superpc.plotcv: Plot output from superpc.cv

In superpc: Supervised principal components

Description

Plots pre-validation results from plotcv, to aid in choosing best threshold

Usage

superpc.plotcv(object, cv.type=c("full","preval"),smooth = TRUE, smooth.df = 10, call.win.metafile=FALSE, ...)

Arguments

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

Author(s)

Eric Bair and Robert Tibshirani

Examples

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)

superpc documentation built on May 29, 2017, 3:43 p.m.

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