R: Cross-validation of feature selection for supervised...

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

### 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 1.09 Index]