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:

[hidden email] [mailto:

[hidden email]] On

Behalf Of Greg Snow

Sent: Thursday, February 28, 2008 10:42 AM

To: Jeanne Vallet;

[hidden email]
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

[hidden email]
(801) 408-8111

> -----Original Message-----

> From:

[hidden email]
> [mailto:

[hidden email]] On Behalf Of Jeanne Vallet

> Sent: Thursday, February 28, 2008 7:07 AM

> To:

[hidden email]
> 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]]

>

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>

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.

>

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