complexity.mincv.CoxBoost: Interface for CoxBoost selection of optimal number of...

Description Usage Arguments Details Value See Also

Description

Determines the number of boosting steps for a survival model fitted by CoxBoost via cross-validation, conforming to the calling convention required by argument complexity in peperr call.

Usage

1
complexity.mincv.CoxBoost(response, x, full.data, ...)

Arguments

response

a survival object (Surv(time, status)).

x

n*p matrix of covariates.

full.data

data frame containing response and covariates of the full data set.

...

additional arguments passed to cv.CoxBoost call.

Details

Function is basically a wrapper around cv.CoxBoost of package CoxBoost. A K-fold cross-validation (default K=10) is performed to search the optimal number of boosting steps, per default in the interval (0, maxstepno=100). The number of boosting steps with minimum mean partial log-likelihood is returned. Calling peperr, the default arguments of cv.CoxBoost can be changed by passing a named list containing these as argument args.complexity.

Value

Scalar value giving the optimal number of boosting steps.

See Also

peperr, cv.CoxBoost


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