[R] non parametric linear regression

[Greg Snow]

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
greg.snow at imail.org
(801) 408-8111
 
 

> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of Jeanne Vallet
> Sent: Thursday, February 28, 2008 7:07 AM
> To: r-help at 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
> 
>  
> 
>  
> 
> 
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> 
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