Ben, I think the reference you're searching for is the one below @ARTICLE{Lebreton1992, author = {Lebreton, J.-D. and Burnham, K. P. and Clobert, J. and Anderson, D. R.}, title = {Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies}, journal = {Ecological {M}onographs}, year = {1992}, volume = {62}, pages = {67-118}, keywords = {Modeling, survival, Capture-recapture}, pdf = {Lebreton1992.pdf} }
Cheers, C Selon Ben Bolker <[EMAIL PROTECTED]>: > Marc Schwartz <marc_schwartz <at> comcast.net> writes: > > > > > on 10/31/2008 01:07 PM Antonio.Gasparrini <at> lshtm.ac.uk wrote: > > > > I'm trying to extract the AIC statistic from a GLM model > > >with quasipoisson link. > > > The formula I'm referring to is > > > > > > AIC = -2(maximum loglik) + 2df * phi > > > > > > with phi the overdispersion parameter, as reported in: > > > > > > Peng et al., Model choice in time series studies os air pollution and > mortality. J R Stat Soc A, 2006; 162: > > pag 190. > > > > > I was under the impression that there is no log likelihood for quasi* > > family models, thus no AIC, which is why they are not calculated/printed > > in the glm() summary outputs. > > > > Yes, but ... this is a matter of some disagreement. > > Long answer: The purist > position (hi Prof. Ripley) is that quasi-likelihood estimation > does not produce a likelihood and should not return one. > A common position in applied statistics (I think starting with > a paper by Lebreton, but I can't find the ref right now: > see refs below) is that dividing the log-likelihood of a regular > likelihood fit by the estimated scale (overdispersion) parameter > of the quasi- variant gives a "quasilikelihood" that can be > used to compute a quasi-AIC that can then be used in model > selection. > > Short answer: I think that if you fit the non-quasi version > of the model (ie. Poisson family in your case) and extract > the likelihood from it, then divide by the overdispersion > parameter estimated from the "quasi" variant, that should > give you what you want. > > By the way, the formula quoted above looks funny. > Shouldn't it be > > QAIC = -2(maximum loglik)/phi + 2df > > ? The formula quoted above (phi times my > version) should give the same ordering, but > model weights and interpretations of QAIC > differences will be wrong. > > cheers > Ben Bolker > > > Anderson, D. R., K. P. Burnham, and G. C. White. 1994. AIC model selection in > overdispersed capture-recapture data. Ecology 75, no. 6: 1780-1793. > > Richards, Shane A. 2008. Dealing with overdispersed count data in applied > ecology. Journal of Applied Ecology 45: 218-227. > doi:10.1111/j.1365-2664.2007.01377.x. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.