[R] Interpretation of ranef output
Ben Bolker
bbolker at gmail.com
Tue Dec 11 21:38:34 CET 2012
Ginnie D Morrison <ginn <at> utexas.edu> writes:
> I'm running a generalized linear model
Note that this is a generalized linear *mixed* model, which
complicates the situation somewhat (otherwise you wouldn't
be dealing with random effects). Presumably you are using
glmer from the lme4 package, but it would be good to state that
explicitly ...
> and am interested in using the
> random effects that are output for further analysis. My random effect is
> interacting with two different fixed effects (which which are factors with
> two levels each). When I retrieve the random effects I get something like
> this:
> (Intercept) nutrient (Intercept) light (Intercept)
> Aa-0 0.59679192 0.34824858 2.241424479 -2.335037842 0.775359364
> Ag-0 -2.73719135 2.10428715 -0.980046942 -1.832587350 -1.938942967
> Ak-1 -0.13221525 1.03282635 -0.624239559 1.594044342 0.313188938
> Alc-0 1.16506640 -0.05914007 1.964024728 -1.133954211 1.190791025
> ALL1-2 -1.06702524 -0.55728016 -0.019232427 -2.389703709 -1.357733079
> Alst-1 -1.59281754 0.23968834 -1.506568899 -0.280839171 -1.558589679
> Amel-1 0.46083213 -0.02787653 0.885634543 -0.632460142 0.469048098
>
> So...3 intercepts and two values for the interaction terms.
You should probably specify your random effect as
... (1|grpFactor) + (0 + nutrient|grpFactor) + (0 + light|grpFactor)
to avoid having the intercept terms estimated separately ... my guess
is that the model is overspecified.
> My questions are:
> Which one is the straight-up intercept for the random term itself?
> How do I get the predictor/estimate (basically, the BLUP) for the different
> interaction levels?
In the same way that you would combine coefficients from a standard
linear model to get predicted values for specific cases. We probably
need a little more context about the experimental design.
Followups to r-sig-mixed-models at r-project.org, please ...
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