R: Summarizing prediction error curves

crps {pec} | R Documentation

## 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.

### 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.

### Value

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

### Author(s)

Thomas A. Gerds tag@biostat.ku.dk

### 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

    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)

[Package pec version 2020.11.17 Index]