[R-sig-ME] GLMM & lack of linearity on the logit

[Luciano La Sala]

Dear R-people, 

I have just received from reviewers of a manuscript some harsh comments on
the statistical procedures. I'm studying risk factors of mortality at the
nest level among Olrog's Gull nest mates, which is why I used mixed models
with random intercepts (Nest ID). The outcome of interest if "Death"
(yes/no) and one of my explanatory variables is "Egg Volume" (continuous).
Since violation of linearity on the logit was evident I created 4 categories
using the quartiles of the distribution and modeled them as dummies.     

However, one reviewer stated: "It is unclear why you used volume of eggs as
a factor (i.e. categorized variable) in the analyses. Incorporating this
predictor as a continuous variable, as was originally measured, would make
analysis more informative. You stated that you made so "to relax the
linearity assumption". GLMM are sufficiently robust to accept a continuous
variable into a categorized model that, with the correct link function and
the variable transformation, would support well the linearity assumption." 

That said, I wonder if (1) categorization is such a bad thing on the one
hand, and (2) lack of linearity on the logit scale can be handled well by
GLMM.

In my case, adding quadratic and cubic terms after assessment of the shape
of the x-y relationship did not improve the fit, so I decided to use dummies
and thus relax the linearity assumption. 

Thank you very much in advance. 

Luciano