Compute values of likelihood ratio test from supervised principal components fit
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 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. |
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 |
"Eric Bair, Ph.D."
"Jean-Eudes Dazard, Ph.D."
"Rob Tibshirani, Ph.D."
Maintainer: "Jean-Eudes Dazard, Ph.D."
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.