Plot output from superpc.cv
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, Ph.D."
"Jean-Eudes Dazard, Ph.D."
"Rob Tibshirani, Ph.D."
Maintainer: "Jean-Eudes Dazard, Ph.D."
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.
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.