superpc.fit.to.outcome {superpc} | R Documentation |
Fit predictive model using outcome of supervised principal components, via either coxph (for surival data) or lm (for regression data)
superpc.fit.to.outcome(fit, data.test, score, competing.predictors = NULL, print=TRUE, iter.max = 5)
fit |
Object returned by superpc.train |
data.test |
Data object for prediction. Same form as data object documented in superpc.train. |
score |
Supervised principal component score, from superpc.predict |
competing.predictors |
Optional- list of competing predictors to be included in the model |
print |
Should a summary of the fit be printed? Default TRUE |
iter.max |
Max number of iterations used in predictive model fit. Default 5. Currently only relevant for Cox PH model |
Returns summary of coxph or lm fit
~~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") superpc.fit.to.outcome(a, data, fit$v.pred)