There are, however, the multcomp and multcompView packages that
might provide something of interest in this regard. "multcomp" has a
companion book, "Multiple Comparisons Using R" (Bretz, Hothorn,
Westfall, 2010, CRC Press), which I believe provides an excellent
overview of the state of the art in multiple comparisons. The simple
rule is Bonferroni, which involves multiplying the p-values by n or
n(n-1)/2.
Note, also, that one of the most important innovations in
statistical methods of the past quarter century is the development of
"false discovery rate", which estimates the false alarm rate among the
cases that the user actually sees, which is a mixture of true and false
hypotheses. The p value, by contrast, is the probability of a decision
error only among hypotheses that are true.
For more info, see the Wikipedia entries on Bonferroni or false
discovery rate -- or the book by Bretz, Hothorn and Westfall or the
vignettes accompanying the multcomp package.
Hope this helps.
Spencer
On 4/4/2011 8:54 AM, Bert Gunter wrote:
1. This is not an R question, AFAICS.
2. Sounds like a research topic. I don't think there's a meaningful
simple answer. I suspect it strongly depends on the model and context.
-- Bert
On Mon, Apr 4, 2011 at 8:02 AM, January Weiner
<january.wei...@mpiib-berlin.mpg.de> wrote:
Dear all,
I have an n x n matrix of p-values. The matrix is symmetrical, as it
describes the "each against each" p values of correlation
coefficients.
How can I best correct the p values of the matrix? Notably, the total
number of the tests performed is n(n-1)/2, since I do not test the
correlation of each variable with itself. That means, I only want to
correct one half of the matrix, not including the diagonal. Therefore,
simply writing
pmat<- p.adjust( pmat, method= "fdr" )
# where pmat is an n x n matrix
...doesn't cut it.
Of course, I can turn the matrix in to a three column data frame with
n(n-1)/2 rows, but that is slow and not elegant.
regards,
j.
--
-------- Dr. January Weiner 3 --------------------------------------
Max Planck Institute for Infection Biology
Charitéplatz 1
D-10117 Berlin, Germany
Web : www.mpiib-berlin.mpg.de
Tel : +49-30-28460514
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Spencer Graves, PE, PhD
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