[R-sig-ME] R2 for Negative Binomial calculated with GLMMADMB

[Ben Bolker]

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On 14-12-17 03:14 PM, Rolf Turner wrote:
> On 18/12/14 04:18, Jens Oldeland wrote:
>> Dear List-members,
>> 
>> recently, the R2 calculations for GLMMs invented by Schielzieth
>> and Nakagawa 2012 [1] were implemented into the MuMIn package.
>> This is incredibly good news, as many colleagues still require R2
>> to understand a model output.
> 
> I would suggest that if your colleagues require R2 to "understand"
> the output from a glmm model, then they neither understand glmm
> models nor R2.
> 
> cheers,
> 
> Rolf Turner
> 

  I had a brief look at Schielzeth & Nakagawa 2012; they don't give
any immediately useful expression for the 'distribution-specific
variance' term sigma^2_d, and going back to Nakagawa and Schielzeth
2010 ("Repeatability for Gaussian and non-Gaussian data ...", cited by
SN2012 for distribution-specific variances) points further into the
weeds   as they say "There are other options, like negative binomial
models, that could also be considered (but are not treated here" and
refer the reader to papers by Carrasco 2009 and Carrasco and Jover
2005 ...  (Presumably the problem here is that the overdispersion in
the standard NB2 parameterization is neither additive nor
multiplicative.)  This probably wouldn't be too hard to work out in a
few hours of thought, but ...

  If you just need to make reviewers/colleagues happy, you could
always use the squared correlation coefficient between fitted and
observed values (I think Doug Bates has suggested this in the past).
This is certainly "an" R^2 measure, if not "the" R^2 measure.


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