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 ------------------------------
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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