superpc.train {superpc} | R Documentation |
Does prediction of a quantitative regression or survival outcome, by the supervised principal components method.
superpc.train(data, type = c("survival", "regression"), s0.perc=NULL)
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) |
Compute wald scores for each feature (gene), for later use in superpc.predict and superpc.cv
gene.scores=gene.scores, type=type, call = this.call
feature.scores |
Score for each feature (gene) |
type |
problem type |
call |
calling sequence |
Eric Bair and Robert Tibshirani
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
#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")