complexity.ipec.rsf_mtry {peperr} | R Documentation |
Determines the optimal value for the number of candidate values at each split for a survival model fitted by random survival forest via integrated prediction error curve (IPEC) estimates, conforming to the calling convention required by argument complexity
in peperr
call.
complexity.ipec.rsf_mtry(response, x, boot.n.c = 10, mtry, eval.times = NULL, full.data, relative=TRUE, ...)
response |
a survival object (with Surv(time, status) ). |
x |
n*p matrix of covariates. |
boot.n.c |
number of bootstrap samples. |
mtry |
vector of potential values for parameter mtry, i.e. number of randomly selected variables at each split. |
eval.times |
vector of evaluation time points. |
full.data |
Data frame containing response and covariates of the full data set. |
relative |
Should relative IPEC be used for evaluation? |
... |
additional arguments passed to rsf call. |
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.ipec.rsf_mtry
uses a Lebesgue-like integral taking Kaplan-Meier estimates as weights. The optimal value for parameter mtry out of the set of passed values, for which the minimal IPEC is obtained, is returned.
Scalar value giving the optimal value for parameter mtry.
peperr
, rsf