mnlAveEffPlot.Rd
Produces a plot of average effects for one variable while holding the others constant at observed values.
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
The average effect (i.e., predicted probability)
The lower 95% confidence bound
The upper 95% confidence bound
The values of the dependent variable being predicted
The values of the independent variable being manipulated
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.
#> # weights: 35 (24 variable) #> initial value 872.315349 #> iter 10 value 655.272636 #> iter 20 value 559.902797 #> iter 30 value 551.176433 #> final value 551.169697 #> convergedif (FALSE) mnlAveEffPlot(mnl.mod, "lrself", data=france)