[R] non parametric linear regression
Bert Gunter
gunter.berton at gene.com
Thu Feb 28 20:06:01 CET 2008
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: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
Behalf Of Greg Snow
Sent: Thursday, February 28, 2008 10:42 AM
To: Jeanne Vallet; r-help at 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
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
>
>
>
>
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at 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 at 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.
More information about the R-help
mailing list