See if rowttests is any faster.
library(genefilter)
?rowttests
You have to install Bioconductor. I've used this on large datasets,
but I haven't compared
timings.
On Mon, Sep 13, 2010 at 4:26 PM, Alexey Ush <usha...@yahoo.com> wrote:
> Hello,
>
> I have a question regarding how to speed up the t.test on large dataset. For
> example, I have a table "tab" which looks like:
>
> a b c d e f g h....
> 1
> 2
> 3
> 4
> 5
>
> ...
>
> 100000
>
> dim(tab) is 100000 x 100
>
>
>
> I need to do the t.test for each row on the two subsets of columns, ie to
> compare a b d group against e f g group at each row.
>
>
> subset 1:
> a b d
> 1
> 2
> 3
> 4
> 5
>
> ...
>
> 100000
>
>
> subset 2:
> e f g
> 1
> 2
> 3
> 4
> 5
>
> ...
>
> 100000
>
> 100000 t.test's for each row for these two subsets will take around 1 min.
> The prblem is that I have around 10000 different combinations of such a
> subsets. therefore 1min*10000
> =10000min in the case if I will use "for" loop like this:
>
> n1=10000 #number of subset combinations
> for (i1 in 1:n1) {
>
> n2=100000 # number of rows
> i2=1
> for (i2 in 1:n1) {
> t.test(tab[i2,v5],tab[i2,v6])$p.value #v5 and v6 are vectors
> containing the veriable names for the two subsets (they are different for
> each loop)
> }
>
> }
>
>
> My question is there more efficient way how to do this computations in a
> short period of time? Any packages, like plyr? May be direct calculations
> isted of using t.test function?
>
>
> Thank you.
>
>
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
>
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