[R-sig-ME] Error with glmmADMB and beta distribution

[Ben Bolker]

Heather Kharouba <kharouba at ...> writes:

> I am new to glmmADMB and would like to use the beta distribution with
> glmmadmb. However, I keep running into the same error. The response
> variable is AUC (area under the curve data) from species distribution
> models which is continuous and ranges from 0 to 1. The fixed variables are
> the number of variables used to build the model (continuous), spatial
> extent of the distribution model (continuous), model type (factor) and
> taxonomic group (categorical) and the random effect is study.
> 
> Here's the model:
> 
> model1<-glmmadmb(AUC~variables+log_area+model+taxa+(1|study),
> family="beta", verbose=TRUE, data=auc);
> 
> A snapshot of the data:
> 
> study                  taxa       AUC      model variables log_area
>  Araujo et al. 2005 BIRD 0.9156878   GAM         7 16.21771
> Araujo et al. 2005 BIRD 0.9288596   GAM         7 16.21771
> Araujo et al. 2005 BIRD 0.9254065   GAM         7 16.21771
> Araujo et al. 2005 BIRD 0.8825593   GAM         7 16.21771
> Araujo et al. 2005 BIRD 0.9388894   GAM         7 16.21771
> Araujo et al. 2005 BIRD 0.9061483   GAM         7 16.21771
> 
> When I run the model I get this error:
> Error in glmmadmb(AUC ~ variables + log_area + model + taxa + (1 | study),
>  :
>   The function maximizer failed (couldn't find STD file)
> 

  [snip]
> 
> I'm using glmm ADMB version 0.7.2.2 with R version 2.14.2 on a Mac OS X
> Version 10.6.8. I've tried including
> admb.opts=admbControl(shess=FALSE,noinit=FALSE) and still get the same
> error. I'm guessing there's something wrong with either the response
> variable or with the overall data structure?

  My first guess would be that you have AUC values that are 
exactly equal to 0 or 1; they will give infinite/NaN log-likelihoods
in the beta model. Beyond that, I'm not sure.  It would be useful
to know a little bit more about your data -- how many total observations?
How many studies?

Does fitting the model without the random effect work, i.e.

model2 <- glmmadmb(AUC~variables+log_area+model+taxa,
  family="beta", verbose=TRUE, data=auc)

?