## moose: Mean Squared Out-of-Sample Error Projection

Projects mean squared out-of-sample error for a linear regression based upon the methodology developed in Rohlfs (2022) <doi:10.48550/arXiv.2209.01493>. It consumes as inputs the lm object from an estimated OLS regression (based on the "training sample") and a data.frame of out-of-sample cases (the "test sample") that have non-missing values for the same predictors. The test sample may or may not include data on the outcome variable; if it does, that variable is not used. The aim of the exercise is to project what what mean squared out-of-sample error can be expected given the predictor values supplied in the test sample. Output consists of a list of three elements: the projected mean squared out-of-sample error, the projected out-of-sample R-squared, and a vector of out-of-sample "hat" or "leverage" values, as defined in the paper.

Version: |
0.0.1 |

Published: |
2022-09-09 |

Author: |
Chris Rohlfs
[aut, cre] |

Maintainer: |
Chris Rohlfs <car2228 at columbia.edu> |

License: |
MIT + file LICENSE |

NeedsCompilation: |
no |

CRAN checks: |
moose results |

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