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().
I'm happy about any suggestion! Antje On 11 February 2011 09:16, Ingmar Visser <i.vis...@uva.nl> wrote: > The ML estimate of lambda is the mean, so no need for (iterative) > optimization. See eg: > http://mathworld.wolfram.com/MaximumLikelihood.html > hth, Ingmar > > On Fri, Feb 11, 2011 at 8:52 AM, Antje Niederlein > <niederlein-rs...@yahoo.de> wrote: >> >> Hello, >> >> I tried to fit a poisson distribution but looking at the function >> fitdistr() it does not optimize lambda but simply estimates the mean >> of the data and returns it as lambda. I'm a bit confused because I was >> expecting an optimization of this parameter to gain a good fit... >> If I would use mle() of stats4 package or mle2() of bbmle package, I >> would have to write the function by myself which should be optimized. >> But what shall I return? >> >> -sum((y_observed - y_fitted)^2) >> >> ? >> >> Any other suggestions or comments on my solution? >> >> Antje >> >> ______________________________________________ >> 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.
