Fit predictive model using outcome of supervised principal components

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Description

Fit predictive model using outcome of supervised principal components, via either coxph (for surival data) or lm (for regression data)

Usage

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superpc.fit.to.outcome(fit, data.test, score, competing.predictors = NULL, print=TRUE, iter.max = 5)

Arguments

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

Value

Returns summary of coxph or lm fit

Author(s)

Eric Bair and Robert Tibshirani

References

~put references to the literature/web site here ~

Examples

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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)