R: Form principal components predictor from a trained superpc...

superpc.predict {superpc} | R Documentation

## Form principal components predictor from a trained superpc object

### Description

Computes supervised principal components, using scores from "object"

### Usage

    superpc.predict(object, data, newdata, threshold, n.components = 3, prediction.type = c("continuous", "discrete", "nonzero"), n.class = 2)

### Arguments

`object` | Obect returned by superpc.train
`data` | List of training data, of form described in superpc.train documentation,
`newdata` | List of test data; same form as training data
`threshold` | Threshold for scores: features with abs(score)>threshold are retained.
`n.components` | Number of principal components to compute. Should be 1,2 or 3.
`prediction.type` | "continuous" for raw principal component(s); "discrete" for principal component categorized in equal bins; "nonzero" for indices of features that pass the threshold
`n.class` | Number of classes into which predictor is binned (for prediction.type="discrete"

### Value

`v.pred` | Supervised principal componients predictor
`u` | U matrix from svd of feature matrix x
`d` | singual values from svd of feature matrix x
`which.features` | Indices of features exceeding threshold
`n.components` | Number of supervised principal components requested
`call` | calling sequence

### Author(s)

Eric Bair and Robert Tibshirani

### 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)

    plot(fit$v.pred,ytest)

[Package superpc version 1.09 Index]