superpc.plotcv: Plot output from superpc.cv

Description Usage Arguments Author(s) References Examples

Description

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

Usage

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    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)

Maintainer: "Jean-Eudes Dazard, Ph.D."

References

Examples

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## Not run: 
set.seed(332)

#generate some data
x <- matrix(rnorm(50*30), ncol=30)
y <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
censoring.status <- sample(c(rep(1,20), rep(0,10)))

featurenames <- paste("feature", as.character(1:50), 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)

## End(Not run)

superpc documentation built on Oct. 24, 2020, 1:07 a.m.