[R] Multilevel model with dichotomous dependent variable

[Douglas Bates]

Andrew Perrin <clists at perrin.socsci.unc.edu> writes:

> Greetings-
> 
> I'm working with data that are multilevel in nature and have a dichotomous
> outcome variable (presence or absence of an attribute). As far as I can
> tell from reading archives of the R and S lists, as well as Pinheiro and
> Bates and Venables and Ripley,
> 
> - nlme does not have the facility to do what amounts to a mixed-effects
> logistic regression.
> - The canonical alternative is GLMMgibbs, but there are concerns about
> this as well.
> - There has been some talk of wrappers around nlme that would add PQL (a
> technique about which I know nothing) as a way of estimating such
> equations.
> 
> Does this accurately summarize the state of software availability? If not,
> what updates should I know about? If so, what would be the costs and
> benefits of the following courses of action:
> 
> 1.) Use nlme, violating the assumption of continuous outcomes, simply
> assigning 0 and 1 as the outcome values. (A crude option, but one that has
> the distinct advantage of increased comprehensibility to sociologists, the
> research's target audience.)
> 
> 2.) Use glmmGibbs, which introduces some new assumptions and requirements
> of the data, but is perhaps the closest to a "correct" approach
> 
> 3.) Find a PQL implementation and learn enough about the technique to use
> it

Recent versions of the MASS package in the VR bundle have a glmmPQL
function that does exactly this.  Penalized Quasi-Likelihood (PQL) is
a method of fitting these models.  The name refers to the
approximation to the likelihood that is used for fitting.

I would recommend starting with glmmPQL.

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