# Interpreting output from lmer

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.

• Hi, there are blind and visually impaired users of this site who interact with it using screen readers. The screen readers can't handle the equation in your screenshot. Please edit the post to include the equation as LaTeX. If it helps, we have some resources on using LaTeX on Cross Validated. Sep 14, 2021 at 15:33

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")


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 “*”