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Last data update: 2014.03.03

R: Average Effects for Multinomial Logistic Regression Models

mnlChange2

R Documentation

Average Effects for Multinomial Logistic Regression Models

Description

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

Usage

mnlChange2(obj, varname, data, change = c("unit", "sd"), R = 1500)

Arguments

`obj`	An object of class `multinom`
`varname`	A string identifying the variable to be manipulated.
`data`	Data frame used to fit `object`.
`change`	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

Author(s)

Dave Armstrong (UW-Milwaukee, Department of Political Science)

Examples

library(nnet)
data(france)
mnl.mod <- multinom(vote ~ age + male + retnat + lrself, data=france)
mnlChange2(mnl.mod, "lrself", data=france, )

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DAMisc)
Loading required package: car
Loading required package: effects

Attaching package: 'effects'

The following object is masked from 'package:car':

    Prestige

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DAMisc/mnlChange2.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mnlChange2
> ### Title: Average Effects for Multinomial Logistic Regression Models
> ### Aliases: mnlChange2
> 
> ### ** 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, )	
$mean
            lrself
PCF    0.046270032
PS     0.091429844
Green -0.001599002
RPR   -0.078909980
UDF   -0.057272165

$lower
           lrself
PCF    0.03184023
PS     0.07502256
Green -0.01030989
RPR   -0.08960933
UDF   -0.06920504

$upper
            lrself
PCF    0.060786815
PS     0.107269119
Green  0.007797836
RPR   -0.067364659
UDF   -0.046332067

> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>