[R-sig-ME] R2 for Negative Binomial calculated with GLMMADMB
fbda005 at uni-hamburg.de
Fri Dec 19 12:19:09 CET 2014
Thanks to all the repliers so far, and in particular thanks to Ben
Bolker for the posterior predictive test example on RPubs! I did not
work with Baysian stats so far, but I see that it is necessary, not only
in the case of complex models but mostly there. Just yesterday, I dug
through the 4 first chapters of the Introduction to WinBUGS... :)
Simple questions often provoke the most interesting discussions, isn´t
I learned something.
Thanks again and merry Xmas to all of you,
Am 18.12.2014 22:48, schrieb Simon Blomberg:
> I agree with Doug. R2 for anything other than an ordinary linear model is rearranging deck chair on the Titanic. GLMs and GLMMs are complicated. They can be wrong in a variety of ways and expecting a single number like R2 (however defined) is a poor way to assess the relative fit of a model. Pseudo R2s don't answer the same question as R2 for an OLS model anyway, as Doug pointed out. My approach would be to use posterior predictive tests in a Bayesian context, or perhaps cross-validation.
> Sent from my iPhone
>> On 19 Dec 2014, at 1:36 am, Jens Oldeland <fbda005 at uni-hamburg.de> wrote:
>> 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
>> Zitat von Douglas Bates <bates at stat.wisc.edu>:
>>> 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.
>>> On Wed Dec 17 2014 at 9:49:28 AM Jens Oldeland <fbda005 at uni-hamburg.de>
>>>> Dear List-members,
>>>> recently, the R2 calculations for GLMMs invented by Schielzieth and
>>>> Nakagawa 2012  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...:
>>>> 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
>>>>  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,
>>>> 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
>>>> [[alternative HTML version deleted]]
>>>> R-sig-mixed-models at r-project.org mailing list
>> 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,
>> 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
>> R-sig-mixed-models at r-project.org mailing list
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
Mail: jens.oldeland at uni-hamburg.de
Oldeland at gmx.de
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models