randomForestSRC-backend: randomForestSRC survival backend

In peperr: Parallelised Estimation of Prediction Error

randomForestSRC survival backend

Description

Wrappers for random survival forests fitted with randomForestSRC.

Usage

fit.rfsrc(response, x, cplx, ...)
complexity.oob.rfsrc(response, x, full.data,
    mtry = unique(pmax(1L, c(floor(sqrt(ncol(x))), floor(ncol(x)/3), ncol(x)))),
    nodesize = c(5L, 15L), ntree = 200L, ...)
## S3 method for class 'rfsrc'
predictProb(object, response, x, times, complexity = NULL, ...)

Arguments

response	survival response as a Surv object or a two-column time/status matrix.
x	covariate matrix.
cplx	either a selected mtry value or a named list such as list(mtry = ..., nodesize = ...).
full.data	full data frame, accepted for the peperr complexity-function contract.
mtry	candidate mtry values for OOB tuning.
nodesize	candidate terminal-node sizes for OOB tuning.
ntree	number of trees used during OOB tuning.
object	a fitted rfsrc object.
times	evaluation times for survival probabilities.
complexity	unused for fitted forests; included for interface compatibility.
...	additional arguments passed to rfsrc or predict.rfsrc.

Details

No PLL.rfsrc method is provided, because random survival forests do not naturally expose a Cox-style partial likelihood. They can still be used with pmpec through predictProb.rfsrc.

Value

Fitted rfsrc objects, tuning lists containing mtry and nodesize, and survival-probability matrices.

See Also

peperr, rfsrc, predict.rfsrc

peperr documentation built on March 25, 2026, 9:06 a.m.