pec (version 2018.07.26)

crps: Summarizing prediction error curves

Description

Computes the cumulative prediction error curves, aka integrated Brier scores, in ranges of time.

Usage

crps(object, models, what, times, start)

Arguments

object

An object with estimated prediction error curves obtained with the function pec

models

Which models in object$models should be considered.

what

The name of the entry in x to be cumulated. Defauls to PredErr Other choices are AppErr, BootCvErr, Boot632, Boot632plus.

times

Time points at which the integration of the prediction error curve stops.

start

The time point at which the integration of the prediction error curve is started.

Value

A matrix with a column for the crps (ibs) at every requested time point and a row for each model

Details

The cumulative prediction error (continuous ranked probability score) is defined as the area under the prediction error curve, hence the alias name, ibs, which is short for integrated Brier score.

References

E. Graf et al. (1999), Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, vol 18, pp= 2529--2545.

Gerds TA, Cai T & Schumacher M (2008) The performance of risk prediction models Biometrical Journal, 50(4), 457--479

See Also

pec

Examples

# NOT RUN {
set.seed(18713)
library(prodlim)
library(survival)
dat=SimSurv(100)
pmodel=coxph(Surv(time,status)~X1+X2,data=dat,x=TRUE,y=TRUE)
perror=pec(list(Cox=pmodel),Hist(time,status)~1,data=dat)

## cumulative prediction error
crps(perror,times=1) # between min time and 1
## same thing:
ibs(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)


# }