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, 2011 at 8:49 AM, terdon <> wrote: > 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 multiple testing corrections I do: > > mt <- mt.rawp2adjp(pv, proc="BH") > > Because of the size of the vector this takes a VERY long time and a LOT of > memory. I cannot split the job into smaller ones since all values in the > vector are required for the calculation. > > Can anyone think of a trick that would allow me to reduce the memory usage > of this procedure? Or t least make it faster? > > Thanks > > -- > View this message in context: > http://r.789695.n4.nabble.com/Multiple-testing-corrections-on-very-large-vector-tp3341398p3341398.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list 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.
