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There is nothing I know of, which I find surprising. If you look
at Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2005). Maximum likelihood
estimation of limited and discrete dependent variable models with nested
random effects. Journal of Econometrics, 128(2), 301?323 they compare
different number of quadrature points starting at 5, and find that they
need more in situations where the clusters are small and random effects
variance high. In Rabe-Hesketh and Skrondal's  book "Multilevel and
Longitudinal Modeling Using Stata" they recommend a minimum of 5 adaptive
quadrature points for binary data. Obviously not peer reviewed but this
entry from Andrew Gelmans blog is interesting
http://andrewgelman.com/2010/09/10/r_vs_stata_or_d/ The best strategy is
what I was taught with non-adaptive Gauss_Hermite and that is to increase
the quadrature points and see if it makes a difference. If it doesn't then
you have the right number of quadrature points.

One of my experience with this was fitting some unusual random effects
models to binary data using adaptive Gauss-Hermite, using routines I wrote.
I wondered why I needed 21 points, so I plotted the log-likelihood for a
cluster and one side was very steep (almost cliff-like) compared to the
other, so it just wasn't going to work.
On 6 November 2014 08:42, Ben Bolker <bbolker at gmail.com> wrote: