superpc.lrtest.curv {superpc}

Compute values of likelihood ratio test from supervised principal components fit

Description

Compute values of likelihood ratio test from supervised principal components fit

Usage

    superpc.lrtest.curv(object, 
                        data, 
                        newdata, 
                        n.components=1, 
                        threshold=NULL, 
                        n.threshold=20)

Arguments

object

Object returned by superpc.train.

data

List of training data, of form described in superpc.train documentation.

newdata

List of test data; same form as training data.

n.components

Number of principal components to compute. Should be 1,2 or 3.

threshold

Set of thresholds for scores; default is n.threshold values equally spaced over the range of the feature scores.

n.threshold

Number of thresholds to use; default 20. Should be 1,2 or 3.

Value

lrtest

Values of likelihood ratio test statistic

comp2

Description of 'comp2'

threshold

Thresholds used

num.features

Number of features exceeding threshold

type

Type of outcome variable

call

calling sequence

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

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.lrtest.curv(a, data, data.test)
#superpc.plot.lrtest(aa)