Olivier, type ?AIC and have a look at the description
Description: Generic function calculating the Akaike information criterion for one or several fitted model objects for which a log-likelihood value can be obtained, according to the formula -2*log-likelihood + k*npar, where npar represents the number of parameters in the fitted model, and k = 2 for the usual AIC, or k = log(n) (n the number of observations) for the so-called BIC or SBC (Schwarz's Bayesian criterion). AIC = -2*log-likelihood + k*npar can be negative as SBC, too. Hannu On 9/7/07, Olivier MARTIN <[EMAIL PROTECTED]> wrote: > > Hi all, > > > I obtained negative values for AIC and BIC criteria for a particular > model that I have > developped... > > I don't remember to have negative values for these crietria for others > applications, so I am a > little suprised... Could anyone tell me if something is wrong or his > conclusion concerning my model? > > Best regards, > Olivier. > > ______________________________________________ > R-help@stat.math.ethz.ch 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch 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.