Date:         Wed, 6 Apr 2005 09:28:24 -0700
Reply-To:     Chris Maloof <cjmaloof@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Chris Maloof <cjmaloof@GMAIL.COM>
Organization: http://groups.google.com
Subject:      Re: Biased prob. estimates in random logistic regression?
Comments: To: sas-l@uga.edu
Content-Type: text/plain; charset="iso-8859-1"

Hmm, I may not have been very clear.  It's the random effects that are
causing the problem, and I can't leave them out because they're
important to modeling several of our data sets.  The two item types are
kind of a red herring, in the sense that the model still gives
biased-looking estimates with only one item type, when the intercept
and random effect size are the only parms.

My expectation for the simulation is that the estimate ought to be an
unbiased estimate of the actual mean in the generated data set (not
necessarily .2 or .9) -- that is, it's about equally likely to be too
high and too low.  Right now, the estimate is always too much toward
the extreme probability (0 or 1).  I might well be confused somewhere
though, since this does seem like about the simplest possible use of
NLMIXED with binary outcomes and random effects.