# Maximal First Differences for Multinomial Logistic Regression Models

### Description

For objects of class `multinom`

, it calculates the change in predicted probabilities, for maximal discrete changes in all covariates holding all other variables constant at typical values.

### Usage

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### Arguments

`obj` |
A model object of class |

`data` |
Data frame used to fit |

`typical.dat` |
Data frame with a single row containing values at which to hold variables constant when calculating first differences. These values will be passed to |

`diffchange` |
A string indicating the difference in predictor values to calculate the discrete change. |

`sim` |
Logical indicating whether simulated confidence bounds should be produced. |

`R` |
Number of simulations to perform if |

### Details

The function calculates the changes in predicted probabilities for maximal discrete changes in the covariates for objects of class `multinom`

. This function works with polynomials specified with the `poly`

function. It also works with multiplicative interactions of the covariates by virtue of the fact that it holds all other variables at typical values. By default, typical values are the median for quantitative variables and the mode for factors. The way the function works with factors is a bit different. The function identifies the two most different levels of the factor and calculates the change in predictions for a change from the level with the smallest prediction to the level with the largest prediction.

### Value

A list with the following elements:

`diffs` |
A matrix of calculated first differences |

`minmax` |
A matrix of values that were used to calculate the predicted changes |

`minPred` |
A matrix of predicted probabilities when each variable is held at its minimum value, in turn. |

`maxPred` |
A matrix of predicted probabilities when each variable is held at its maximum value, in turn. |

### Author(s)

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

### Examples

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