If v is your vector of sample variances (and assuming that their distribution is chi-square) you can define f(df) <- sum(dchisq(v,df,log=TRUE)) and now you need to maximize f, which can be done using any optimization function (like optim).
--- On Sat, 26/7/08, Julio Rojas <[EMAIL PROTECTED]> wrote: > From: Julio Rojas <[EMAIL PROTECTED]> > Subject: [R] Chi-square parameter estimation > To: r-help@r-project.org > Received: Saturday, 26 July, 2008, 12:03 AM > Hi. I have made 100 experiments of an M/M/1 queue, and for > each one I have calculated both, mean and variance of the > queue size. Now, a professor has told me that variance is > usually chi-squared distributed. Is there a way in R that I > can find the parameter that best fits a chi-square to the > variance data? I know there's fitdistr()m but this > function doesn't handle chi-square. I believe the mean > estimator for the chi-square is df (degrees of freedom). > The theoretical variance for an M/M/1 queue with > lambda=30/33 is ~108. So, should I expect the chi-square > with parameter 108 is the one that best fits the data? > > Thanks a lot for your help. > > > > > > > ____________________________________________________________________________________ > Yahoo! MTV Blog & Rock >¡Cuéntanos tu > historia, inspira una canción y gánate un viaje a los > Premios MTV! Participa aquí http://mtvla.yahoo.com/ > [[alternative HTML version > deleted]]______________________________________________ > R-help@r-project.org 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. ______________________________________________ R-help@r-project.org 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.
