aorsf::orsf() fits a model that creates a large number of decision
trees, each de-correlated from the others. The final prediction uses all
predictions from the individual trees and combines them.
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. .
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
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.