[R] non parametric linear regression

From: Jeanne Vallet <jeanne.vallet_at_inh.fr>
Date: Thu, 28 Feb 2008 15:06:31 +0100


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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