I have tried glm.nb in the MASS package, but many models (I have 250 models
with different combinations of predictors for fish counts data) either fail
to converge or even diverge.

 

I'm attempting to use the negbin function in the AOD package, but am unsure
what to use for the "random" term, which is supposed to provide a right hand
formula for the overdispersion parameter. I'm not even sure what this
statement means. Any advice you have would be greatly appreciated.

 

negbin(formula, random, data, phi.ini = NULL, warnings = FALSE, 
         na.action = na.omit, fixpar = list(),
         hessian = TRUE, control = list(maxit = 2000), ...)

 

My largest model using glm.nb looks like this:

 

negBin.glm1 <- glm.nb(Count ~ offset(log(Tow.Area)) + Basin + Bathy +
Hypoxia + Period + Depth + Basin*Depth + Bathy*Depth + Hypoxia*Depth +

              Period*Depth + Basin*Period + Bathy*Period + Hypoxia*Period +
Hypoxia:Period:Depth + Bathy:Period:Depth +

              Basin:Period:Depth, control=glm.control(maxit=1000),
method="glm.fit",

                data=Combined.Counts.df)

 

How could I adjust this to function with the "negbin" function?
Specifically, what would I use for the required "random" term?

 

 

 

Caroline E. Paulsen

Masters Candidate

School of Aquatic and Fishery Sciences

University of Washington

phone: 206.852.9539

email: cpaulsen@u.washington.edu

 


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