Mixed-model-binary logistic model with dependence between individual repeated measures

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Hi, I am a novice R user and do not know how to properly mail to this
list. I apologies if I do it in the wrong way.

I want to analyze my data using a random intercept (later extended
also to random slope) logistic model for a binary outcome (later
extended to a ordinal outcome). This I have been able to do in SAS if
assuming the repeated measurements within an individual to be
independent, but I want to be able to choose different covariance
structures for the individual measurements. This I cannot do directly
in either SAS or STATA, and therefore now turn to R. How can I do
this in R?

Anna

I'm surprised that you can't do this in SAS (PROC MIXED, NLMIXED, or
GLIMMIX?) or Stata <http://www.gllamm.org/>, but: if you want to do it
in R, your choices are glmmPQL in the MASS package or possibly one of
the generalized estimating equation packages (geese, geepack?)  I would
recommend the following references for getting started:

  Zuur et al Mixed models (Springer)
  Pinheiro and Bates 2000 (Springer), especially the material on
temporal autocorrelation models

  Extending to a ordinal outcome with temporal autocorrelation could be
tricky ...




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