R: Prediction by supervised principal components

superpc.train {superpc} | R Documentation

## Prediction by supervised principal components

### Description

Does prediction of a quantitative regression or survival outcome, by the supervised principal components method.

### Usage

    superpc.train(data, type = c("survival", "regression"), s0.perc=NULL)

### Arguments

`data` | Data object with components x- p by n matrix of features, one observation per column; y- n-vector of outcome measurements; censoring.status- n-vector of censoring censoring.status (1= died or event occurred, 0=survived, or event was censored), needed for a censored survival outcome
`type` | Problem type: "survival" for censored survival outcome, or "regression" for simple quantitative outcome
`s0.perc` | Factor for denominator of score statistic, between 0 and 1: the percentile of standard deviation values added to the denominator. Default is 0.5 (the median)

### Details

Compute wald scores for each feature (gene), for later use in superpc.predict and superpc.cv

### Value

gene.scores=gene.scores, type=type, call = this.call

`feature.scores` | Score for each feature (gene)
`type` | problem type
`call` | calling sequence

### Author(s)

Eric Bair and Robert Tibshirani

### References

Bair E, Tibshirani R (2004) Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol 2004 April; 2 (4): e108; http://www-stat.stanford.edu/~tibs/superpc

### Examples

    #generate some example data
    set.seed(332)
    x<-matrix(rnorm(1000*40),ncol=40)
    y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
    censoring.status<- 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)

    a<- superpc.train(data, type="survival")

[Package superpc version 1.09 Index]