integer, default: 500L)
min_n: Minimal Node Size (type: integer, default: 5L)
mtry: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))

Additionally, this model has one engine-specific tuning parameter:

split_min_stat: Minimum test statistic required to split a node. Default is 3.841459 for the log-rank test, which is roughly a p-value of 0.05.

Translation from parsnip to the original package (censored regression)

The censored extension package is required to fit this model.

library(censored)

rand_forest() %>%
  set_engine("aorsf") %>%
  set_mode("censored regression") %>%
  translate()

## Random Forest Model Specification (censored regression)
## 
## Computational engine: aorsf 
## 
## Model fit template:
## aorsf::orsf(formula = missing_arg(), data = missing_arg(), weights = missing_arg())

This engine does not require any special encoding of the predictors. Categorical predictors can be partitioned into groups of factor levels (e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables are not required for this model.

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.

Predictions of survival probability at a time exceeding the maximum observed event time are the predicted survival probability at the maximum observed time in the training data.

Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min YI, Mcclure LA, Howard G, Simon N. Oblique random survival forests. Annals of applied statistics 2019 Sep; 13(3):1847-83. DOI: 10.1214/19-AOAS1261
Jaeger BC, Welden S, Lenoir K, Pajewski NM. aorsf: An R package for supervised learning using the oblique random survival forest. Journal of Open Source Software 2022, 7(77), 1 4705. \doi{10.21105/joss.04705}.
Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM. Accelerated and interpretable oblique random survival forests. arXiv e-prints 2022 Aug; arXiv-2208. URL: https://arxiv.org/abs/2208.01129