complexity.ipec.CoxBoost: Interface function for CoxBoost complexity selection via...

In peperr: Parallelised Estimation of Prediction Error

Interface function for CoxBoost complexity selection via integrated prediction error curves

Description

Determines the number of boosting steps for a survival model fitted by CoxBoost via integrated prediction error curve estimates, conforming to the calling convention required by argument complexity in peperr call.

Usage

complexity.ipec.CoxBoost(response, x, boot.n.c = 10, boost.steps = 100,
   eval.times = NULL, smooth = FALSE, full.data, ...)

complexity.ripec.CoxBoost(response, x, boot.n.c = 10, boost.steps = 100,
   eval.times = NULL, smooth = FALSE, full.data, ...)

Arguments

response	a survival object (with Surv(time, status)).
x	n*p matrix of covariates.
boot.n.c	number of bootstrap samples.
boost.steps	maximum number of boosting steps, i.e. number of boosting steps is selected out of the interval [1, boost.steps].
eval.times	vector of evaluation time points.
smooth	logical. Shall the prediction error curve be smoothed by local polynomial regression before integration?
full.data	data frame containing response and covariates of the full data set.
...	additional arguments passed to CoxBoost call.

Details

Plotting the .632+ estimator for each time point given in eval.times results in a prediction error curve. A summary measure can be obtained by integrating over time. complexity.ripec.CoxBoost computes a Riemann integral, while complexity.ipec.CoxBoost uses a Lebesgue-like integral taking Kaplan-Meier estimates as weights. The number of boosting steps for which the minimal integrated error is obtained is returned.

Since CoxBoost is only suggested by peperr, install it before calling these functions. If smooth = TRUE, locfit is also required.

Value

Scalar value giving the number of boosting steps.

See Also

peperr, CoxBoost

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