A marginal effect is the effect one independent variable on the dependent variable has when it is changed by one unit and the other independent variables constant. In the simple OLS regression correspond to the marginal effects the values of the regression coefficients (beta-values).
How do I calculate these for other GLMs, particular I am interested in Gamma (log link) and Lognormal GLMs.
I read that I can multiply the estimated GLM coefficients by the probability density function of the linked distribution (which is the derivative of the cumulative density function).
So in a simplified R example this would be:
df <- data.frame(y= abs(rnorm(20)), v1= rnorm(100), v2=rnorm(100)) m <- glm(y~., family=Gamma(link=log), data=df) # marginal effect me <- coef(m) * mean(d__<WHAT DIST HERE>__(predict(m, type="link"))
Another question, in simple OLS the betas are the marginal effects, is this true for a Tobit model as well?