SamL wrote (excerpted):
I am also hoping that David is wrong about stata leaving it to gllamm to
work for non-linear mixed models, although this is clearly the short-term
situation. Gllamm is a godsend, but it is also very difficult to be sure
one is actually estimating what one wants to estimate--I've noticed
several queries on statalist that have that flavor. This may be because
gllamm is not integrated into stata and thus may not follow stata
conventions. I am not sure. Maybe gllamm does follow the conventions
closely, but that it is so flexible that it is difficult to document.
Whatever the reason, it seems very slow and very opaque.
At any rate, even if gllamm runs faster, nothing will substitute for
having a module for non-linear mixed models that is actually written and
supported by stata. It is my sincere hope that in a few releases (10?
11?) such will be the case. Multi-level modelling of discrete outcomes is
pretty mainstream now, for good or ill. It is my hope it will become
mainstreamed into stata 10 or stata 11 as well.
[edited]
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There are quasilikelihood approaches to fitting multilevel generalized linear
models that are faster than -gllamm- and that might be more suitable than
-gllamm- for certain circumstances, but these have their own foibles. In
addition, -gllamm- is *much* more flexible and capable than your average
penalized quasilikelihood implementation. Even so, quasilikelihood methods
should become more accessible with Mata.
But -gllamm- is not the general-purpose nonlinear mixed effects model-fitting
routing that SAS's PROC NLMIXED is, at least not yet. (You can get marginal
nonlinear models now using -ml- with -cluster()-; this kind of approach will
become more user-friendly with Stata 9's new -nl, cluster()-.) SamL's comment
that the list gets queries by users unsure of what they're getting with -gllamm-
might be taken to heart by StataCorp: it's probably better to get -xtmixed-
into the hands of Stata users and see how much support is needed with the
simpler case before venturing into nonlinear mixed effects models. And after
users are accustomed to the steps to be taken when encountering models that
settle toward negative variance components (which appear to be constrained to
zero by the parameterization that -xtmixed- uses) in order to fit the data,
then Stata Corp can proceed to devote resources to embellish -xtmixed- with,
say, a bigger smorgasbord of covariance structures (if there's enough user
interest--what's already there will likely handle anything I'd ever confront)
or denominator degrees of freedom estimations.
Joseph Coveney
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