Hello, 

I'm working with long term catch data for a rare species of fish, and I'm interested in running a generalized mixed model to look at environmental variables associated with catch. The problem that I've encountered is that the data has a lot of zeros, and I don't believe there's code available which deals with zero inflated data, but also allows you to use random effects (other than the Bayesian MCMCglmm). Does anyone know if that's correct? 

Essentially, I'm analyzing data where sampling occurred at the same locations for 15 years (about 30 locations). Because of this, I was hoping to have sampling location nested within year as random in the model; however, I won't be able to do this using the zero-inflated models or hurdle models that are out there. 

One suggestion someone made in another mailing list was having an observation level random effect in the model (see code below). Previously, the models wouldn't run, but the model ran once this observation level random effect was included. The thing I'm wondering is whether this is an appropriate method to deal with zero-inflated data. I can't really seem to find much support for this approach, so I am having trouble feeling comfortable using it. I don't fully understand what having the observation level random effect is doing. 

So my dilemma is 1) should I use the less supported observation level random effect (which would allow for the random effects I figure should be in the model); or 2) should I run a more supported zero-inflated/hurdle model which doesn't allow for these random effects (then including year and station as fixed effects??). 

Any recommendations you can provide would much appreciated. Thanks in advance for your help! 

Cheers, 

Kris 



##Code for observation-level random effect 

Generalized linear mixed model fit by the Laplace approximation 
Formula: Juv_Catch ~ Volume + Secchi + EcTop + WaterTemp + (1 | Yr/Station) + (1 | obs) 
Data: dataB 
AIC BIC logLik deviance 
3 680 3728 -1832 3664 
Random effects: 
Groups Name Variance Std.Dev. 
obs (Intercept) 1.93927 1.3926 
Station:Yr (Intercept) 3.56159 1.8872 
Yr (Intercept) 0.27626 0.5256 
Number of obs: 2974, groups: obs, 2974; Station:Yr, 748; Yr, 23 

Fixed effects: 
Estimate Std. Error z value Pr(>|z|) 
(Intercept) 5.243e +00 1.057 e +00 4.962 6.96 e-07 *** 
Volume 7.704e-04 1.135e-04 6.788 1.14e-11 *** 
Secchi -8.639e-02 5.750e-03 -15.025 < 2e-16 *** 
EcTop -7.593e-05 1.257e-05 -6.040 1.54e-09 *** 
WaterTemp -2.434e-01 4.838e-02 -5.031 4.88e-07 *** 
--- 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Correlation of Fixed Effects: 
(Intr) Volume Secchi EcTop 
Volume -0.166 
Secchi -0.052 -0.142 
EcTop -0.295 0.005 0.162 
WaterTemp -0.942 -0.045 -0.132 0.197 



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