Young-Jin, Similar to AIC, BIC is nothing but a penalized version of loglikelihood. There is no way to tell 'how small is small' for BIC unless you compare BIC from one model with BIC from another model.
On 3/6/06, Young-Jin Lee <[EMAIL PROTECTED]> wrote: > > Dear R-List > > I have a question about how to interpret BIC as a goodness-of-fit > statistic. > I was trying to use "EMclust" and other "mclust" library and found that > BIC > was used as a goodness-of-fit statistic. > Although I know that smaller BIC indicates a better fit, it is not clear > to > me how good a fit is by reading a BIC number. Is there a standard way of > interpreting a BIC value? > > Thanks in advance. > > [[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 > -- WenSui Liu (http://statcompute.blogspot.com) Senior Decision Support Analyst Health Policy and Clinical Effectiveness Cincinnati Children Hospital Medical Center [[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
