predict and multiplicative variables / interaction terms in probit regressions

Question

I want to determine the marginal effects of each dependent variable in a probit regression as follows:

• predict the (base) probability with the mean of each variable
• for each variable, predict the change in probability compared to the base probability if the variable takes the value of mean + 1x standard deviation of the variable

In one of my regressions, I have a multiplicative variable, as follows:

my_probit <- glm(a ~ b + c + I(b*c), family = binomial(link = "probit"), data=data)

Two questions:

1. When I determine the marginal effects using the approach above, will the value of the multiplicative term reflect the value of b or c taking the value mean + 1x standard deviation of the variable?
2. Same question, but with an interaction term (* and no I()) instead of a multiplicative term.

Many thanks

Answer 1

When interpreting the results of models involving interaction terms, the general rule is DO NOT interpret coefficients. The very presence of interactions means that the meaning of coefficients for terms will vary depending on the other variate values being used for prediction. The right way to go about looking at the results is to construct a "prediction grid", i.e. a set of values that are spaced across the range of interest (hopefully within the domain of data support). The two essential functions for this process are expand.grid and predict.

dgrid <- expand.grid(b=fivenum(data$b)[2:4], c=fivenum(data$c)[2:4]
# A grid with the upper and lower hinges and the medians for `a` and `b`.

predict(my_probit,  newdata=dgrid)

You may want to have the predictions on a scale other than the default (which is to return the linear predictor), so perhaps this would be easier to interpret if it were:

predict(my_probit,  newdata=dgrid, type ="response")

Be sure to read ?predict and ?predict.glm and work with some simple examples to make sure you are getting what you intended.

Predictions from models containing interactions (at least those involving 2 covariates) should be thought of as being surfaces or 2-d manifolds in three dimensions. (And for 3-covariate interactions as being iso-value envelopes.) The reason that non-interaction models can be decomposed into separate term "effects" is that the slopes of the planar prediction surfaces remain constant across all levels of input. Such is not the case with interactions, especially those with multiplicative and non-linear model structures. The graphical tools and insights that one picks up in a differential equations course can be productively applied here.