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

1 | ```
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

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

`feature.scores ` |
Score for each feature (gene) |

`type ` |
problem type |

`call ` |
calling sequence |

Eric Bair and Robert Tibshirani

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

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")
``` |

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