Yes, I understand. If I have a distribution which is not listed in fitdistr() but I still would like to you compute the ML estimate. Would it be correct to maximize the following function?
sum( log( dens_mydistr(x, my_distr_param))) As I said, I try to step into this field by reading and trying things and I'm not sure whether I got it right how to find the ML-function of a more complex distribution... Antje On 11 February 2011 10:14, Ingmar Visser <i.vis...@uva.nl> wrote: > Antje, > > On Fri, Feb 11, 2011 at 9:58 AM, Antje Niederlein > <niederlein-rs...@yahoo.de> wrote: >> >> 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... > > The ML estimate is of course made under the assumption that the data stems > from a Poisson distribution, and under that assumption, the ML estimate is > most efficient and unbiased compared with other estimates. > > Best, Ingmar > >> >> 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.
