I am running a probit glmer, with a binary response varaible and a categorical explanatory variable with three dummy levels and have tried to calculate the marginal effect using the following code:
ProbitScalar<- mean(dnorm(predict(m1,type = "link")))
The ProbitScalar value is then multiplied by the coefficient estimates from the regression output.
I get the following values:
-0.2946806 (referring to the intercept and reference level) -0.1527443 -0.07252501
I am slightly confused how to interpret them as they seem quite low compared to what I would expect from the raw data.
Is it correct that the second variable has a 15% lower chance of achieving success (the binary response variable) than the reference group and the final variable has a 7% less chance of achieving success than the reference group?