On Sep 12, 2015, at 2:32 AM, Juli wrote: > Hi Jim, > > thank you for your help. :) > > My point is, that there are outlier and I don´t really know how to deal with > that. > > I need the dataframe for a regression and read often that only a few outlier > can change your results very much. In addition, regression diacnostics > didn´t indcate me the best results. > Yes, and I know its not the core of statistics to work in a way you get > results you would like to have ;). > > So what is your suggestion? > > And if I remove the outliers, my problem ist, that as you said, they differ > in length. I need the data frame for a regression, so can I remove the whole > column or is there a call to exclude the data?
Most regression methods have a 'subset' parameter which would allow you to distort the data to your desired specification. But why not think about examining a different statistical model or using robust methods? That way you can keep all your data. (Sounds like you don't really have a lot.) -- David. > > JULI > > > > -- > View this message in context: > http://r.789695.n4.nabble.com/removing-outlier-tp4712137p4712170.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. David Winsemius Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.