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())));
BIC doesn't work for glm(family=binomial()) (PR#8208)
3 messages · jusung@andrew.cmu.edu, Peter Dalgaard, Brian Ripley
jusung at andrew.cmu.edu 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.
O__ ---- Peter Dalgaard ?ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
On Sun, 16 Oct 2005, Peter Dalgaard wrote:
jusung at andrew.cmu.edu 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, ripley at stats.ox.ac.uk 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