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 (UWMilwaukee, 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): 263277.
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