ipec: Integrated prediction error curve

In peperr: Parallelised Estimation of Prediction Error

Integrated prediction error curve

Description

Summary measures of prediction error curves

Usage

ipec(pe, eval.times, type=c("Riemann", "Lebesgue", "relativeLebesgue"), response=NULL)

Arguments

pe	prediction error at different time points. Vector of length of eval.times or matrix (columns correspond to evaluation time points, rows to different prediction error estimates)
eval.times	evalutation time points
type	type of integration. 'Riemann' estimates Riemann integral, 'Lebesgue' uses the probability density as weights, while 'relativeLebesgue' delivers the difference to the null model (using the same weights as for 'Lebesgue').
response	survival object (Surv(time, status)), required only if type is 'Lebesgue' or 'relativeLebesgue'

Details

For survival data, prediction error at each evaluation time point can be extracted of a peperr object by function perr. A summary measure can then be obtained via intgrating over time. Note that the time points used for evaluation are stored in list element attribute of the peperr object.

Value

ipec	Value of integrated prediction error curve. Integer or vector, if pe is vector or matrix, respectively, i.e. one entry per row of the passed matrix.

See Also

perr

Examples

## Not run: 
n <- 200
p <- 100
beta <- c(rep(1,10),rep(0,p-10))
x <- matrix(rnorm(n*p),n,p)
real.time <- -(log(runif(n)))/(10*exp(drop(x %*% beta)))
cens.time <- rexp(n,rate=1/10)
status <- ifelse(real.time <= cens.time,1,0)
time <- ifelse(real.time <= cens.time,real.time,cens.time)

# Example:
# Obtain prediction error estimate fitting a Cox proportional hazards model
# using CoxBoost 
# through 10 bootstrap samples 
# with fixed complexity 50 and 75
# and aggregate using prediction error curves
peperr.object <- peperr(response=Surv(time, status), x=x, 
   fit.fun=fit.CoxBoost, complexity=c(50, 75), 
   indices=resample.indices(n=length(time), method="sub632", sample.n=10))
# 632+ estimate for both complexity values at each time point
prederr <- perr(peperr.object)
# Integrated prediction error curve for both complexity values
ipec(prederr, eval.times=peperr.object$attribute, response=Surv(time, status))

## End(Not run)

peperr documentation built on March 31, 2023, 7:34 p.m.