[R-sig-ME] Negative Binomial in glmmadmb

[Ben Bolker]

  I guess the question is what you want to plot ... do you want to plot
graphical diagnostics (Q-Q, residuals vs fitted, etc.) as in the
standard R plot.* methods for models, predictions, effects, ... ?

   Keep in mind that glmmTMB is brand new -- if you're having issues
with the predictions, you should probably let the developers know what
they are by posting an issue at
https://github.com/glmmTMB/glmmTMB/issues ...

   cheers
      Ben Bolker

On 16-07-06 09:21 AM, Chad Newbolt wrote:
> Which graphical package is recommended to be used in conjunction with
> glmmTMB?  ggplot2?  Specifically, I would like to use the predict
> function but have been having a few issues.  As stated, I'm a
> relative novice with R (even more so with graphical packages in R)
> and trying to learn largely on my own so please excuse simplicity of
> questions.
> 
> 
> 
> Thanks,
> 
> 
> 
> Chad
> 
> ________________________________ From: Mollie Brooks
> <mbrooks at ufl.edu> Sent: Tuesday, July 5, 2016 12:10 PM To: Chad
> Newbolt Cc: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME]
> Negative Binomial in glmmadmb
> 
> Hi Chad,
> 
> I�ve been using AICtab from the bbmle package. If you have any NAs,
> you may need to be vigilant that the various models really are using
> the same data. I haven�t carefully checked that part of the
> functionality yet.
> 
> cheers, Mollie
> 
> ------------------------ Mollie Brooks, PhD Postdoctoral Researcher,
> Population Ecology Research Group Department of Evolutionary Biology
> & Environmental Studies, University of Z�rich 
> http://www.popecol.org/team/mollie-brooks/
> 
> 
> On 5Jul 2016, at 19:03, Chad Newbolt
> <newboch at auburn.edu<mailto:newboch at auburn.edu>> wrote:
> 
> Can aictabs be used in conjunction with the glmmTMB package and/or
> there any alternatives that will quickly accomplish the same thing?
> I've tried aictabs and does not seem to work at first attempt.
> 
> Chad ________________________________________ From:
> R-sig-mixed-models
> <r-sig-mixed-models-bounces at r-project.org<mailto:r-sig-mixed-models-bounces at r-project.org>>
> on behalf of Chad Newbolt
> <newboch at auburn.edu<mailto:newboch at auburn.edu>> Sent: Friday, July 1,
> 2016 7:29 PM To:
> r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
>
> 
Subject: Re: [R-sig-ME] Negative Binomial in glmmadmb
> 
> I had to use:
> 
> family=list(family="nbinom1", link="log")
> 
> in glmmTMB
> 
> whereas
> 
> family="nbinom1"
> 
> had previously worked in glmmADMB.  Thanks for pointing me towards
> examples. ________________________________________ From: Philipp
> Singer <killver at gmail.com<mailto:killver at gmail.com>> Sent: Friday,
> July 1, 2016 2:53 PM To: Chad Newbolt;
> r-sig-mixed-models at r-project.org<mailto:r-sig-mixed-models at r-project.org>
>
> 
Subject: Re: [R-sig-ME] Negative Binomial in glmmadmb
> 
> Exactly as you would do it in glmmADMB, just replace ADMB with
> TMB...
> 
> Check the github examples: 
> https://github.com/glmmTMB/glmmTMB/tree/master/glmmTMB/tests/testthat
>
>  On 01.07.2016 21:47, Chad Newbolt wrote: Thanks so much for the
> response.  I know this is probably very simple but how do I denote
> the family as negative binomial using glmmTMB?  I've dug through text
> regarding this package and have had trouble coming up with anything
> that works.
> 
> Chad ________________________________________ From:
> R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> on
> behalf of Ben Bolker <bbolker at gmail.com> Sent: Thursday, June 30,
> 2016 7:45 PM To: r-sig-mixed-models at r-project.org Subject: Re:
> [R-sig-ME] Negative Binomial in glmmadmb
> 
> Chad Newbolt <newboch at ...> writes:
> 
> [snip]
> 
> Since I have evidence for overdispersion, I'm using negative binomial
> distribution as opposed to Poisson.  My two questions are: 1) When I
> fit using the following global zero inflation model I receive the
> following error: 
> fit1=glmmadmb(Fawn~Age+I(Age^2)+BodySize+SSCM+AvgAge+Age*AvgAge+ 
> I(Age^2)*AvgAge+BodySize*AvgAge+SSCM*AvgAge+(1|Sire), 
> data=datum,family="nbinom",zeroInflation = TRUE)
> 
> I think you can shorten this a bit to
> 
> (Age+I(Age^2)+BodySize+SSCM)*AvgAge + (1|Sire)
> 
> Parameters were estimated, but standard errors were not: the most 
> likely problem is that the curvature at MLE was zero or negative 
> Error in glmmadmb(Fawn ~ Age + I(Age^2) + BodySize + SSCM + AvgAge + 
> Age * : The function maximizer failed (couldn't find parameter file) 
> Troubleshooting steps include (1) run with 'save.dir' set and inspect
> output files; (2) change run parameters: see '?admbControl';(3)
> re-run with debug=TRUE for more information on failure mode In
> addition: Warning message: running command 
> 'C:\windows\system32\cmd.exe /c glmmadmb -maxfn 500 -maxph 5 -noinit 
> -shess' had status 1 However, when I change to zeroInflation = FALSE,
> I receive no warnings and everything seems to go as should. Does this
> simply mean that my data is not zero inflated, hence the zero
> inflated model will not run, or is this something I should be 
> concerned about and investigate the cause further?  When I debug I 
> see the following warning....Warning -- Hessian does not appear to be
> positive definite Hessian does not appear to be positive definite. 2)
> When fitting more simple versions(predictors removed) I receive the
> same error as above when using the family=nbinom; however these 
> errors disappear when using family=nbinom1.  Is this indicative of an
> underlying problem or am I OK to use the ouput from the later family
> where variance = ??.  Thanks, Chad [[alternative HTML version 
> deleted]] Short answer: you should be a little concerned, and you
> should not assume that your data are not zero-inflated. These are
> not indications about what your model is actually finding, just
> indications that ADMB ran into *some* kind of trouble.
> Unfortunately, there is no really simple guide to trouble-shooting
> these kinds of problems.  Some general suggestions:
> 
> * try out the glmmTMB package - it's newer/experimental, but often
> more stable * the ?admbControl man page suggests trying shess=FALSE
> and noinit=FALSE * it may not help in this case, but centering
> continuous predictors is always worth a shot * similarly, poly(Age,2)
> is a little more stable than (Age+I(Age^2)) * inspect your data
> graphically to see whether there are outliers or other odd patterns
> that might be messing up the fit
> 
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> 
> 
> [[alternative HTML version deleted]]
> 
> 
> 
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