For those of us in the bio-medical sphere, categorical outcomes (eg, alive
/ dead) are _very_ important and , in the general outcomes literature, they
are currently associated with large data sets (250000 observations and
upwards are not at all unusual, whether these be from administrative or
purpose built data-bases). Although we can use "xtlogit", unlike "gllamm"
there is no way (?) to access empirical Bayes' estimates whilst using
"xtlogit" as with "gllapred" (and "xtreg").
This is somewhat frustrating, as, although it may be useful to demonstrate
with "xtlogit" that there is previously undiagnosed "heterogeneity", the
literature using multilevel models for (binary) outcomes proceeds to use
these EBE to illustrate important aspects of the model (the classic paper,
some time ago now, is H. Goldstein and D. J. Spiegelhalter. League tables
and their limitations: Statistical issues in the comparisons of
institutional performance. Journal of the Royal Statistical Society A 159
(Part3):385-443, 1996.)
