Average Effects for Multinomial Logistic Regression Models

Calculates average effects of a variable in multinomial logistic regression holding all other variables at observed values.

mnlChange2(obj, varnames, data, diffchange = c("unit", "sd"), R = 1500)

Arguments

obj	An object of class multinom
varnames	A string identifying the variable to be manipulated.
data	Data frame used to fit object.
diffchange	A string indicating the difference in predictor values to calculate the discrete change. sd gives plus and minus one-half standard deviation change around the median and unit gives a plus and minus one-half unit change around the median.
R	Number of simulations.

Value

A list with elements:

mean

Average effect of the variable for each category of the dependent variable.

lower

Lower 95 percent confidence bound

upper

Upper 95 percent confidence bound

Examples

library(nnet)
data(france)
mnl.mod <- multinom(vote ~ age + male + retnat + lrself, data=france)
#> # 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 
#> converged
mnlChange2(mnl.mod, "lrself", data=france, )
#>        PCF     PS      Green   RPR     UDF    
#> lrself -0.042* -0.095*  0.001   0.079*  0.058*