Plot method for prediction error curves of a peperr object

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

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.

Author

Thomas Hielscher t.hielscher@dkfz.de

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. doi: 10.18637/jss.v062.i05

See also

peperr

Examples

if (FALSE) {

# 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)]
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)
}