On Sun, 16 Oct 2005, Peter Dalgaard wrote: > [EMAIL PROTECTED] writes: > >> Full_Name: Ju-Sung Lee >> Version: 2.2.0 >> OS: Windows XP >> Submission from: (NULL) (66.93.61.221) >> >> >> BIC() requires the attribute $nobs from the logLik object but the logLik of a >> glm(formula,family=binomial()) object does not include $nobs. Adding >> attr(obj,'nobs') = value, seems to allow BIC() to work. >> >> Reproducing the problem: >> library(nmle); >> BIC(logLik(glm(1~1,family=binomial()))); > > It is not clear to me that "nobs" is a well-defined concept for > arbitrary likelihood functions. In particular, binomial models are > tricky: Is "13 successes in 79 trials" one (binomial) observation or > 79 (Bernoulli) ones?? > > So BIC may not be defined. In which sense is this a bug, anyway? The > BIC function is defined inside the nlme package which is not designed > to work with anything but continuous data.
Schwarz originally introduced BIC only for linear regressions (and in essentially the random regressors case as I recall). It is perhaps worth pointing out that 'nobs' (and hence BIC) is not well-defined for a linear mixed model either: the appropriate multiplier suggested by the theory depends on the type of asymptotics which are assumed. -- 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 ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
