Your problem seems to be exactly the problem I've been having for the past
week. So glad to find this thread.
However, when I follow the syntax you used to get around the issue, I still
didn't get the output to work in the way yours did. I don't believe my
factors are truly nested however (nor your factors: if A, B, C, and D occur
in both levels 1 and 2, the effects would be crossed).
I have a model with the variable condition, and the variable patient
(indicating whether the subject is a patient or control) with repeated
measures per subject, and the following is the model I had:
MLsd = lmer(H.y.~patient*stance*cond + (1|subj), data=H)
I want to be able to look at the same contrast set up you were interested
in, and compare patient to control at every level of the variable condition
(cond).
*When I tried using the syntax you used with my model:
lmer(H.y.~patient*stance*cond+(cond/patient) + (1|subj), data=H), I got this
result, which seems to be using condition 1 as a part of the baseline. Any
idea how to change that?*
Fixed effects:
Estimate Std. Error t value
(Intercept) 0.552594 0.012774 43.26
patient1 -0.005951 0.016989 -0.35
stance2 -0.009010 0.014577 -0.62
cond2 -0.023666 0.007028 -3.37
cond3 -0.015469 0.006948 -2.23
patient1:stance2 0.027988 0.019472 1.44
patient1:cond2 0.015981 0.009977 1.60
patient1:cond3 0.014622 0.009905 1.48
Best,
Rachel
(M.S. candidate frantically working to finish her master's project in the
coming week or so)
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