Hi, I'm a different Paul Johnson :) (Had a shock reviewing this thread
thinking I'd posted a message that I did not recognize at all).

The question is too vague now.  Aside from agreeing with another Paul
Johnson that there are differences between the negative binomial and
random-effects interpretations, I can't see where this is leading. The
negative binomial model magnifies variance for larger values of the
expected outcome, but it does not, by design, include "clustering" of
variances. On the other hand, the glmer model you describe is a model of a
"clustered" random effect that generates variety among outcomes for a given
set of other predictors.  Both can cause "overdispersion" in the sense you
notice, but they really are substantively different explanations of it.

How about you show us the estimated models to let us see what's so
different, in your view?

Apart from the residual diagnostics you are concerned about, what about the
substantively important parameters and predicted values?  I guess that's a
little interesting because, when I last checked, glmer did not have a
predict version in the CRAN package (was in testing Rforge version in
2012).

Are the predicted values of outcomes similar in the 2 models? How does it
look when you create a scatterplot of the predicted values of the GLMMADMB
model versus the glmer model?

If you post the code that leads to the different diagnostic scatterplots
and offer a link to the data, this might be interesting to make into one of
the working examples for the mixed models wiki.  I could assign some grad
students on this to work it out fully.



On Wed, Aug 28, 2013 at 4:46 AM, Luca Corlatti <luca.corlatti@boku.ac.at>wrote:

> Thanks, Paul, it's somewhat consoling to read somebody else had the same
> thoughts!
> Thanks a lot for your advice. What I miss here, though, is why the results
> of the model selection differ so much between glmmadmb and glmer, hence
> what is preferable between the two approaches...
> Cheers,
> Luca
>
>
>
>
> >>> Paul Johnson <paul.johnson@glasgow.ac.uk> 26/08/13 0.29 >>>
> Hi Luca,
>
> I've also seen this difference in residuals from a glmmadmb nbinom2 fit
> and a lognormal-Poisson fit with glmer. Generally the residuals X fitted
> plot from glmmadmb looks good (homoscedastic, no trend) while that from the
> lognormal-Poisson looks wrong (strong curve climbing sharply from negative
> to positive before plateauing). After some head-scratching I've decided
> that this isn't a problem.
>
> The reason for the discrepancy is that the fitted values from the
> lognormal-Poisson fit include the overdispersion random effects, i.e.
> everything expect the Poisson variation, while the NB fitted values don't,
> because overdispersion is intrinsic to the NB, not an added random effect.
> Because the obs-level random effect is there to increase the spread of the
> Poisson distribution, it's almost inevitable with strong overdispersion
> that the residuals with low fitted values will be negative while those with
> high fitted values will be positive.
>
> The way to get a fair comparison between the two is to remove the
> obs-level random effect from the fitted values of the lognormal-Poisson,
> leaving just the fixed effects and any other random effects. I find this
> generally gives a residual pattern much more similar to NB in glmmadmb:
>
>   Fitted <- exp(log(fitted(mod)) - ranef(mod)$obs[[1]])
>   Resid <- (dat$response - Fitted) / sqrt(Fitted + (Fitted^2) *
> c(exp(VarCorr(mod)$obs) - 1))
>   plot(Fitted, Resid)
>
> # obs is the name of the factor used for the obs-level random effect,
> # mod is the glmer fit of the lognormal-Poisson model, dat$response are
> the responses, and
> # the bit inside the sqrt() is the variance function for the
> lognormal-Poisson
>
> Generally I don't find that the model estimates and SEs differ much though.
>
> Best wishes,
> Paul
>
>
> Paul Johnson
>
> Institute of BAH&CM
> Graham Kerr Building
> University of Glasgow
> Glasgow G12 8QQ
>
> http://www.gla.ac.uk/researchinstitutes/bahcm/staff/pauljohnson/
> http://www.stats.gla.ac.uk/~paulj/index.html
>
>
> ________________________________________
> From: r-sig-mixed-models-bounces@r-project.org [
> r-sig-mixed-models-bounces@r-project.org] On Behalf Of Luca Corlatti [
> luca.corlatti@boku.ac.at]
> Sent: 25 August 2013 20:18
> To: r-sig-mixed-models@r-project.org
> Subject: [R-sig-ME] Overdispersion and model selection: glmmadmb vs. glmer
>
> Dear all,
> I recently ran a model selection (AIC-based) to investigate the role of
> several etho-ecological factors in shaping the emission of parasites in my
> study species. My data are counts showing overdispersion. I therefore
> fitted my models using the function glmmadmb with family=nbinom. Visual
> inspection of residuals (normality, heteroschedasticity, independence)
> suggested the global model fitted the data adequately, and I'm pretty happy
> with the results of my analysis. For the sake of curiosity, however, I
> tried to re-run the model selection using the function glmer, with
> family=poisson, adding the observation-level as a random factor (1|obs) to
> account for overdispersion, as recently suggested. In this case, however,
> visual inspection of residuals for the global model were not very
> satisfactory. After running the model selection, the results were quite
> different from those obtained with glmmadmb (not dramatically different,
> but still...). Given I have no deep knowledge into the philosophy behind
> the use of glmer with (1|obs), I was wondering:
> 1) when one should rely on the use of glmer with (1|obs) to account for
> overdispersion? (i.e. is the check of residuals for the global model the
> key issue here?)
> 2) why did I find such a difference in the outcome of the 2 model
> selections?
> Kind regards,
> Luca
>
>
>
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>


-- 
Paul E. Johnson
Professor, Political Science      Assoc. Director
1541 Lilac Lane, Room 504      Center for Research Methods
University of Kansas                 University of Kansas
http://pj.freefaculty.org               http://quant.ku.edu

	[[alternative HTML version deleted]]

