I have a question about my use of a mixed model/lmer. The basic model is this:
lmer(DV ~ group * condition + (1|pptid), data= df)
Group and condition are both factors: group has two levels (groupA, groupB) and condition has three levels (condition1, condition2, condition3). It's data from human subjects, so pptid is a random effect for each person.
The model found the following with p value output:
Estimate MCMCmean HPD95lower HPD95upper pMCMC Pr(>|t|) (Intercept) 6.1372 6.1367 6.0418 6.2299 0.0005 0.0000 groupB -0.0614 -0.0602 -0.1941 0.0706 0.3820 0.3880 condition2 0.1150 0.1151 0.0800 0.1497 0.0005 0.0000 condition3 0.1000 0.1004 0.0633 0.1337 0.0005 0.0000 groupB:condition2 -0.1055 -0.1058 -0.1583 -0.0610 0.0005 0.0000 groupB:condition3 -0.0609 -0.0612 -0.1134 -0.0150 0.0170 0.0148
Now, I know that the rows listed compare each level of the factors to the reference level. For group, the reference is groupA and for condition, the reference is condition1.
Would I be correct in interpreting this output in the following way:
- No overall differences between the groups (hence groupB having a p of >.05)
- Overall differences between condition 1 and condition 2, and between condition 1 and condition 3.
- Differences between groupA, condition 1 versus groupB, condition 2 and also between groupA, condition 1 versus group B, condition 3.
Is that correct? I think I'm a little confused about how to interpret this with regards to interactions between levels of two different factors.
I've read various questions on here and done some web searches, and managed to get contrasts set up with glht: would that be a better way to look at the differences between the groups and conditions? I figured that would be the case given the signs of interactions being present here.