From tkpmep m@iii@g oii gm@ii@com  Mon Dec 11 15:44:24 2023
From: tkpmep m@iii@g oii gm@ii@com (tkpmep m@iii@g oii gm@ii@com)
Date: Mon, 11 Dec 2023 20:14:24 +0530
Subject: [Rd] Base R wilcox.test gives incorrect answers,
 has been fixed in DescTools, solution can likely be ported to Base R
Message-ID: <00c101da2c40$89ffc220$9dff4660$@gmail.com>

While using the Hodges Lehmann Mean in DescTools (DescTools::HodgesLehmann),
I found that it generated incorrect answers (see
<https://github.com/AndriSignorell/DescTools/issues/97>
https://github.com/AndriSignorell/DescTools/issues/97). The error is driven
by the existence of tied values forcing wilcox.test in Base R to switch to
an approximate algorithm that returns incorrect results - see
<https://aakinshin.net/posts/r-hodges-lehmann-problems/>
https://aakinshin.net/posts/r-hodges-lehmann-problems/ for a detailed
exposition of the issue.

 

Andri Signorell and Cyril Moser have a new C++ implementation of
DescTools::HodgesLehmann using a O(N log(N)) algorithm due to Monahan, but
wilcox.test in Base R appears to be still broken. Will someone kindly bring
this observation, as well as the existence of a solution, to the attention
of the relevant person(s) in the Base R development team? 

 

The paper by Mohanan, as well as the original Fortran implementation of the
algorithm are linked to from
<https://github.com/AndriSignorell/DescTools/issues/97>
https://github.com/AndriSignorell/DescTools/issues/97). Inefficient O(N^2)
algorithms for the Hodges-Lehmann mean are known and are implemented in a
variety of packages. For example, the authors of rt.test
(https://cran.r-project.org/web/packages/rt.test) use the O(N^2) approach. I
suspect that Andri and Cyril will be more than happy to assist with fixing
wilcox.test in Base R with their implementation of Monahan's fast algorithm.

 

Sincerely

 

Thomas Philips

 


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[Rd] Base R wilcox.test gives incorrect answers, has been fixed in DescTools, solution can likely be ported to Base R
tkpmep m@iii@g oii gm@ii@com