Hey Mike,

I am working on a similar dataset as you. I have point counts of birds in 26 sites, in total 57 points [number of point counts per site varying from 1 to 5]. These points were visited 5 times ( in the same year). Most of the counts are 0 (> 90%), and the dataset is thus zero-inflated - which, as far as I know is a special case of overdispersion. There  are 3 continuous explanatory variables under consideration. I have been looking at different packages to fit a model with 
1)a  zero-inflated poison distribution 
2)a nested random effect to account for the fact that I have points located within sites
3)a random effect to account for the 5 visits (repeated measures)

However, none of the packages that I tried is able to do this ( I tried zeroinfl {pscl}, fmr {gnlm}, MCMCglmm {MCMCglmm} and glmm.ADMB{glmmADMB}). The problem seems to be the nested random effect (1|site/point in my case).

The only function that I found able to handle this nested random design and a distribution that comes close to be 'good' for my data is glmer with a quasi-poisson distribution. My  model syntax is:

test <- glmer(data$abundance~data$pca1+data$pca2+data$Gynoxis+(1|site/point) + (1|visit),family=quasipoisson(link="log"))

and yields the folowing output

Generalized linear mixed model fit by the Laplace approximation 
Formula: data$abundance ~ data$pca1 + data$pca2 + data$Gynoxis + (1 |      site/point) + (1 | visit) 
   AIC   BIC logLik deviance
 158.1 187.1 -71.05    142.1
Random effects:
 Groups     Name        Variance  Std.Dev.
 point:site (Intercept) 0.0468101 0.216356
 site       (Intercept) 0.0000000 0.000000
 visit      (Intercept) 0.0012396 0.035208
 Residual               0.0187386 0.136889
Number of obs: 276, groups: point:site, 57; site, 26; visit, 5

Fixed effects:
             Estimate Std. Error t value
(Intercept)  -3.20340    0.10701 -29.936
data$pca1    -0.02629    0.03100  -0.848
data$pca2    -0.15055    0.02907  -5.180
data$Gynoxis -0.02246    0.01320  -1.702

Correlation of Fixed Effects:
            (Intr) dt$pc1 dt$pc2
data$pca1   -0.239              
data$pca2   -0.098 -0.071       
data$Gynoxs -0.878  0.330  0.205

I was not aware of the comments of Ben Bolker on glmer and quasi-Poisson, and am thus also interested in any responses on this. Am I right to conclude that, ecept glmer, none of the packages mentioned above is capable to handle nested random effects and zero-inflated Poisson data?

Cheers and many thanks,

Diederik



Diederik Strubbe
Evolutionary Ecology Group
Department of Biology, University of Antwerp
Universiteitsplein 1
B-2610 Antwerp, Belgium
http://webhost.ua.ac.be/deco
tel : 32 3 820 23 85


	[[alternative HTML version deleted]]


