superpc (version 1.09)

superpc.predict.red.cv: 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.reducedLikelihood ratio tests for reduced models
  • componentsNumber of supervised principal components used
  • v.preval.redOutcome predictor from reduced models. Array of num.reduced.models by (number of test observations)
  • typeType of outcome
  • callcalling sequence

References

~put references to the literature/web site here ~

Examples

Run this code
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)

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