superpc (version 1.12)

superpc.plotred.lrtest: Plot likelihood ratio test statistics from supervised principal components predictor

Description

Plot likelihood ratio test statistics from supervised principal components predictor

Usage

superpc.plotred.lrtest(object.lrtestred, 
                           call.win.metafile=FALSE)

Arguments

object.lrtestred

Output from either superpc.predict.red or superpc.predict.redcv

call.win.metafile

Used only by PAM Excel interface call to function

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,])$v[,1] + .1*rnorm(30)
ytest <- 10 + svd(x[1:50,])$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)
# }

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