superpc.fit.to.outcome {superpc}R Documentation

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

Note

~~further notes~~

Author(s)

Eric Bair and Robert Tibshirani

References

~put references to the literature/web site here ~

Examples

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)

[Package superpc version 1.05 Index]