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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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")
``` |

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