# Plot Average Effects of Variables in Proportional Odds Logistic Regression

### Description

For objects of class `polr`

the function plots the average effect of a single variable holding all other variables at their observed values.

### Usage

1 2 |

### Arguments

`obj` |
An object of class |

`varname` |
A string providing the name of the variable for which you want the plot to be drawn. |

`data` |
Data used to estimate |

`R` |
Number of simulations to generate confidence intervals. |

`nvals` |
Number of evaluation points of the function |

`plot` |
Logical indicating whether or not the result should be plotted (if |

`returnInd` |
Logical indicating whether average individual probabilities should be returned. |

`returnMprob` |
Logical indicating whether marginal probabilities, averaged over individuals, should be returned. |

`...` |
Arguments passed down to the call to |

### Details

Following the advice of Hanmer and Kalkan (2013) the function calculates the average effect of a variable holding all other variables at observed values and then plots the result.

### Value

Either a plot or a list with a data frame containing the 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 |

and the elements Ind or Mprob, as requested.

### Author(s)

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

### References

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.

### Examples

1 2 3 4 |