Plot likelihood ratio test statistics from supervised principal components predictor
Plot likelihood ratio test statistics from supervised principal components predictor
superpc.plotred.lrtest(object.lrtestred,
call.win.metafile=FALSE)object.lrtestred |
Output from either superpc.predict.red or superpc.predict.redcv |
call.win.metafile |
Used only by PAM Excel interface call to function |
"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,])$v[,1] + .1*rnorm(30)
ytest <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
censoring.status <- sample(c(rep(1,20), rep(0,10)))
censoring.status.test <- 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)
data.test <- list(x=x,
y=ytest,
censoring.status=censoring.status.test,
featurenames=featurenames)
a <- superpc.train(data, type="survival")
aa <- superpc.cv(a, data)
fit.red <- superpc.predict.red(a,
data,
data.test,
.6)
fit.redcv <- superpc.predict.red.cv(fit.red,
aa,
data,
.6)
superpc.plotred.lrtest(fit.redcv)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.