# Oblique random survival forests via aorsf

Source:`R/rand_forest_aorsf.R`

`details_rand_forest_aorsf.Rd`

`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.

## 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: 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())
```

### 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()`

arguments have arguments called
`case_weights`

that expect vectors of 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. .

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