AIC is calculated in many places in R, but I do not believe any use that formula.
Here is a guess as to your confusion: in linear models there are p coefficients plus sigma^2 to be estimated and hence there is often an extra 2 associated with the scale parameter. For example, in > gaussian()$aic function (y, n, mu, wt, dev) sum(wt) * (log(dev/sum(wt) * 2 * pi) + 1) + 2 Beyond that, additive constants do not matter in comparing AIC between models and the defn of log-likelihood is only up to an additive constant. So sometimes calculations omit constants common to all models: extractAIC.lm does, for example. On Tue, 9 Mar 2004, Stephen Dicey wrote: > could somebody refer me to the reason R uses > > -2*loglik + 2*(#param)+2 > > to calculate AIC? -- 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 PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
