[R-sig-ME] single argument anova for GLMMs (really, glmer, or dispersion?)
A.Robinson at ms.unimelb.edu.au
Sat Dec 13 22:14:16 CET 2008
On Sat, Dec 13, 2008 at 12:00:07PM -0500, Ben Bolker wrote:
> > 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 ...
Yes indeed :), you are correct. But, that's a problem that bedevils
glms too. I was more thinking along the lines: when would it be
inadvisable to use a transformation and explicit variance model
instead of a glm?
Department of Mathematics and Statistics Tel: +61-3-8344-6410
University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599
More information about the R-sig-mixed-models