Prediction by supervised principal components

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Description

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

Usage

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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

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#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")