superpc.rainbowplot {superpc} | R Documentation |
Makes a heatmap display of outcome predictions from superpc, along with expected survival time, and values of competing predictors
superpc.rainbowplot(data, pred, sample.labels, competing.predictors, call.win.metafile=FALSE)
data |
List of (test) data, of form described in superpc.train documentation |
pred |
Superpc score from superpc.predict or superpc.predict.red |
sample.labels |
Vector of sample labels of test data |
competing.predictors |
List of competing predictors to be plotted |
call.win.metafile |
Used only by Excel interface call to function |
Any censored survival times are estimated by E(T|T>C), where $C$ is the observed censoring time and the Kaplan-Meier estimate from the training set is used to estimate the expectation.
Eric Bair and Robert Tibshirani
set.seed(332) x<-matrix(rnorm(1000*40),ncol=40) y<-10+svd(x[1:60,])$v[,1]+ 5*rnorm(40) censoring.status<- sample(c(rep(1,30),rep(0,10))) ytest<- 10+svd(x[1:60,])$v[,1]+ 5*rnorm(40) censoring.status.test<- sample(c(rep(1,30),rep(0,10))) competing.predictors.test=list(pred1=rnorm(40), pred2=as.factor(sample(c(1,2),replace =TRUE,size=40))) 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) sample.labels=paste("te",as.character(1:40),sep="") a<- superpc.train(data, type="survival") pred=superpc.predict(a,data,data.test,threshold=.25, n.components=1)$v.pred superpc.rainbowplot(data,pred, sample.labels,competing.predictors=competing.predictors.test)