[R-sig-ME] glmmadmb instead of glmer to avoid convergence warnings in lme4

[Lisa Rummel]

Dear mixed models experts,

I'm working with a dataset to analize the success of translocation programs of animal species. For this purpose, the success of various programs has been evaluated at a scale from 0 to 10. Now we want to investigate, in which way different factors like the duration of the program or the number of released animals, influence the success rate. 

We´re using a linear mixed-effects model (package lme4 1.1-6) with "success" as response variable, "duration" in years as predictor variable and species as random effect. But when I run the model, I obtain the following convergence warning:


> model10 <- glmer(succes~duration+(1|species), family=poisson)
 Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model failed to converge with max|grad| = 0.0136827 (tol = 0.001)


In order to avoid the convergence failure, we changed the distribution into a negative binomial distribution, using a glmmadmb model.


> model11 <- glmmadmb(succes~duration+(1|species), data, family="nbinom")
> summary(model11)

Call:
glmmadmb(formula = succes ~ duration + (1 | species), data = data, 
    family = "nbinom")

AIC: 183.8 

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  1.944321   0.123230   15.78   <2e-16 ***
duration    -0.000988   0.013070   -0.08     0.94    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of observations: total=33, species=21 
Random effect variance(s):
Group=species
            Variance StdDev
(Intercept)  0.01666 0.1291

Negative binomial dispersion parameter: 15.451 (std. err.: 24.659)

Log-likelihood: -87.8784 


Our question is, if using the glmmadmb model instead of the glmer is an applicable solution for the convergence failure in this case? Also, we're not quite sure if we can use the poisson/negative binomial distribution for the variable "success", because it`s limited to discrete values from 0 to 10. Perhaps there is kind of a truncated distribution we could use?

Thank you in advanced,
Lisa