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Last data update: 2014.03.03

R: Functions for Estimating Interaction Effects in Logit and...

intEff

R Documentation

Functions for Estimating Interaction Effects in Logit and Probit Models

Description

Norton and Ai (2003) and Norton, Wang and Ai (2004) discuss methods for calculating the appropriate marginal effects for interactions in binary logit/probit models. These functions are direct translations of the Norton, Wang and Ai (2004) Stata code.

Usage

	intEff(obj, vars, data)

Arguments

`obj`	A binary logit or probit model estimated with `glm`.
`vars`	A vector of the two variables involved in the interaction.
`data`	A data frame used in the call to `obj`.

Value

A data frame with the following variable:

`int_eff`	The correctly calucalted marginal effect.
`linear`	The incorrectly calculated marginal effect following the linear model analogy.
`phat`	Predicted Pr(Y=1\|X).
`se_int_eff`	Standard error of `int_eff`.
`zstat`	The interaction effect divided by its standard error

Author(s)

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

References

Norton, Edward C., Hua Wang and Chunrong Ai. 2004. Computing Interaction Effects and Standard Errors in Logit and Probit Models. The Stata Journal 4(2): 154-167.

Ai, Chunrong and Edward C. Norton. 2003. Interaction Terms in Logit and Probit Models. Economics Letters 80(1): 123-129.

Norton, Edward C., Hua Wang and Chunrong Ai. 2004. inteff: Computing Interaction Effects and Standard Errors in Logit and Probit Models, Stata Code. http://www.stata-journal.com/software/sj4-3/.

Examples

data(france)
mod <- glm(voteleft ~ age*lrself + retnat + male, data=france, family=binomial)
out <- intEff(obj=mod, vars=c("age", "lrself"), data=france)
plot(out$phat, out$int_eff, xlab="Predicted Pr(Y=1|X)", 
	ylab = "Interaction Effect")
ag <- aggregate(out$linear, list(out$phat), mean)
lines(ag[,1], ag[,2], lty=2, col="red", lwd=2)
legend("topright", c("Correct Marginal Effect", "Linear Marginal Effect"), 
	pch=c(1, NA), lty=c(NA, 2), col=c("black", "red"), lwd=c(NA, 2), inset=.01)

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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Type 'contributors()' for more information and
'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/intEff.Rd_%03d_medium.png", width=480, height=480)
> ### Name: intEff
> ### Title: Functions for Estimating Interaction Effects in Logit and Probit
> ###   Models
> ### Aliases: intEff
> 
> ### ** Examples
> 
> data(france)
> mod <- glm(voteleft ~ age*lrself + retnat + male, data=france, family=binomial)
> out <- intEff(obj=mod, vars=c("age", "lrself"), data=france)
> plot(out$phat, out$int_eff, xlab="Predicted Pr(Y=1|X)", 
+ 	ylab = "Interaction Effect")
> ag <- aggregate(out$linear, list(out$phat), mean)
> lines(ag[,1], ag[,2], lty=2, col="red", lwd=2)
> legend("topright", c("Correct Marginal Effect", "Linear Marginal Effect"), 
+ 	pch=c(1, NA), lty=c(NA, 2), col=c("black", "red"), lwd=c(NA, 2), inset=.01)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>