superpc.train: Prediction by supervised principal components
In superpc: Supervised Principal Components

Description Usage Arguments Details Value Author(s) References Examples

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

1
2
3

    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

`feature.scores`	Score for each feature (gene)
`type`	problem type
`s0.perc`	Factor for denominator of score statistic
`call`	calling sequence

"Eric Bair, Ph.D."
"Jean-Eudes Dazard, Ph.D."
"Rob Tibshirani, Ph.D."

Maintainer: "Jean-Eudes Dazard, Ph.D."

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.

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")