>>>>> Bert Gunter >>>>> on Tue, 24 Aug 2021 10:50:50 -0700 writes:
> 1. No Excel attachments made it through. Binary > attachments are generally stripped by the list server for > security reasons. > 2. As you may have already learned, this is the wrong > forum for statistics or package specific questions. Read > *and follow* the posting guide linked below to post on > r-help appropriately. In particular, for questions about > specific non-standard packages, contact package > maintainers (found through e.g. ?maintainers) > 3. Statistics issues generally don't belong here. Try > stats.stackexchange.com instead perhaps. > 4. We are not *R Core development,* and you probably > should not be contacting them either. See here for > general guidelines for R lists: > https://www.r-project.org/mail.html > Bert Gunter > "The trouble with having an open mind is that people keep > coming along and sticking things into it." -- Opus (aka > Berkeley Breathed in his "Bloom County" comic strip ) Well, this was a bit harsh of an answer, Bert. p.adjust() is a standard R function (package 'stats') -- as David Swanepoel did even mention. I think he's okay asking here if the algorithms used in such a standard R functions are "ok" and how/why they seemlingly differ from other implementations ... Martin > On Tue, Aug 24, 2021 at 10:39 AM David Swanepoel > <davidswanep...@hotmail.com> wrote: >> >> Dear R Core Dev Team, I hope all is well your side! My >> apologies if this is not the correct point of contact to >> use to address this. If not, kindly advise or forward my >> request to the relevant team/persons. >> >> I have a query regarding the 'Hochberg' method of the >> stats/p.adjust R package and hope you can assist me >> please. I have attached the data I used in Excel, which >> are lists of p-values for two different tests (Hardy >> Weinberg Equilibrium and Linkage Disequilibrium) for four >> population groups. >> >> The basis of my concern is a discrepancy specifically >> between the Hochberg correction applied by four different >> R packages and the results of the Hochberg correction by >> the online tool, >> MultipleTesting.com<http://www.multipletesting.com/>. >> >> Using the below R packages/functions, I ran multiple test >> correction (MTC) adjustments for the p-values listed in >> my dataset. All R packages below agreed with each other >> regarding the 'significance' of the p-values for the >> Hochberg adjustment. >> >> >> * stats/p.adjust (method: Hochberg) * mutoss/hochberg * >> multtest/mt.rawp2adjp (procedure: Hochberg) * elitism/mtp >> (method: Hochberg) >> >> In checking the same values on the MultipleTesting.com, >> more p-values were flagged as significant for both the >> HWE and LD results across all four populations. I show >> these differences in the Excel sheet attached. >> Essentially, using the R packages, only the first HWE >> p-value of Pop2 is significant at an alpha of 0.05. Using >> the MT.com tool, however, multiple p-values are shown to >> be significant across both tests with the Hochberg >> correction (the highlighted cells in the Excel sheet). >> >> >> I asked the authors of MT.com about this, and they gave >> the following response: >> >> "we have checked the issue, and we believe the >> computation by our page is correct (I cannot give opinion >> about the other packages). When we look on the original >> Hochberg paper, and we only use the very first (smallest) >> p value, then m"=1, thus, according to the equation in >> the Hochberg 1988 paper, in this case practically there >> is no further correction necessary. In other words, in >> case the *smallest* p value is smaller than alpha, then >> the *smallest* p value will remain significant >> irrespective of the other p values when we make the >> Hochberg correction." >> >> I have attached the Hochberg paper here but, >> unfortunately, I don't understand enough of the stats to >> verify this. I have applied their logic on the same Excel >> sheet under the section "MT.com explanation", which shows >> why they consider the highlighted values significant. >> >> I have also attached the 2 R files that I used to do the >> MTC runs and they can be run as is. They are just quite >> long as they contain many of the other MTC methods in the >> different packages too. >> >> Kindly provide your thoughts as to whether you agree with >> this interpretation of the Hochberg paper or not? I would >> like to see concordance between the MT.com tool and the >> different R packages above (or understand why they are >> different), so that I can be more confident in the >> explanations of my own results as a stats layman. >> >> I hope this makes sense. Please let me know if I need to >> clarify anything. >> >> >> Many thanks and kind regards, David >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and >> more, see 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@r-project.org mailing list -- To UNSUBSCRIBE and > more, see 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@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.