superpc.predict.red.cv {superpc}R Documentation

Cross-validation of feature selection for supervised principal components

Description

Applies superpc.predict.red to cross-validation folds generates in superpc.cv. Uses the output to evaluate reduced models, and compare them to the full supervised principal components predictor.

Usage

superpc.predict.red.cv(fitred, fitcv, data, threshold, sign.wt="both")

Arguments

fitred Output of superpc.predict.red
fitcv Output of superpc.cv
data Training data object
threshold Feature score threshold; usually estimated from superpc.cv
sign.wt Signs of feature weights allowed: "both", "pos", or "neg"

Value

lrtest.reduced Likelihood ratio tests for reduced models
components Number of supervised principal components used
v.preval.red Outcome predictor from reduced models. Array of num.reduced.models by (number of test observations)
type Type of outcome
call calling sequence

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*40),ncol=40)
y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
censoring.status<- sample(c(rep(1,30),rep(0,10)))
censoring.status.test<- sample(c(rep(1,30),rep(0,10)))

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")
aa<-superpc.cv(a, data)

fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")

fit.red<- superpc.predict.red(a,data, data.test, threshold= .6)

fit.redcv<- superpc.predict.red.cv(fit.red, aa,  data, threshold= .6)

superpc.plotred.lrtest(fit.redcv)


[Package superpc version 1.05 Index]