Liset Dat moet lukken en hoe sneller je het anlevert hoe eerder je resultaten terug ziet. Dus, Ik wacht af.
groet Peter S Date sent: Sun, 17 Apr 2005 18:07:28 +0100 (BST) From: Prof Brian Ripley <[EMAIL PROTECTED]> To: Deepayan Sarkar <[EMAIL PROTECTED]> Subject: Re: [R] generalized linear mixed models - how to compare? Copies to: [email protected], Nestor Fernandez <[EMAIL PROTECTED]> > On Sun, 17 Apr 2005, Deepayan Sarkar wrote: > > > On Sunday 17 April 2005 08:39, Nestor Fernandez wrote: > > >> I want to evaluate several generalized linear mixed models, including > >> the null model, and select the best approximating one. I have tried > >> glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result > >> in similar parameter estimates but fairly different likelihood > >> estimates. > >> My questions: > >> 1- Is it correct to calculate AIC for comparing my models, given that > >> they use quasi-likelihood estimates? If not, how can I compare them? > >> 2- Why the large differences in likelihood estimates between the two > >> procedures? > > > > The likelihood reported by glmmPQL is wrong, as it's the likelihood of > > an incorrect model (namely, an lme model that approximates the correct > > glmm model). > > Actually glmmPQL does not report a likelihood. It returns an object of > class "lme", but you need to refer to the reference for how to interpret > that. It *is* support software for a book. > > > GLMM uses (mostly) the same procedure to get parameter estimates, but as > > a final step calculates the likelihood for the correct model for those > > estimates (so the likelihood reported by it should be fairly reliable). > > Well, perhaps but I need more convincing. The likelihood involves many > high-dimensional non-analytic integrations, so I do not see how GLMM can > do those integrals -- it might approximate them, but that would not be > `calculates the likelihood for the correct model'. It would be helpful to > have a clarification of this claim. (Our experiments show that finding an > accurate value of the log-likelihood is difficult and many available > pieces of software differ in their values by large amounts.) > > Further, since neither procedure does ML fitting, this is not a maximized > likelihood as required to calculate an AIC value. And even if it were, > you need to be careful as often one GLMM is a boundary value for another, > in which case the theory behind AIC needs adjustment. > > -- > 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://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Stichting NIVEL / NETHERLANDS INSTITUTE OF PRIMARY HEALTH CARE Peter Spreeuwenberg P.O. Box 1568 3500 BN Utrecht Netherland E-mail : [EMAIL PROTECTED] Direct : +31-30-2729678 General : +31-30-2729700 Fax : +31-30-2729729 Web-site http://www.nivel.nl ____________________________DISCLAIMER__________________________ This message contains information that may be privileged or\...{{dropped}} ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
