[R-sig-ME] glmmADMB error messages with percentage cover data

[Elwyn Sharps]

Hi,


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")


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