Sorry for my ignorance, but could you explain a little further? I'm guessing from your response that this makes the log-likelihood that is quoted by glmmPQL a poor measure of model fit. Are there are statistics that would be better for reporting model fit?
thanks. ---------------------------------------------------------------------- Andrew J Perrin - http://www.unc.edu/~aperrin Assistant Professor of Sociology, U of North Carolina, Chapel Hill [EMAIL PROTECTED] * andrew_perrin (at) unc.edu On Fri, 21 Nov 2003, Prof Brian Ripley wrote: > glmmPQL does not fit by maximum likelihood, and what is being quoted is > not a likelihood for the original problem. > > On Fri, 21 Nov 2003, Andrew Perrin wrote: > > > Greetings- > > > > a reviewer for a paper of mine noted an anomaly in some models I ran using > > glmmPQL (from the MASS package). Specifically, the models are three-level > > hierarchical probit models estimated using PQL under R. The anomaly is > > that the log-likelihoods decrease (or, alternatively -2logLik increases) > > as variables are added to the null model. This is unusual, and I'm trying > > to figure out how to interpret it. I've found some indication (e.g., at > > http://www.ssicentral.com/hlm/hlm00150.htm) that PQL estimation doesn't > > produce meaningful log-likelihoods, but I'm suspicious of that claim. Any > > comments or advice would be helpful. > > -- > Brian D. Ripley, [EMAIL PROTECTED] > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ > University of Oxford, Tel: +44 1865 272861 (self) > 1 South Parks Road, +44 1865 272866 (PA) > Oxford OX1 3TG, UK Fax: +44 1865 272595 > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
