Determine the prediction error curve for a fitted model

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

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

Arguments

full.data	data frame with full data set.
response	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.
x	n*p matrix of covariates.
model	survival model as returned by fit.fun as used in call to peperr.
cplx	numeric, number of boosting steps or list, containing number of boosting steps in argument stepno.
times	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.
type	character.
fullsample.attr	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 aggregation.fun call with the full data set.
...	additional arguments passed to pmpec call.

Details

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.

Value

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

See also

peperr, predictProb, pmpec