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

Converts multinomial logit data into a combination of several binary logit data sets, in order to analyze it via the Begg & Gray approximation using a binary logistic regression.

1 2 | ```
mlogit2logit(f, data, choices = NULL, base.choice = 1,
varying = NULL, sep = ".")
``` |

`f` |
Formula as described in Details of |

`data` |
Data frame containing the variables of the model. |

`choices` |
Vector of names of alternatives. If it is not given, it is determined from the response column of the data frame. Values of this vector should match or be a subset of those in the response column. If it is a subset, |

`base.choice` |
Index of the base alternative within the vector |

`varying` |
Indices of variables within |

`sep` |
Separator of variable name and alternative name in the ‘varying’ variables. |

Details of the conversion algorithm are described in the vignette of this package, see `vignette('conversion')`

.

List with components:

`data` |
Converted data set. |

`formula` |
Formula to be used with the converted data set. |

`nobs` |
Number of observations in the original data set. |

`z.index` |
Index of all |

`z.names` |
Names of the |

`zcols` |
List in which each element corresponds to any of the |

`choices` |
Vector of names of the alternatives. |

`choice.main.intercept` |
Index of alternative within |

This function is called from within the `bic.mlogit`

and thus usually will not need to be called explicitly.

Hana Sevcikova

Begg, C.B., Gray, R. (1984) Calculation of polychotomous logistic regression parameters using individualized regressions. Biometrika **71**, 11–18.

Yeung, K.Y., Bumgarner, R.E., Raftery, A.E. (2005) Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics **21** (10), 2394–2402.

1 2 3 4 5 |

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