[R-sig-ME] Binomial GLMM or GAMM with random intercept and temporal correlation

[Samantha Cox]

Dear mixed models mailing list,

I am trying to model some binomial data (0/1) as a function of sex (0/1) and DistanceToFeature (continuous km's) with an interaction between the two.  My data is nested and I therefore want to include a random intercept for InidividualID and within that I want to include an AR1 correlation structure as the data is serially/temporally auto-correlated.  I understand any correlation structure should be nested within the random effect.

So far I have tried running the model using glmmPQL as

glmmPQL(Y ~ DistanceToFeature * Sex + (1|InidividualID), correlation=corAR1(form=~1|IndividualID/ContinuousBout), family='binomial', data='birds')

(note - ContinuousBout is an ID for where there are time gaps in the data).

However, although this runs, am I right in understanding that I should not use PQL estimation with binomial data as it gives biased results?  Does anyone know of a way I can model this?

I understand that this is also the case if I wish to use GAMM (as later I will be modelling a non-linear explanatory as well)?

Additionally I will also be running a similar set up but where the data are not equally spaced in time (and therefore an AR1 structure would not apply). Can anyone give a recommendation of a modelling framework for this also.

Any help would be much appreciated.

Thank you

Sam


________________________________
[http://www.plymouth.ac.uk/images/email_footer.gif]<http://www.plymouth.ac.uk/worldclass>

This email and any files with it are confidential and intended solely for the use of the recipient to whom it is addressed. If you are not the intended recipient then copying, distribution or other use of the information contained is strictly prohibited and you should not rely on it. If you have received this email in error please let the sender know immediately and delete it from your system(s). Internet emails are not necessarily secure. While we take every care, Plymouth University accepts no responsibility for viruses and it is your responsibility to scan emails and their attachments. Plymouth University does not accept responsibility for any changes made after it was sent. Nothing in this email or its attachments constitutes an order for goods or services unless accompanied by an official order form.

	[[alternative HTML version deleted]]