| 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
feature.scores |
Score for each feature (gene) |
type |
problem type |
s0.perc |
Factor for denominator of score statistic |
call |
calling sequence |
Author(s)
"Eric Bair, Ph.D."
"Jean-Eudes Dazard, Ph.D."
"Rob Tibshirani, Ph.D."
Maintainer: "Jean-Eudes Dazard, Ph.D."
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
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")