[R-sig-ME] single argument anova for GLMMs (really, glmer, or dispersion?)
bolker at ufl.edu
Sat Dec 13 18:00:07 CET 2008
> I think that this is fair enough and well put, John, but I'm going to
> push back in the other direction with a hypothetical example. Let's
> say that you have your over-dispersed count data. What do you lose if
> you simply take some convenient and credible transformation of the
> response variable and then use lme, paying close attention to your
> conditional distribution plots?
Besides the aesthetic preference for fully specified models etc.
(although there's also the danger of forgetting that "all models
are wrong etc." and believing the model too much), the most common
reason in ecological contexts for not being able to get away with
transformation is that the data are zero-rich (someone mentioned
zero-inflated/hurdle models earlier in this discussion, which
basically amounts to modeling presence/absence [either of
"structural" zeros or of all zero values] and conditional
density separately). There's nothing you can do to transform
a spike in the data (at zero or elsewhere) into anything
other than a spike ...
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