Re: [R] non parametric linear regression

From: Greg Snow <>
Date: Thu, 28 Feb 2008 11:41:35 -0700

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
(801) 408-8111

> -----Original Message-----
> From:
> [] On Behalf Of Jeanne Vallet
> Sent: Thursday, February 28, 2008 7:07 AM
> To:
> 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
> <
> m> Kendall's t is constructed for the slope. Non-parametric
> linear regression is much less sensitive to extreme
> observations (outliers) than is
> <
> 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
> <> 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 (
> <> Conover, 1999)."
> Thanks in advance!
> Regards,
> Jeanne Vallet
> PhD student,
> Angers, France
> [[alternative HTML version deleted]]
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> and provide commented, minimal, self-contained, reproducible code.
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Received on Thu 28 Feb 2008 - 18:44:28 GMT

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