[R] glmmADMB: Generalized Linear Mixed Models using AD Model Builder

[Roel de Jong]

Dear R-users,

because lme(r) & glmmpql, which are based on Penalized Quasi Likelihood, 
are not very robust with Bernoulli responses, I wanted to test glmmADMB. 
I run the following simulation study:

500 samples are drawn with the model specification:
y = (intercept*f1+pred2*f2+pred3*f3)+(intercept*ri+pred2*rs)
     where pred2 and pred3 are predictors distributed N(0,1)
     f1..f3 are fixed effects, f1=-1, f2=1.5, f3=0.5
     ri is random intercept with associated variance var_ri=0.2
     rs is random slope with associated variance var_rs=0.4
     the covariance between ri and rs "covr"=0.1

1500 units/dataset, class size=30

convergence:
~~~~~~~~~~~~
No crashes.
5/500 Datasets had on exit a gradient of the log-likelihood > 0.001 
though. Removing the datasets with questionable convergence doesn't seem 
to effect the simulation analysis.

bias:
~~~~~~
f1=-1.00531376
f2= 1.49891060
f3= 0.50211520
ri= 0.20075947
covr=0.09886267
rs= 0.38948382

Only the random slope "rs" is somewhat low, but i don't think it is of 
significance

coverage alpha=.95: (using asymmetric confidence intervals)
~~~~~~~~~~~~~~~~~~~~~~~~
f1=0.950
f2=0.950
f3=0.966
ri=0.974
covr=0.970
rs=0.970

While some coverages are somewhat high, confidence intervals based on 
asymptotic theory will not have exactly the nominal coverage level, but 
with simulations (parametric bootstrap) that can be corrected for.

I can highly recommend this excellent package to anyone fitting these 
kinds of models, and want to thank Hans Skaug & Dave Fournier for their 
hard work!

Roel de Jong.


Hans Julius Skaug wrote:
> Dear R-users,
> 
> Half a year ago we put out the R package "glmmADMB" for fitting
> overdispersed count data.
> 
> http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html
> 
> Several people who used this package have requested
> additional features. We now have a new version ready.
> The major new feature is that glmmADMB allows Bernoulli responses
> with logistic and probit links. In addition there is
> a "ranef.glmm.admb()" function for getting the random effects.
> 
> The download site is still:
> 
> http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html
> 
> The package is based on the software ADMB-RE, but the full
> unrestricted R-package is made freely available by Otter Research Ltd
> and does not require ADMB-RE to run. Versions for Linux and Windows
> are available.
> 
> We are still happy to get feedback for users, and to get suggestions
> for improvement.
> 
> We have set up a forum at http://www.otter-rsch.ca/phpbb/ for discussions 
> about the software.
> 
> Regards,
> 
> Hans
> 
> _____________________________
> Hans Julius Skaug
> 
> Department of Mathematics
> University of Bergen
> Johannes Brunsgate 12
> 5008 Bergen
> Norway
> ph. (+47) 55 58 48 61
> 
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