mnlAveEffPlot: Average Effects Plot for Multinomial Logistic Regression

In davidaarmstrong/damisc: Dave Armstrong's Miscellaneous Functions

Average Effects Plot for Multinomial Logistic Regression

Description

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

Usage

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

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

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

davidaarmstrong/damisc documentation built on Oct. 1, 2023, 3:05 p.m.