Integrated prediction error curve

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'

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.

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.

See also

perr

Examples

if (FALSE) { # \dontrun{
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))
} # }