[R] Query regarding stats/p.adjust package (base) - specifically 'Hochberg' function
m@ech|er @end|ng |rom @t@t@m@th@ethz@ch
Tue Aug 24 23:11:16 CEST 2021
>>>>> Bert Gunter
>>>>> on Tue, 24 Aug 2021 10:50:50 -0700 writes:
> 1. No Excel attachments made it through. Binary
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> 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
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> 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
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> 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 ...
> On Tue, Aug 24, 2021 at 10:39 AM David Swanepoel
> <davidswanepoel using 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,
>> 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
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