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

Shinichi Nakagawa shinichi.nakagawa at otago.ac.nz
Thu Dec 18 10:32:53 CET 2014

Dear Jens

Our proposed R2 is not 'the' R2 but is also an R2 for mixed models that has several of the useful properties of traditional R2 - actually first proposed by Snijders & Bosker (1994).

Let’s say NB(lambda, theta) with the log link — the mean = lambda, and the variance = lambda+ lambda^2/theta

The level 1 variance (on the link scale) should be ln(1+1/lambda+1/theta): see the Appendix of our paper, Nakagawa & Schielzeth (2013)

For lambda, it is good to use mean(Y) (Y is the response; counts) and the package should give you the value of theta (also, one should use mean(Y) for Possion models). 

Here the level 1 variance, sigma^2_1= sigma^2_e (additive over-dispersion)+sigma^2_d (distribution specific) = sigma^2_epsilon (residual variance) as in our paper (2013).

But Holger and I are doing some simulation study to check this first before its use, and we think we can extend the proposed R2 to other distributions although we need to test a few things first (we should be ready in one month or so). 

Best wishes,


Shinichi Nakagawa, PhD
(Associate Professor of Behavioural Ecology)
Department of Zoology
University of Otago
340 Great King Street
P. O. Box 56
Dunedin, New Zealand
Tel:  +64-3-479-5046
Fax: +64-3-479-7584

From: R-sig-mixed-models [r-sig-mixed-models-bounces at r-project.org] on behalf of Jens Oldeland [fbda005 at uni-hamburg.de]
Sent: Thursday, December 18, 2014 4:18 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] R2 for Negative Binomial calculated with GLMMADMB

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...:

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

[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,

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

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