Description Usage Arguments Details Value Author(s) References Examples

View source: R/DAMisc_functions.R

Calculates proportional reduction in error (PRE) and expected proportional reduction in error (epre) from Herron (1999).

1 |

`mod1` |
A model of class |

`mod2` |
A model of the same class as |

`sim` |
A logical argument indicating whether a parametric bootstrap should be used to calculate confidence bounds for (e)PRE. See |

`R` |
Number of bootstrap samples to be drawn if |

Proportional reduction in error is calculated as a function of correct and incorrect predictions (and the probabilities of correct and incorrect predictions for ePRE). When `sim=TRUE`

, a parametric bootstrap will be used that draws from the multivariate normal distribution centered at the coefficient estimates from the model and using the estimated variance-covariance matrix of the estimators as Sigma. This matrix is used to form `R`

versions of XB and predictions are made for each of the `R`

different versions of XB. Confidence intervals can then be created from the bootstrap sampled (e)PRE values.

An object of class `pre`

, which is a list with the following elements:

`pre` |
The proportional reduction in error |

`epre` |
The expected proportional reduction in error |

`m1form` |
The formula for model 1 |

`m2form` |
The formula for model 2 |

`pcp` |
The percent correctly predicted by model 1 |

`pmc` |
The percent correctly predicted by model 2 |

`epcp` |
The expected percent correctly predicted by model 1 |

`epmc` |
The expected percent correctly predicted by model 2 |

`pre.sim` |
A vector of bootstrapped PRE values if |

`epre.sim` |
A vector of bootstrapped ePRE values if |

Dave Armstrong (UW-Milwaukee, Department of Political Science)

Herron, M. 1999. Postestimation Uncertainty in Limited Dependent Variable Models. Political Analysis 8(1): 83–98.

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