R: Determine the prediction error curve for a fitted model

aggregation.pmpec {peperr}

R Documentation

Determine the prediction error curve for a fitted model

Description

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.

Usage

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