The optimal linear regression olr(), runs all the possible combinations of linear regression equations. The olr() returns the equation which has the greatest adjusted R-squared term or the greatest R-squared term based on the user's discretion. Essentially, the olr() returns the best fit equation out of all the possible equations. R-squared increases with the addition of an explanatory variable whether it is 'significant' or not, thus this was developed to eliminate that conundrum. Adjusted R-squared is preferred to overcome this phenomenon, but each combination will still produce different results and this will return the best one. Complimentary functions are included which list all of the equations, all of the equations in ascending order, a function to give the user a specific model's summary, and the list of adjusted R-squared terms & R-squared terms. A 'Python' version is available at: <https://pypi.org/project/olr/>.

Version: | 1.1 |

Depends: | R (≥ 2.10) |

Imports: | plyr, utils, stats |

Published: | 2020-01-08 |

Author: | Mathew Fok |

Maintainer: | Mathew Fok <mfok at stevens.edu> |

License: | GPL-3 |

URL: | https://github.com/MatHatter, https://pypi.org/project/olr/ |

NeedsCompilation: | no |

CRAN checks: | olr results |

Reference manual: | olr.pdf |

Package source: | olr_1.1.tar.gz |

Windows binaries: | r-devel: olr_1.1.zip, r-release: olr_1.1.zip, r-oldrel: olr_1.1.zip |

macOS binaries: | r-release: olr_1.1.tgz, r-oldrel: olr_1.1.tgz |

Old sources: | olr archive |

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