Description Usage Arguments Details Value See Also
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.
1 2 3 4 5  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, ...)

response 
a survival object (with 
x 

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 
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 Lebesguelike integral taking KaplanMeier estimates as weights. The number of boosting steps of the interval (0, boost.steps
), for which the minimal IPEC is obtained, is returned.
Scalar value giving the number of boosting steps.
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