Large data set and mixed models

I would like two compare the diving behaviour of two groups of  
penguins (7 penguin in each group). Each penguin performed several  
dives within several foraging trips. As a result, I got a huge data  
set of dives (nearly 100000).
To compare the diving behaviour of the two groups, I used a mixed  
model with:
-the penguin as a random factor,
-the number of dives nested in the foraging trip as a repeated factor,
-the group, the foraging trip and maximal depth as fixed factors.
Covariance structure was auto-regressive.
I tried this model on SPSS, SAS and R but all failed.

Has anybody been faced with such a huge dataset analysed with mixed  
models?

Thank you

MiKL

Dear Michael,

perhaps you could send more details about the model you're trying to
estimate, so we could be of help.

i.e.:
- What is the model specification
- What happens -> error message, uninterpretable findings?
- A closer description of the data
- What system are you trying to estimate this model with?

In general I wouldn't say the 100000 cases is 'huge' in terms of R-Project.
Sure, some models will take some time to converge, but it should be doable.

If you'd send me (a sample of) your data, I'd be willing to take a look at
it.

On 17-okt-2008, at 15:02, Michael Beaulieu wrote:

I would like two compare the diving behaviour of two groups of penguins (7
penguin in each group). Each penguin performed several dives within several
foraging trips. As a result, I got a huge data set of dives (nearly 100000).
To compare the diving behaviour of the two groups, I used a mixed model
with:
-the penguin as a random factor,
-the number of dives nested in the foraging trip as a repeated factor,
-the group, the foraging trip and maximal depth as fixed factors.
Covariance structure was auto-regressive.
I tried this model on SPSS, SAS and R but all failed.

Has anybody been faced with such a huge dataset analysed with mixed
models?

Thank you

MiKL