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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