R twitter, trying to make sure I've understood LMER correctly. I have a simple design with a subjective measure, and 2 within subject factors (testing session 1-3, and nback level 0-1). I'm trying to test for measure ~ nback x session effects while appropriately modelling RFX.
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I specified my full model like so: Factor2 ~ nBack*session + (1 + nBack|subject) + (1 + nBack|session)
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For model comparison, I then iteratively build up the fixed effects and do anova(m1,m2,m3,m4).pic.twitter.com/jdNjxXKB8K
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I'm trying to clarify if I've specified my random slopes and intercepts in a sensible way. Is this model overdetermined? Sometimes the base model without fixed effects (m1) fails to converge.
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I've based my model off Bodo Witner's tutorial, but i'm still a bit confused about how the RFX slopes are specified. I wish there was a design matrix I could visualize or something, as the call is a bit abstract to me. I think I get the theory.
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Paging Dr @samhforbes — I think he can help. 
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