For a binomial model, with probit link function:
model = glm(response~A+B+C, family = binomial("probit"), na.action = na.omit)
where A and B are continuous, C is a binary qualitative variable (sex, M or F). Coefficients of the model:
(Intercept) A B C -10.326460234 0.008913341 0.253518215 -0.291546432
Given C = sexF, where sexM is eliminated from the model matrix (I think this is to eliminate aliasing problems?), can the fitted probability for a combination with C = sexM (coefficient not provided) be obtained using the difference between the intercept and C coefficients (-10.03491) and applying a formula such as:
$$-10.326 +0.0089x + 0.254x -0.292x= probit(P) $$
I'm sorry if my explanation is unclear, I'm studying GLMs for the first time and trying to get to grips with an exercise Q.