aggregation.pmpec: Determine the prediction error curve for a fitted model

aggregation.pmpecR Documentation

Determine the prediction error curve for a fitted model


Interface to pmpec, for conforming to the structure required by the argument in peperr call. Evaluates the prediction error curve, i.e. the Brier score tracked over time, for a fitted survival model.


aggregation.pmpec(, response, x, model, cplx=NULL, times = NULL, 
   type=c("apparent", "noinf"), fullsample.attr = NULL, ...)


data frame with full data set.


Either a survival object (with Surv(time, status), where time is an n-vector of censored survival times and status an n-vector containing event status, coded with 0 and 1) or a matrix with columns time containing survival times and status containing integers, where 0 indicates censoring, 1 the interesting event and larger numbers other competing risks.


n*p matrix of covariates.


survival model as returned by as used in call to peperr.


numeric, number of boosting steps or list, containing number of boosting steps in argument stepno.


vector of evaluation time points. If given, used as well as in calculation of full apparent and no-information error as in resampling procedure. Not used if fullsample.attr is specified.




vector of evaluation time points, passed in resampling procedure. Either user-defined, if times were passed as args.aggregation, or the determined time points from the call with the full data set.


additional arguments passed to pmpec call.


If no evaluation time points are passed, they are generated using all uncensored time points if their number is smaller than 100, or 100 time points up to the 95% quantile of the uncensored time points are taken.

pmpec requires a predictProb method for the class of the fitted model, i.e. for a model of class class predictProb.class.


A matrix with one row. Each column represents the estimated prediction error of the fit at the time points.

See Also

peperr, predictProb, pmpec

peperr documentation built on March 18, 2022, 5:28 p.m.