superpc (version 1.12)

superpc.train: 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)

Value

feature.scores

Score for each feature (gene)

type

problem type

s0.perc

Factor for denominator of score statistic

call

calling sequence

Details

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

References

• E. Bair and R. Tibshirani (2004). "Semi-supervised methods to predict patient survival from gene expression data." PLoS Biol, 2(4):e108.

• E. Bair, T. Hastie, D. Paul, and R. Tibshirani (2006). "Prediction by supervised principal components." J. Am. Stat. Assoc., 101(473):119-137.

Examples

# NOT RUN {
set.seed(332)

#generate some data
x <- matrix(rnorm(50*30), ncol=30)
y <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
censoring.status <- sample(c(rep(1,20), rep(0,10)))

featurenames <- paste("feature", as.character(1:50), sep="")
data <- list(x=x, 
             y=y, 
             censoring.status=censoring.status, 
             featurenames=featurenames)

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