Negative Binomial in glmmadmb

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

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>

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