Interpreting output from lmer

Question

This probably has been asked many a times, but I cannot find the answer. I'm trying to interpret the output that I get from lmer. My code is as follows:

Model1<- lmer(DV ~ Eduy_IMP + Gender + Age + APOEdich_IMP + 
     predictor + Time:Factor2:Factor1*predictor + 
     (1 + Time|Study), Data)
summary(Model1, ddf="Kenward-Roger")

DV = Dependant variable; Factor2 has levels 1,2,3; Factor1 has levels 0, 1.

Here is my Output:

My Question: I need to be able to report a coefficient for the predictor in the following groups: Predictor in each of the individual levels of Factor1 and Factor2. How do I get this? I even tried having different data sets and running the above code, but I cannot get a coefficient for these.

My aim is to see the influence of the predictor on the DV in Factor1 and Factor2.

Answer 1

I think in order to get there you would have to include the factors as main effects themselves. Currently, you only use factors in an interaction term. I am not even sure if this is sound (including an interaction without the corresponding main effects), but I am no expert. Nevertheless, this is why you only get coefficients for the interaction or more specifically for the different contrasts.

Model1 <- lmer(DV ~ Eduy_IMP + Gender + Age  + Factor1 + 
            Factor2 + APOEdich_IMP + predictor + 
            Time:Factor2:Factor1*predictor + 
            (1 + Time|Study), Data)
summary(Model1, ddf="Kenward-Roger")

Answer 2

It appears you are using “:” or colons for the interaction between Factor1 and Factor2. In R, that will estimate the interaction only but not the main/conditional effects. To estimate both the main/conditional effects and the interaction, use the asterisk or “*”