Antje Niederlein <niederlein-rstat <at> yahoo.de> writes: > > Hi Ingmar, hi Dennis, > > okay, you're right. I was expecting that the result would give the > best fit to my data even if it's not a real poisson distribution. It > looks somehow similar... > But how to judge the goodness of fit? I was using the residual sum of > squares. I'm not a statistician, so I'm not sure whether this method > is the one to choose... > If I estimate lambda with mle2() and use the RSS as criteria to > minimize, my lambda is much smaller that with fitdistr(). >
There are many ways to define the "best fit"; RSS is one reasonable option, maximum likelihood (which in the case of a Poisson distribution is equivalent to least-squares weighted by a variance that is equal to the expected mean, i.e. (y.obs-y.fitted)^2/y.fitted) is another. Which you choose really depends on why you are calculating the estimates in the first place/ what you intend to use them for, although for Poisson data maximum likelihood approaches are more widely accepted. ______________________________________________ [email protected] 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.
