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

In davidaarmstrong/damisc: Dave Armstrong's Miscellaneous Functions

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 list is returned with two elements - byobs and atment. The byobs result gives the interaction effect evaluated at each observation. The atmean element has the marginal effect evaluated at the mean. Each eleement contains an element int which is 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

The X element of each returned result is the X-matrix used to generate the result.

Author(s)

Dave Armstrong

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.

Examples

data(france)
mod <- glm(voteleft ~ age*lrself + retnat + male, data=france, family=binomial)
out <- intEff(obj=mod, vars=c("age", "lrself"), data=france)
out <- out$byobs$int
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)

davidaarmstrong/damisc documentation built on Oct. 1, 2023, 3:05 p.m.