R2 {pec}R Documentation

Summarizing prediction error curves

Description

Cumulating prediction error curves over time and a time-dependent $R^2$ like measure.

Usage

R2(object, who, what, times ,nullModel=1)
crps(object, who, what, times, start)

Arguments

object An object with estimated prediction error curves obtained with the function pec
who Which models in object$models should be considered. Default is all models for crps and all models except the reference for R2
what Usually pred.error – if crossvalidation or bootstrap methods are used then also the apparent.error and if replan=boot.632plus then also the BootB0.error, the estimated overfit, and the NoInf.error can be cumulated or compared to a reference prediction model.
times Time points at which the summaries are shown.
start Only for crps: the time point at which the cumulation is started
nullModel Position of the model whose prediction error is used as the reference in the denominator when constructing $R^2$

Details

The cumulative prediction error (continuous ranked probability score) is defined as the area under the prediction error curve.

In survival analysis the prediction error of the Kaplan-Meier estimator plays a similar role as the total sum of squares in linear regression. Hence, it is a sensible reference model for $R^2$.

Value

A matrix with a column for every requested prediction model

Author(s)

Thomas A. Gerds tag@biostat.ku.dk

See Also

pec

Examples

set.seed(18713)

dat=SimSurv(100)
nullmodel=prodlim(Hist(time,status)~1,data=dat)
pmodel=coxph(Surv(time,status)~X1+X2,data=dat)
perror=pec(list(KaplanMeier=nullmodel,Cox=pmodel),Hist(time,status)~1,data=dat)

## cumulative prediction error
crps(perror,times=1) # between min time and 1
crps(perror,times=1,start=0) # between 0 and 1
crps(perror,times=seq(0,1,.2),start=0) # between 0 and seq(0,1,.2)

R2(perror,times=seq(0,1,.1))

[Package pec version 1.0.7 Index]