superpc.lrtest.curv {superpc} | R Documentation |
Compute values of likelihood ratio test from supervised principal components fit
superpc.lrtest.curv(object, data, newdata, n.components = 1, threshold = NULL, n.threshold = 20)
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. |
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 |
~~further notes~~
Eric Bair and Robert Tibshirani
~put references to the literature/web site here ~
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)