Check out package quantreg for quantile regression (including medians) and at least packages MASS and robust for robust regression.
-- Bert Gunter Genentech -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Greg Snow Sent: Thursday, February 28, 2008 10:42 AM To: Jeanne Vallet; r-help@r-project.org Subject: Re: [R] non parametric linear regression These methods are more commonly called robust regression or resistant regression (it is not really non-parametric since you are trying to estimate the slope which is a parameter, just not of a normal distribution). There are many methods for doing robust regressions, the book Modern Applied Statistics with S (MASS) has a good discussion on some different techniques. Running the command: > RSiteSearch("median regression") Gives several hits, one of which is the mblm function in the mblm package which, based on its description, does the calculations you mention. Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [EMAIL PROTECTED] (801) 408-8111 > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Jeanne Vallet > Sent: Thursday, February 28, 2008 7:07 AM > To: r-help@r-project.org > Subject: [R] non parametric linear regression > > Dear all, > > I am looking for if non parametric linear regression is > available in R. The method I wish to use is described in the > help of statsdirect statistical software like this : "This is > a distribution free method for investigating a linear > relationship between two variables Y (dependent, outcome) and > X (predictor, independent). The slope b of the regression > (Y=bX+a) is calculated as the median of the gradients from > all possible pairwise contrasts of your data. A confidence > interval based upon > <http://www.statsdirect.com/help/nonparametric_methods/kend.ht > m> Kendall's t is constructed for the slope. Non-parametric > linear regression is much less sensitive to extreme > observations (outliers) than is > <http://www.statsdirect.com/help/regression_and_correlation/sr > eg.htm> simple linear regression based upon the least squares > method. If your data contain extreme observations which may > be erroneous but you do not have sufficient reason to exclude > them from the analysis then non-parametric linear regression > may be appropriate. This function also provides you with an > approximate two sided Kendall's rank correlation test for > independence between the variables. Technical Validation : > Note that the two sided confidence interval for the slope is > the inversion of the two sided Kendall's test. The > approximate two sided P value for Kendall's t or tb is given > but the > <http://www.statsdirect.com/help/distributions/pk.htm> exact > quantile from Kendall's distribution is used to construct the > confidence interval, therefore, there may be slight > disagreement between the P value and confidence interval. If > there are many ties then this situation is compounded ( > <http://www.statsdirect.com/help/references/refs.htm> Conover, 1999)." > > Thanks in advance! > > > > Regards, > > Jeanne Vallet > > PhD student, > > Angers, France > > > > > > > [[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. ______________________________________________ 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.
