Hi terdon,
Very happy to help and know it worked.
Honestly, I do not know exactly where the differences are, but it is not
hard to check the sources and compare both algorithms. When doing this, you
can can see that p.adjust() uses vectorization whereas mt.rawp2adjp() does
not. Perhaps that is th
Hi Jorge,
first of all THANKS! I just ran you suggestion and got blown away:
system.time(res <- p.adjust(pv, method = 'fdr'))
user system elapsed
55.052 3.100 62.560
I had tried the same thing using mt.rawp2adjp as per my original post, sent
it to a cluster here on friday afternoo
Hi terdon,
You are absolutely right. I apologize for any inconvenience my lack of
coffee might have caused :-)
I simulated some p-values with the length of your vector and ran the
p.adjust() function on them. Here is what I got:
system.time(res <- p.adjust(pv, method = 'fdr'))
user system el
Hi Jorge, and thanks for your answer, it looks promising.
However, I have a question. First of all, I am a lowly biologist so please
excuse any horrible errors of understanding I may make.
So, the BH correction depends on, among other things, sorting the vector of
P-values from the smallest to th
Hi terdon,
Take a look at ?p.adjust and its argument n. For example, you could adjust
B pv values by using
p.adjust(pv[1:B], method = 'BH', n = B)
Then, you can continue processing other subsets of pv and concatenate the
result. Here, a for() loop might be useful.
HTH,
Jorge
On Tue, Mar 8,
Hello all,
I am calculating probabilities of association for terms in the
GeneOntology database. I have ~4e7 probabilities and I would like to correct
for multiple testing. At the moment I have something like the following:
pv is a vector containing my 4e7 probabilities.
To run the multipl
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