I have this model:
model <- zelig(dv~(product*intervention), model = "negbin", data = data)
intervention has two levels: neutral(=0), treatment(=1)
product has two levels: product1(=0), product2(=1)
I build f_all to just have one factor with 4 groups for comparison analysis.
Thus I have 4 groups in f_all
My interaction hypothesis is that treatment only works for product2.
Zelig gives me my predicted significant interaction.
Yet, I need planned contrasts to test my specific hypothesis: c(-1,1,0,0) and c(0,0,1,-1)
I researched and found a description of doing this with multcomp on this page: post comparisons
The regression output shows my predicted interaction
(Intercept) 1.34223 0.08024 16.728 <2e-16 *** product 0.08747 0.08025 1.090 0.2757 intervention 0.07437 0.07731 0.962 0.3361 interaction 0.45645 0.22263 2.050 0.0403 *
However, it said multcomp and the glht function is for linear models, but I am using a negbin model.
3 Questions regarding this problem:
1. Can I do planned comparisons on my negbin model using multcomp?
2. If not what appropriate method is there to do this for my negbin model?
3. Based on R using treatment contrasts per default could I just interpret the interaction coefficient as the contrast comparing product2-neutral versus product2-treatment? Can I then interpret the intervention coefficient as contrast comparing product1-neutral versus product1-treament?