[R-sig-ME] Dealing with overdispersion with glmmadmb beta distribution

[Heather Kharouba]

Dear R users,

In the analysis I'm doing, I'm interested in testing the importance of a
factor (with 5 levels). The response variable varies continuously from 0
to 1 so I've used a beta distribution with glmmadmb:

m1<-glmmadmb(AUC~variables+log_area+model+taxa+(1|study), family="Beta",
verbose=TRUE, debug=TRUE, data=mat);

Call:
glmmadmb(formula = AUC2 ~ variables + log_area + model2 + taxa +
    (1 | study), data = mat, family = "Beta", verbose = TRUE,
    debug = TRUE)


Coefficients:
                              Estimate Std. Error z value Pr(>|z|)
(Intercept)                    -1.1568     1.3899   -0.83   0.4052
variables                       0.0248     0.0112    2.22   0.0264 *
log_area                        0.1976     0.0859    2.30   0.0215 *
model2autologistic regression   2.2526     0.7345    3.07   0.0022 **
model2domain                   -0.3246     0.5753   -0.56   0.5726
model2GAM                       0.1757     0.4768    0.37   0.7125
model2GARP                     -0.2985     0.5626   -0.53   0.5957
model2gdm                      -0.5436     0.5691   -0.96   0.3395
model2GLM                      -0.3243     0.5860   -0.55   0.5800
model2localwghtregression       0.6689     0.4780    1.40   0.1617
model2logregression            -0.5179     0.6661   -0.78   0.4369
model2maxent                    0.0182     0.5353    0.03   0.9728
model2other                    -0.4466     0.5114   -0.87   0.3825
taxaHER                         0.3200     0.1024    3.12   0.0018 **
taxaINV                         0.6967     0.1591    4.38  1.2e-05 ***
taxaMAM                        -0.1792     0.0859   -2.09   0.0368 *
taxaP                           0.1230     0.0616    2.00   0.0459 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Number of observations: total=4317, study=20
Random effect variance(s):
Group=study
            Variance StdDev
(Intercept)    0.328 0.5727
Beta dispersion parameter: 14.503 (std. err.: 0.35671)

Log-likelihood: 9167.05

The dispersion parameter is 14.503 suggesting the model is overdispersed.
A recent suggestion from http://glmm.wikidot.com/faq and
r-sig-mixed-models archives to account for overdispersion is to include
individual-level random effects. However, if I include this additional
random effect, I get the following error:

Error in glmmadmb(AUC2 ~ variables + log_area + model2 + taxa + (1 |
study) +  :
  The function maximizer failed (couldn't find STD file)

suggesting that the model cannot be estimated. I'm wondering whether there
are other methods to reduce overdispersion I might have overlooked that
could affect the variance-mean relationship and that would be appropriate
for this type of response variable.

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

Thanks in advance!
Heather Kharouba