# superpc v1.09

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## Supervised principal components

Supervised principal components for regression and
survival analsysis. Especially useful for high-dimnesional
data, including microarray data.

## Functions in superpc

Name | Description | |

superpc.decorrelate | Decorrelate features with respect to competing predictors | |

superpc.plot.lrtest | Plot likelhiood ratio test statistics | |

superpc.plotred.lrtest | Plot likelihood ratio test statistics from supervised principal components predictor | |

superpc-internal | Internal superpc functions | |

superpc.rainbowplot | Make rainbow plot of superpc and compeiting predictors | |

superpc.predict.red | Feature selection for supervised principal components | |

superpc.predict.red.cv | Cross-validation of feature selection for supervised principal components | |

superpc.plotcv | Plot output from superpc.cv | |

superpc.predictionplot | Plot outcome predictions from superpc | |

superpc.predict | Form principal components predictor from a trained superpc object | |

superpc.fit.to.outcome | Fit predictive model using outcome of supervised principal components | |

superpc.listfeatures | Return a list of the important predictors | |

superpc.cv | Cross-validation for supervised principal components | |

superpc.lrtest.curv | Compute values of likelihood ratio test from supervised principal components fit | |

superpc.train | Prediction by supervised principal components | |

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## Details

LazyLoad | false |

LazyData | false |

License | GPL-2 |

URL | http://www-stat.stanford.edu/~tibs/superpc |

Packaged | 2012-02-26 20:06:30 UTC; tibs |

Repository | CRAN |

Date/Publication | 2012-02-27 07:36:05 |

depends | survival |

Contributors | Eric Bair, R. Tibshirani |

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