In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("rand_forest", "aorsf")

Tuning Parameters

defaults <-
  tibble::tibble(parsnip = c("trees", "min_n", "mtry"),
                 default = c("500L", "5L", "ceiling(sqrt(n_predictors))"))

param <-
  rand_forest() %>%
  set_engine("aorsf") %>%
  set_mode("censored regression") %>%
  make_parameter_list(defaults) %>%
  distinct()

This model has r nrow(param) tuning parameters:

param$item

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)

r uses_extension("rand_forest", "aorsf", "censored regression")

library(censored)

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

Preprocessing requirements

Case weights

Other details

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.

References

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