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

Description

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

Usage

 `1` ```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

gene.scores=gene.scores, type=type, call = this.call

 `feature.scores ` Score for each feature (gene) `type ` problem type `call ` calling sequence

Author(s)

Eric Bair and Robert Tibshirani

References

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

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```#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") ```

superpc documentation built on May 29, 2017, 3:43 p.m.