R help - Nov 2010 - autocorrelation in count data

hello,

I try to model traffic accidents with the following model:

glm.nb(y~j+w+m+sf+b+ft,data=fr[]). the problem is that there exist  
autocorrelation  in the data. one possibility is to model traffic  
accidents with inar(1)-models. has anyone an idea how to change this  
model in order to abtain an integer valued time series model?

thanks
nazli

see
http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9892.2010.00684.x/abstract

kjetil

On Fri, Nov 19, 2010 at 6:02 PM,  <sahin at hsu-hh.de>
wrote:
> hello,
>
> I try to model traffic accidents with the following model:
>
> glm.nb(y~j+w+m+sf+b+ft,data=fr[]). the problem is that there exist
> autocorrelation ?in the data. one possibility is to model traffic accidents
> with inar(1)-models. has anyone an idea how to change this model in order
to
> abtain an integer valued time series model?
>
> thanks
> nazli
>
> ______________________________________________
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

You can fit this model with AD Model Builder's random effects module.
there is an example fitting a Poisson and negative binomial to the 
venerable
polio data set with ar(1) random effects at

        
http://admb-project.org/examples/count-data/negative-binomial-serially-correlated-counts

A big advantage of ADMB is flexible modeling of both the mean and 
overdispersion.