superpc.plotcv {superpc}

Plot output from superpc.cv

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, Ph.D."

• "Jean-Eudes Dazard, Ph.D."

• "Rob Tibshirani, Ph.D."

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

References

• E. Bair and R. Tibshirani (2004). "Semi-supervised methods to predict patient survival from gene expression data." PLoS Biol, 2(4):e108.

• E. Bair, T. Hastie, D. Paul, and R. Tibshirani (2006). "Prediction by supervised principal components." J. Am. Stat. Assoc., 101(473):119-137.

Examples

## 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)