superpc.lrtest.curv {superpc}R Documentation

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 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

Note

~~further notes~~

Author(s)

Eric Bair and Robert Tibshirani

References

~put references to the literature/web site here ~

Examples

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)

[Package superpc version 1.05 Index]