peperr (version 1.2)

plot.peperr: Plot method for peperr object

Description

Plots, allowing to get a first impression of the prediction error estimates and to check complexity selection in bootstrap samples.

Usage

# S3 method for peperr
plot(x, y, ...)

Arguments

x

peperr object.

y

not used.

…

additional arguments, not used.

Details

The plots provide a simple and fast overview of the results of the estimation of the prediction error through resampling. Which plots are shown depends on if complexity was selected, i.e., a function was passed in the peperr call for complexity, or explicitly passed. In case of survival response, prediction error curves are shown. In case of binary response, where one complexity value is passed explicitly, no plot is available. Especially in the case that complexity is selected in each bootstrap sample, these diagnostic plots help to check whether the resampling procedure works adequately and to detect specific problems due to high-dimensional data structures.

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)

peperr.object1 <- 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))
plot(peperr.object1)

peperr.object2 <- peperr(response=Surv(time, status), x=x, 
   fit.fun=fit.CoxBoost, args.fit=list(penalty=100),
   complexity=complexity.mincv.CoxBoost, args.complexity=list(penalty=100),
   indices=resample.indices(n=length(time), method="sub632", sample.n=10),
   trace=TRUE)
plot(peperr.object2)

peperr.object3 <- peperr(response=Surv(time, status), x=x, 
   fit.fun=fit.CoxBoost, args.fit=list(penalty=100),
   complexity=complexity.mincv.CoxBoost, args.complexity=list(penalty=100),
   indices=resample.indices(n=length(time), method="sub632", sample.n=10),
   args.aggregation=list(times=seq(0, quantile(time, probs=0.9), length.out=100)),
   trace=TRUE)
plot(peperr.object3)
# }