Plot method for prediction error curves of a peperr object

Plots individual and aggregated prediction error estimates based on bootstrap samples.

Usage

Plot.peperr.curves(
  x,
  at.risk = TRUE,
  allErrors = FALSE,
  bootRuns = FALSE,
  bootQuants = TRUE,
  bootQuants.level = 0.95,
  leg.cex = 0.7,
  ...
)

Arguments

x: peperr object.
at.risk: number at risk to be display. default is TRUE.
allErrors: Display .632, no information and average out-of-bag error in addition. default is FALSE.
bootRuns: Display individual out-of-bag bootstrap samples. default is FALSE.
bootQuants: Display pointwise out-of-bag bootstrap quantiles as shaded area. default is TRUE.
bootQuants.level: Quantile probabilities for pointwise out-of-bag bootstrap quantiles. default is 0.95, i.e. 2.5% and 97.5% quantiles.
leg.cex: size of legend text
...: additional arguments, not used.

Details

This function is literally taken from plot.peperr in the peperr package. The display of prediction error curves is adapted to allow for numbers at risk and pointwise bootstrap quantiles.

References

Sill M., Hielscher T., Becker N. and Zucknick M. (2014), c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models, Journal of Statistical Software, Volume 62(5), pages 1–22. https://doi.org/10.18637/jss.v062.i05.

Author

Thomas Hielscher t.hielscher@dkfz.de

Examples


if (FALSE) { # \dontrun{

# example from glmnet package
set.seed(10101)
library(glmnet)
library(survival)
library(peperr)

N=1000;p=30
nzc=p/3
x=matrix(rnorm(N*p),N,p)
beta=rnorm(nzc)
fx=x[,seq(nzc)]%*%beta/3
hx=exp(fx)
ty=rexp(N,hx)
tcens=rbinom(n=N,prob=.3,size=1)# censoring indicator
y=Surv(ty,1-tcens)

peperr.object <- peperr(response=y, x=x, 
                        fit.fun=fit.glmnet, args.fit=list(family="cox"), 
                        complexity=complexity.glmnet,  
                        args.complexity=list(family="cox",nfolds=10),
                        indices=resample.indices(n=N, method="sub632", sample.n=10))

# pointwise bootstrap quantiles and all error types
Plot.peperr.curves(peperr.object, allErrors=TRUE)

# individual bootstrap runs and selected error types
Plot.peperr.curves(peperr.object, allErrors=FALSE, bootRuns=TRUE)
} # }