Compute values of likelihood ratio test from supervised principal components fit

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Description

Compute values of likelihood ratio test from supervised principal components fit

Usage

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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 scoresL 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

If it is a LIST, use

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 and Robert Tibshirani

References

~put references to the literature/web site here ~

Examples

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set.seed(332)
#generate some data

x<-matrix(rnorm(1000*20),ncol=20)
y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
censoring.status<- sample(c(rep(1,17),rep(0,3)))
censoring.status.test<- sample(c(rep(1,17),rep(0,3)))

featurenames <- paste("feature",as.character(1:1000),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")

fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")


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