Produces a plot of average effects for one variable while holding the others constant at observed values.

1 | ```
mnlAveEffPlot(obj, varname, data, R = 1500, nvals = 25, plot = TRUE, ...)
``` |

`obj` |
An object of class |

`varname` |
A string indicating the variable for which the plot is desired. |

`data` |
The data used to estimate |

`R` |
Number of simulations used to generate confidence bounds. |

`nvals` |
Number of evaluation points for the predicted probabilities. |

`plot` |
Logical indicating whether a plot should be produced (if |

`...` |
Other arguments to be passed down to |

Either a plot or a data frame with variables

`mean` |
The average effect (i.e., predicted probability) |

`lower` |
The lower 95% confidence bound |

`upper` |
The upper 95% confidence bound |

`y` |
The values of the dependent variable being predicted |

`x` |
The values of the independent variable being manipulated |

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

Hanmer, M.J. and K.O. Kalkan. 2013. ‘Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable Models’. American Journal of Political Science. 57(1): 263-277.

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