Comments (2)
The problem is that if we implement the suggestion in #11 (which I think is a good idea for now), we're not going to have any way for users to exclude the random intercepts in cases where they don't want them. So I suggest that for now, we force explicit inclusion of the intercepts (and make sure this is clearly documented), and then we can revisit this later when we build a parser that can handle operators like +
and -
. Does that work?
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Yes, requiring explicit inclusion of the intercept as a policy works for me.
I do note however that this is slightly inconsistent with what happens when we add random slopes for a categorical predictor --something like condition|subject
-- since currently this will add random subject means for all levels of condition, which is essentially a different parameterization of adding a random subject intercept and k-1 random condition contrasts. In other words, X|subject
adds two random terms to the model if X is categorical, but only one random term to the model is X is continuous. I don't really think this is a big problem, just a slight inconsistency.
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Related Issues (20)
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