R: Fit predictive model using outcome of supervised principal...

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

### 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 1.09 Index]