[R-sig-ME] R2 for Negative Binomial calculated with GLMMADMB
Jens Oldeland
fbda005 at uni-hamburg.de
Wed Dec 17 21:17:33 CET 2014
Dear Douglas,
many thanks for your thoughts. I understand that R2 is not perfectly
correct for GLMs or anything more complicated. But still...
In my example, I calculated now these 20 negbin GLMMs and if anybody
asks me how reliable they are, I cannot tell. According to the AIC
thinking, I found the best of my candidate models, i.e. for each model
I checked all possible parameter combinations in order to identify the
"best" model (yes, there is no best model, and yes, searching a model
using this procedure is for sure not optimal). I can calculate AIC
weights which tell me how different my models are but not if the model
is any good.
How can I know? Are there any possibilities to check this? Plotting
observed versus predicted?
I mean, can I publish something without knowing this? I am an
ecologist, so I am not perfectly trained in statistics and also not in
assessing the quality of GLMMs.
Don´t worry, I am not in a bad mood while writing. just curious how
this can be solved.
best regards from Hamburg, Germany
jens
Zitat von Douglas Bates <bates at stat.wisc.edu>:
> <sermon>
> I must admit to getting a little twitchy when people speak of the "R2 for
> GLMMs". R2 for a linear model is well-defined and has many desirable
> properties. For other models one can define different quantities that
> reflect some but not all of these properties. But this is not calculating
> an R2 in the sense of obtaining a number having all the properties that the
> R2 for linear models does. Usually there are several different ways that
> such a quantity could be defined. Especially for GLMs and GLMMs before you
> can define "proportion of response variance explained" you first need to
> define what you mean by "response variance". The whole point of GLMs and
> GLMMs is that a simple sum of squares of deviations does not meaningfully
> reflect the variability in the response because the variance of an
> individual response depends on its mean.
>
> Confusion about what constitutes R2 or degrees of freedom of any of the
> other quantities associated with linear models as applied to other models
> comes from confusing the formula with the concept. Although formulas are
> derived from models the derivation often involves quite sophisticated
> mathematics. To avoid a potentially confusing derivation and just "cut to
> the chase" it is easier to present the formulas. But the formula is not
> the concept. Generalizing a formula is not equivalent to generalizing the
> concept. And those formulas are almost never used in practice, especially
> for generalized linear models, analysis of variance and random effects. I
> have a "meta-theorem" that the only quantity actually calculated according
> to the formulas given in introductory texts is the sample mean.
>
> It may seem that I am being a grumpy old man about this, and perhaps I am,
> but the danger is that people expect an "R2-like" quantity to have all the
> properties of an R2 for linear models. It can't. There is no way to
> generalize all the properties to a much more complicated model like a GLMM.
>
> I was once on the committee reviewing a thesis proposal for Ph.D.
> candidacy. The proposal was to examine I think 9 different formulas that
> could be considered ways of computing an R2 for a nonlinear regression
> model to decide which one was "best". Of course, this would be done
> through a simulation study with only a couple of different models and only
> a few different sets of parameter values for each. My suggestion that this
> was an entirely meaningless exercise was not greeted warmly.
> </sermon>
>
> On Wed Dec 17 2014 at 9:49:28 AM Jens Oldeland <fbda005 at uni-hamburg.de>
> 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 invested 2 weeks in lengthy calculations of about 20
>> negative binomial GLMMs using the glmmADMB package. Now, my colleagues
>> want the R2 (me too), however, sadly, the MuMIn functions do only work
>> for binomial and poisson GLMMS. Further, it seems that the functions do
>> not recognize the glmmADMB package but prefer (g)lmer output.
>>
>> Now my question: Does anybody of you know if this is "easy" to implement
>> and if so "how"? I tried to redo the code provided here (actually posing
>> the same question) but failed...:
>> http://stats.stackexchange.com/questions/109215/r%C2%B2-
>> squared-from-a-generalized-linear-mixed-effects-models-glmm-using-a-negat
>>
>> Or does anybody know if in the near future (this year?) it will be
>> implemented somewhere?
>>
>> Is it possible to transform a GLMMADMB object into an lmer object?
>>
>> Any hints are most welcome,
>>
>> merry Xmas
>> Jens
>>
>>
>> [1] Nakagawa, S., & Schielzeth, H. (2013). A general and simple method
>> for obtaining R2 from generalized linear mixed-effects models./Methods
>> in Ecology and Evolution/,/4/(2), 133-142.
>>
>> --
>> +++++++++++++++++++++++++++++++++++++++++
>> Dr. Jens Oldeland
>>
>> Post-Doc Researcher & Lecturer @ BEE
>> Managing Editor - Biodiversity & Ecology
>>
>> Biodiversity, Ecology and Evolution of Plants (BEE)
>> Biocentre Klein Flottbek and Botanical Garden
>> University of Hamburg
>> Ohnhorststr. 18
>> 22609 Hamburg,
>> Germany
>>
>> Tel: 0049-(0)40-42816-407
>> Fax: 0049-(0)40-42816-543
>> Mail: jens.oldeland at uni-hamburg.de
>> Oldeland at gmx.de
>> Skype: jens.oldeland
>> http://www.biologie.uni-hamburg.de/bzf/fbda005/fbda005.htm
>> http://www.biodiversity-plants.de/biodivers_ecol/biodivers_ecol.php
>> +++++++++++++++++++++++++++++++++++++++++
>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
--
+++++++++++++++++++++++++++++++++++++++++
Dr. Jens Oldeland
Post-Doc Researcher & Lecturer @ BEE
Managing Editor - Biodiversity & Ecology
Biodiversity, Ecology and Evolution of Plants (BEE)
Biocentre Klein Flottbek and Botanical Garden
University of Hamburg
Ohnhorststr. 18
22609 Hamburg,
Germany
Tel: 0049-(0)40-42816-407
Fax: 0049-(0)40-42816-543
Mail: jens.oldeland at uni-hamburg.de
Oldeland at gmx.de
Skype: jens.oldeland
http://www.biologie.uni-hamburg.de/bzf/fbda005/fbda005.htm
http://www.biodiversity-plants.de/biodivers_ecol/biodivers_ecol.php
+++++++++++++++++++++++++++++++++++++++++
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