superpc.lrtest.curv: Compute values of likelihood ratio test from supervised...

In superpc: Supervised Principal Components

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)

superpc documentation built on Oct. 24, 2020, 1:07 a.m.