superpc (version 1.09)

superpc.fit.to.outcome: Fit predictive model using outcome of supervised principal components

Description

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

Usage

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

References

~put references to the literature/web site here ~

Examples

Run this code
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)

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