[R] interaction effects in probits

[Tobias Muhlhofer]

Dear R-listers,

I am trying to compute interaction effects in a probit model, and
conduct hypothesis tests on these effects correctly.

Specifically, I have a model of the form y = a + b1 m + b2 x + b3 m*x,
where both y and m are 0-1 dummies, x is continuous, and I am
interested in the sign and statistical significance of the marginal
effect for observations for which m=1, over the base (obs for which
m=0). Then, I also want to conduct a test of the hypothesis H0: b2 +
b3 = 0.

As we know, probits are non-linear models, and so coefficients of
interaction variables do not carry the usual interpretation of
marginal effects over the base. Instead, I think what one needs to do
is compute the cross-derivative of y with respect to x and m.

Is there an efficient way of doing this in R preferably after having
estimated this model through glm(<formula>, family = binomial(link =
probit))? Can anyone suggest any code?

Thanks!

-- 
 Dr. Tobias Mühlhofer
 Assistant Professor
 Department of Finance
 Kelley School of Business
 Indiana University
 Tel: +1 812 855 9270
 http://tobias.muhlhofer.com