Description Usage Arguments Details Value Author(s) References See Also Examples

This function overloads the `glm`

function so that a check for the existence of the maximum likelihood estimate is computed before fitting a ‘glm’ with a binary response.

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The arguments are identical to the arguments of the `glm`

function provided in the ‘stats’ package with the exception of

`separation` |
either “find” or “test”. Both options prevent the model from being fit to binary data when the maximum likelihood estimate does not exist. Additionally, when |

The following arguments are passed to the `glm`

function:

`formula` |
see |

`family` |
see |

`data` |
see |

`weights` |
see |

`subset` |
see |

`na.action` |
see |

`start` |
see |

`etastart` |
see |

`mustart` |
see |

`offset` |
see |

`control` |
see |

`model` |
see |

`method` |
see |

`x` |
see |

`y` |
see |

`contrasts` |
see |

`...` |
see |

This function checks for the existence of the maximum likelihood estimate before the ‘glm’ function is used to fit binary regression models by solving the linear program proposed in Konis (2007).

See the return value for the `glm`

function.

Kjell Konis kjell.konis@epfl.ch

Kjell Konis (2007). Linear programming algorithms for detecting separated data in binary logistic regression models. DPhil, University of Oxford http://ora.ouls.ox.ac.uk/objects/uuid:8f9ee0d0-d78e-4101-9ab4-f9cbceed2a2a

`glm`

.

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