superpc.train

0th

Percentile

Prediction by supervised principal components

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

Keywords
regression, survival
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
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.scoresScore for each feature (gene)
  • typeproblem type
  • callcalling sequence

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

Aliases
  • superpc.train
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")
Documentation reproduced from package superpc, version 1.08, License: GPL-2

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