Average Effects Plot for Multinomial Logistic Regression

Share:

Description

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

Usage

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

Arguments

obj

An object of class multinom.

varname

A string indicating the variable for which the plot is desired.

data

The data used to estimate obj.

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 TRUE) or numerical results should be returned (if FALSE).

...

Other arguments to be passed down to xyplot.

Value

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

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
library(nnet)
data(france)
mnl.mod <- multinom(vote ~ age + male + retnat + lrself, data=france)
## Not run: mnlAveEffPlot(mnl.mod, "lrself", data=france)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.