R2 for Negative Binomial calculated with GLMMADMB

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.

Cheers,

Simon.

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 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

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]]

-- +++++++++++++++++++++++++++++++++++++++++ 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