[R-sig-ME] glmmADMB error messages with percentage cover data (Elwyn Sharps)

[Ben Bolker]

On 14-11-19 12:16 PM, Elwyn Sharps wrote:
> Hi Alain, Thanks for your reply. Sorry I hadn't noticed this section
> of the glmmADMB help file and as some of the models were running
> fine, I assumed there wasn't a problem with the family. Have you got
> any other ideas on how I can deal with the very large number of zeros
> in the data? As it's percentage cover rather than count data, I don't
> think I can use Poisson or Neg Binomial. Thanks again Elwyn

   A couple of thoughts:

 + I believe glmmADMB *can* handle zero-inflated binomials (I would try
testing with a simple made-up data set to check that the results are
sensible).

 + does your plant cover variable have a meaningful denominator?  (e.g.,
if you had point-count data with 100 points, or with a known number of
points)  If you're just taking a percentage that's assessed from a
visual impression, or e.g. a measure of area from remote sensing, then
you probably don't have a variable that's actually binomial (or
zero-inflated binomial).  A beta distribution might be more appropriate,
but it too would have to be zero-inflated.

  + errors of the sort you're seeing are due to some sort of an
instability in the model -- might be fixable with a more recent version
of the glmmADMB binary files, which are available but I haven't gotten
around to packaging in a new version yet.


> 
>> Date: Wed, 19 Nov 2014 17:38:40 +0100 From: highstat at highstat.com 
>> To: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME]
>> glmmADMB error messages with percentage cover data	(Elwyn Sharps)
>> 
>> 
>> 
>>> 
>>> I have collected data on % cover of a variety of plant species on
>>> heathland (in relation to nesting birds) and am interested in 
>>> testing the effect of density of grazing ponies, the proximity to
>>> nest site (%cover measured at 1 and 10m from the nest) and the
>>> age of the nest (as a control for vegetation being measured at
>>> nests of different ages). The response is percentage cover and
>>> contains many zeros (see attached data for an example).
>>> 
>>> 
>>> I have tried using glmmADMB, with family= binomial and zero
>>> inflation=T. The response was created using cbind e.g. 
>>> y<-(cbind(Data$Plant_cover, Data$Plant_empty)), where Plant cover
>>> = % cover and Plant empty = 100-Plant cover.
>>> 
>>> 
>>> Example model:
>>> 
>>> Fit1<-glmmadmb(y~Grazing*proximity+Age+(1|site),data=Heath,zeroInflation=TRUE,family="binomial")
>>>
>>
>>
>>
>>> 
See the help file of glmmadmb:
>> 
>> zeroInflation:   whether a zero-inflated model should be fitted
>> (only "poisson" and "nbinom" families).
>> 
>> With emphasis on the last part in this sentence.
>> 
>> Kind regards,
>> 
>> 
>> Alain
>> 
>> 
>>> The majority of the models for Species 1 have been running fine,
>>> however I have been getting the occasional error message and for
>>> Species 2, all models give an error message.
>>> 
>>> 
>>> Example error messages:
>>> 
>>> Parameters were estimated, but not standard errors were not: the
>>> most likely problem is that the curvature at MLE was zero or 
>>> negative
>>> 
>>> Error in glmmadmb(w ~ Grazing * proximity + Age + (1 | site),
>>> data = heathland,  :
>>> 
>>> The function maximizer failed (couldn't find STD file)
>>> Troubleshooting steps include (1) run with 'save.dir' set and
>>> inspect output files; (2) change run parameters: see
>>> '?admbControl'
>>> 
>>> Warning message:
>>> 
>>> In glmmadmb(w ~ 1 + (1 | site), data = heathland, zeroInflation =
>>> T,  :
>>> 
>>> Convergence failed:log-likelihood of gradient= -0.269018
>>> 
>>> 
>>> 
>>> I just wanted to check that this is a sensible way to deal with
>>> these data and if there is anything that I should be doing to 
>>> remedy the error messages? I've tried some other options, e.g.
>>> Arc sine transformation couldn't deal with the excess zeros. I've
>>> also considered quasibinomial in lme4, but have seen Doug Bates
>>> comments on the unreliability of the model output.
>>> 
>>> 
>>> 
>>> Any advice would be very much appreciated. All the best Elwyn
>>> 
>>> 
>>> ------------------------------
>>> 
>>> _______________________________________________ 
>>> R-sig-mixed-models mailing list R-sig-mixed-models at r-project.org 
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>> 
>>> 
>>> End of R-sig-mixed-models Digest, Vol 95, Issue 24 
>>> **************************************************
>>> 
>> 
>> -- Dr. Alain F. Zuur
>> 
>> First author of: 1. Beginner's Guide to GAMM with R (2014). 2.
>> Beginner's Guide to GLM and GLMM with R (2013). 3. Beginner's Guide
>> to GAM with R (2012). 4. Zero Inflated Models and GLMM with R
>> (2012). 5. A Beginner's Guide to R (2009). 6. Mixed effects models
>> and extensions in ecology with R (2009). 7. Analysing Ecological
>> Data (2007).
>> 
>> Highland Statistics Ltd. 9 St Clair Wynd UK - AB41 6DZ Newburgh 
>> Tel:   0044 1358 788177 Email: highstat at highstat.com URL:
>> www.highstat.com
>> 
>> _______________________________________________ 
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