superpc (version 1.09)

superpc.lrtest.curv: 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

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)