superpc.predictionplot {superpc} | R Documentation |
Plots outcome predictions from superpc
superpc.predictionplot(train.obj, data, data.test, threshold, n.components=3, n.class=2, shrinkage=NULL, call.win.metafile=FALSE)
train.obj |
Object returned by superpc.train |
data |
List of training data, of form described in superpc.train documen tation, |
data.test |
List of test data; same form as training data |
threshold |
Threshold for scores: features with abs(score)>threshold are retained. |
n.components |
Number of principal components to compute. Should be 1,2 or 3. |
n.class |
Number of classes for survival stratification. Onply applicable for survival data. Default 2. |
shrinkage |
Shrinkage to be applied to feature loadings. Default is NULL meaning no shrinkage |
call.win.metafile |
Used only by Excel interface call to function |
Eric Bair and Robert Tibshirani
set.seed(332) x<-matrix(rnorm(1000*40),ncol=40) y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40) censoring.status<- sample(c(rep(1,30),rep(0,10))) featurenames <- paste("feature",as.character(1:1000),sep="") data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames) a<- superpc.train(data, type="survival") superpc.predictionplot(a,data,data,threshold=1)