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

Description Usage Arguments Details Value See Also

Description

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

Usage

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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 interval (1, boost.steps).

eval.times

vector of evaluation time points.

smooth

logical. Shall 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 of the interval (0, boost.steps), for which the minimal IPEC is obtained, is returned.

Value

Scalar value giving the number of boosting steps.

See Also

peperr, CoxBoost


peperr documentation built on May 2, 2019, 4:08 a.m.