The central question is: What caused the 3 unusual values? What is their scientific relevance? Only you can answer that, not us.
-- Bert On Tue, Aug 13, 2013 at 8:51 AM, Marta Lomas <lomasv...@hotmail.com> wrote: > Thanks for your interest and prompt answer! > > What I try to estimate is the correlation of one bird species counts with a > set of environmental parameters. The count data are zero-inflated and > overdispersed. I am modeling with hurdle-negative binomial-mixed effects. > The results are very difficult to interpret and it get easier dropping out 3 > outliers. But I do not know if I should do this.. > Thanks! > Marta > > >> Subject: Re: [R] Outliers and overdispersion >> From: szehn...@uni-bonn.de >> Date: Tue, 13 Aug 2013 17:41:10 +0200 >> CC: r-help@r-project.org >> To: lomasv...@hotmail.com >> >> I do not know what you are exactly estimating, but if it is about count >> models and the model fit gets better when you drop the outliers, it does not >> say, that the model is now more correct. It just says, if the data were >> without the outliers, this model would fit good. >> >> Overdispersion in count data is sometimes a cue, that you have a mixture >> distribution as the generating process - for example instead of one, K >> different (sub)species of birds which were aggregated in the count data. In >> this case a mixture (negative binomial)- distribution with K components >> could fit the data better. >> >> >> Best >> >> Simon >> >> On Aug 13, 2013, at 5:28 PM, Marta Lomas <lomasv...@hotmail.com> wrote: >> >> > >> > >> > >> > Hi again, >> > >> > I have a question on some outliers that I have in my response variable >> > (wich are bird counts). At the beginning I did not drop them >> > out because they are part of the normal counts and I considered them >> > "ecologically" correct. >> > >> > However, I >> > tried some of the same models without ouliers and the AICs are thus >> > better. I >> > also have nice significances this way... >> > >> > >> > So would you say that, even though the outliers are right >> > observations and taking into consideration that already the negative >> > binomial >> > distribution that I am using is accounting for the some of the >> > overdispersion due to the outliers, it is >> > better to drop them out as the models fit better this way? >> > >> > >> > Thanks for your patience! >> > >> > :) >> > >> > >> > >> > >> > >> > >> > [[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. >> > > [[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. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ 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.
