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

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models


_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models

_______________________________________________
R-sig-mixed-models at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models