superpc.plotcv {superpc} | R Documentation |
Plots pre-validation results from plotcv, to aid in choosing best threshold
superpc.plotcv(object, cv.type=c("full","preval"),smooth = TRUE, smooth.df = 10, call.win.metafile=FALSE, ...)
object |
Object returned by superpc.cv |
cv.type |
Type of cross-validation used- "full" (Default; this is "standard" cross-validation; recommended) and "preval"- pre-validation |
smooth |
Should plot be smoothed? Only relevant to "preval". Default FALSE. |
smooth.df |
Degrees of freedom for smooth.spline, default 10. If NULL, then degrees of freedom is estimated by cross-validation. |
call.win.metafile |
Ignore: for use by PAM Excel program |
... |
Additional plotting args to be passed to matplot |
Eric Bair and Robert Tibshirani
set.seed(332) x<-matrix(rnorm(1000*40),ncol=40) y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40) censoring.status<- sample(c(rep(1,30),rep(0,10))) featurenames <- paste("feature",as.character(1:1000),sep="") data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames) a<- superpc.train(data, type="survival") aa<-superpc.cv(a,data) superpc.plotcv(aa)