I believe this is more a question for SO (stats.stackexchange.com). There are many possible goodness of fit statistics that can easily be calculated in R, but I think the fundamental question is: To what end? First, there are probably several parametric distributions that give (essentially) equally good fits; and second, you may want none of them, preferring some sort of nonparametric fit. Again, the sort of thing that is probably better at SO -- or even better, with a local statistician.
Cheers, Bert Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 "Data is not information. Information is not knowledge. And knowledge is certainly not wisdom." H. Gilbert Welch On Mon, Feb 10, 2014 at 12:25 AM, Alaios <ala...@yahoo.com> wrote: > Hi all, > I have a large number of measurements from which I select a large number of > unique vectors. For each vectors I would like to test which distribution > might be a candidate for fitting. > It is impossible to look on each vector separately but I can inside a for > loop test different models and based on their goodness of fit to make offline > decisions (I will be saving goodness of fits results on a text file). > > Do you know given a vector how I can get the goodness of fit for the "basic" > distributions : "norm", "lnorm", "pois", "exp", "gamma", "nbinom", > "geom", "beta", "unif" and "logis" > > Is it possible to try many of those (or at least some of the above) and try > to get these results? > > Regards > Alex > [[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.
