From maechler at stat.math.ethz.ch  Mon Jan 17 21:45:40 2005
From: maechler at stat.math.ethz.ch (Martin Maechler)
Date: Mon Jan 17 21:45:46 2005
Subject: [Rd] p.adjust(<NA>s), was "Re: [BioC] limma and p-values"
In-Reply-To: <6.2.0.14.1.20050116185753.021f73a0@imaphost.wehi.edu.au>
References: <200501071109.j07B88Mw022009@hypatia.math.ethz.ch>
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	<6.2.0.14.1.20050116185753.021f73a0@imaphost.wehi.edu.au>
Message-ID: <16876.9204.735142.310602@stat.math.ethz.ch>

>>>>> "GS" == Gordon Smyth <smyth@wehi.edu.au>
>>>>>     on Sun, 16 Jan 2005 19:44:26 +1100 writes:

    GS> The new committed version of p.adjust() contains some
    GS> problems:
    >> p.adjust(c(0.05,0.5),method="hommel")
    GS> [1] 0.05 0.50

    GS> No adjustment!

yes, but that's still better than what the current version of 
R 2.0.1 does, namely to give NA NA + two warnings ..

    GS> I can't see how the new treatment of NAs can be
    GS> justified. One needs to distinguish between NAs which
    GS> represent missing p-values and NAs which represent
    GS> unknown p-values. In virtually all applications giving
    GS> rise to NAs, the NAs represent missing p-values which
    GS> could not be computed because of missing data. In such
    GS> cases, the observed p-values should definitely be
    GS> adjusted as if the NAs weren't there, because NAs
    GS> represent p-values which genuinely don't exist.

hmm, "definitely" being a bit strong.  One could argue that
ooonoe should use multiple imputation of the underlying missing
data, or .. other scenarios.

I'll reply to your other, later, more detailed message
separately and take the liberty to drop the other points here...

Martin


[Rd] p.adjust(<NA>s), was "Re: [BioC] limma and p-values"
Martin Maechler