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'
Returns
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.
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.
Examples
## Not run:n <-200p <-100beta <- 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 curvespeperr.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 pointprederr <- perr(peperr.object)for both complexity values
ipec(prederr, eval.times=peperr.object$attribute, response=Surv(time, status))## End(Not run)