non parametric linear regression

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non parametric linear regression

Jeanne Vallet
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.htm> 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/sreg.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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Re: non parametric linear regression

Greg Snow-2
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]]
>
> ______________________________________________
> [hidden email] 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.
>

______________________________________________
[hidden email] 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.
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Re: non parametric linear regression

Bert Gunter
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]]
>
> ______________________________________________
> [hidden email] 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.
>

______________________________________________
[hidden email] 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.

______________________________________________
[hidden email] 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.