superpc.train: Prediction by supervised principal components

Description Usage Arguments Details Value Author(s) References Examples

Description

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

Usage

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    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)

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

References

Examples

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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")

superpc documentation built on Oct. 24, 2020, 1:07 a.m.