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Random survival forests via randomForestSRC

Source: R/rand_forest_randomForestSRC.R
details_rand_forest_randomForestSRC.Rd

randomForestSRC::rfsrc() fits a random survival forest: a large number of survival trees, each grown on a bootstrap sample of the data. The final prediction averages the predictions from the individual trees.

Details

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

Tuning Parameters

This model has 3 tuning parameters:

  • trees: # Trees (type: integer, default: 500L)

  • min_n: Minimal Node Size (type: integer, default: 15L)

  • mtry: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))

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("randomForestSRC") |>
  set_mode("censored regression") |>
  translate()

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

censored::rfsrc_train() is a wrapper around randomForestSRC::rfsrc() that makes it easier to run this model.

Preprocessing requirements

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.

Case weights

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() functions have arguments called case_weights that expect vectors of case weights.

Prediction types

parsnip:::get_from_env("rand_forest_predict") |>
  dplyr::filter(engine == "randomForestSRC") |>
  dplyr::select(mode, type) |>
  print(n = Inf)

## # A tibble: 2 x 2
##   mode                type
##   <chr>               <chr>
## 1 censored regression time
## 2 censored regression survival

Other details

Predictions of type "time" are predictions of the median survival time.

References

  • Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Annals of Applied Statistics 2008; 2(3):841-860. .