Mixed effect logistic regression help

CJ Griffiths <Christine.Griffiths at ...> writes:

Dear all,

I want to specify a mixed conditional logistic regression to model
microhabitat selection, but am unsure whether my dataframe and model are
correct. I want to compare parameters such as wind and temperature at the
location of the animal (1) to a random observation, where the animal was
absent (0). Each Response (1/0) is thus paired by the variable Micro. To
account for this pairing, I specified the random effect as 1|Micro.
However, I repeatedly sampled 11 animals (Ind). Random effect = 1|Ind

...

A little late in the response...

To my understanding, mixed models cannot accommodate the paired 1/0 structure of
your data.  Adding micro as a mixed effect is only accounting for the variance
in your 1/0 response among different levels of micro.  However, since you have
an equal number of 1s and 0s for each level, you see zero as the variance in
micro.  You need to use a conditional logistic model (e.g., clogit in package
survival) to do what you want to do to accommodate the paired case/control
nature of your samples.  However, clogit cannot accommodate the repeated
sampling of individuals (which lmer, nlme, etc. can, but not in this case).  The
only real application I've seen bridging these two 'worlds' is the following:

Duchesne, T., D. Fortin, and N. Courbin. 2010. Mixed conditional logistic
regression for habitat selection studies. Journal of Animal Ecology 79:548-555.

Unfortunately, it was implemented in Matlab, so not so useful to any of us in R
land.  Personally, I'd be very excited to see a mixed conditional logit package
or extension developed for R, but I'm neither statistician nor programmer.  

Giancarlo