choice of reference category only changes coefficient with uncorrelated random intercept and slope
Not sure, but ... I think this is real. (If I were going to pursue it further I would probably try running some simulations.) I think the asymmetry you're seeing is most likely related to the nonlinearity inherent in a GLMM; if that's true, then the effect should go away if you were using a LMM instead of a GLMM ...
On Tue, Sep 5, 2017 at 7:45 PM, David Sidhu <dsidhu at ucalgary.ca> wrote:
Hi Everyone I have noticed something strange... I am running a glmer with a single dichotomous predictor (coded 1/0). The model also includes a random subject intercept, as well as a random item intercept and slope. Changing which level of the predictor serves as the reference category doesn?t change the absolute value of the coefficient, EXCEPT when the random intercept and slope are uncorrelated. This happens whether I keep the predictor as a numeric variable, or change the predictor into a factor and use the following code: t1<-glmer(DV~IV+(1|PPT)+(0+dummy(IV, "1")|Item)+(1|Item), data = data, family = "binomial?) Is this a genuine result? If so, can anyone explain why the uncorrelated random intercept and slope allow it to emerge? If not, how can I run a model that has an uncorrelated random intercept and slope that would prevent the choice of reference category from affecting the result? Thank you very much! Dave --- David M. Sidhu, MSc<http://davidmsidhu.com/> PhD Candidate Department of Psychology University of Calgary [[alternative HTML version deleted]]
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