I didn't actually see any question in this posting, but instead of removing the outliers consider using a robust linear model.
library(MASS) ?rlm The TeachingDemos package has a data set called outliers to show what can happen when you iteratively remove "outliers" in the way you suggest. ------------------------------------- David L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77840-4352 ----- Original Message ----- From: "Lauren Vogric" <lvog...@grahamcapital.com> To: r-help@r-project.org Sent: Friday, July 13, 2012 1:36:43 PM Subject: [R] Fitting data and removing outliers What I'm trying to do is create best fit line in R for a set of data points and then remove all the outliers to re-create a best fit. I can't use IQR because the outliers I have in mind are easily within the range, but way out of line for the best fit, which is ruining the fit. I'd rather throw out those points all together. Thanks! [[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.
