Cross-validated linear discriminant calculations determine the optimum number of features. Test and training scores from successive cross-validation steps determine, via a principal components calculation, a low-dimensional global space onto which test scores are projected, in order to plot them. Further functions are included that serve didactic purposes.

Version: | 0.56 |

Depends: | R (≥ 3.0.0) |

Imports: | MASS, multtest |

Suggests: | knitr |

Published: | 2013-12-05 |

Author: | John Maindonald |

Maintainer: | John Maindonald <john.maindonald at anu.edu.au> |

License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |

URL: | http://www.maths.anu.edu.au/~johnm |

NeedsCompilation: | no |

Citation: | hddplot citation info |

Materials: | README |

In views: | Multivariate |

CRAN checks: | hddplot results |

Reference manual: | hddplot.pdf |

Vignettes: |
Feature Selection Bias in Classification of High Dimensional Data |

Package source: | hddplot_0.56.tar.gz |

MacOS X binary: | hddplot_0.56.tgz |

Windows binary: | hddplot_0.56.zip |

Old sources: | hddplot archive |