[R-sig-ME] Negative Binomial in glmmadmb

[Mollie Brooks]

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 Zürich
http://www.popecol.org/team/mollie-brooks/


> On 5Jul 2016, at 19:03, Chad Newbolt <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> on behalf of Chad Newbolt <newboch at auburn.edu>
> Sent: Friday, July 1, 2016 7:29 PM
> To: 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>
> Sent: Friday, July 1, 2016 2:53 PM
> To: Chad Newbolt; 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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