[R] how to subsample all possible combinations of n species taken 1:n at a time?
jim holtman
jholtman at gmail.com
Mon Apr 6 21:57:20 CEST 2009
Are you just trying to obtain a combination from 25 possible terms?
If so, then just sample the number you want and convert the number to
binary:
> sample(33554432,100)
[1] 6911360 5924262 23052661 12888381 25831589 16700013 24079278
33282839 12751862 26086726 31363494 7118320 21866536 4212929
[15] 8966435 12955834 449305 12830805 29181967 11420211 16175915
20118079 16560488 6248422 27762022 22430005 26650247 3621985
[29] 24283690 13800068 27546362 21711718 26270840 18556802 17774422
26486373 782865 16013167 24572344 23244187 16026237 28897360
[43] 14700082 8214024 2371593 3337527 10612303 17402454 22213173
13650936 30630988 9851680 15403666 11153297 21839554 8657593
[57] 16057288 25713076 2826853 29370859 11377380 28166893 11632747
11199608 15983665 29937151 29002363 13085852 26082502 32232925
[71] 14584722 23907975 13421556 10916983 25403574 6801209 23861215
4083294 8237209 4808486 8040610 1977505 21551566 29402643
[85] 26135975 26753178 15276437 13760103 27208220 20298140 21968831
11851302 9068401 33308858 21256448 7154058 4341004 16042933
[99] 31006704 20091025
This is a 100 samples and you can convert each of the numbers to
binary and the bits will tell you might elements to combine.
On Mon, Apr 6, 2009 at 11:39 AM, jasper slingsby <jslingsby at gmail.com> wrote:
>
> Hello
>
> I apologise for the length of this entry but please bear with me.
>
> In short:
> I need a way of subsampling communities from all possible communities of n
> taxa taken 1:n at a time without having to calculate all possible
> combinations (because this gives me a memory error - using
> combn() or expand.grid() at least). Does anyone know of a function? Or can
> you help me edit the
> combn
> or
> expand.grid
> functions to generate subsamples?
>
> In long:
> I have been creating all possible communities of n taxa taken 1:n at a time
> to get a presence/absence matrix of species occurrence in communities as
> below...
>
> Rows are samples, columns are species:
>
> A B C D . . . .
> 1 0 1 1 1 0 0 0 1 1 1 1 0 0
> 0 0
> 0 1 1 1 1 0 0 0 1 1 1 1 0 0
> 0 0
> 1 1 1 1 1 0 0 0 1 1 1 1 0 0
> 0 0
> 0 0 0 0 0 1 0 0 1 1 1 1 0 0
> 0 0
> 1 0 0 0 0 1 0 0 1 1 1 1 0 0
> 0 0
> 0 1 0 0 0 1 0 0 1 1 1 1 0 0
> 0 0
> 1 1 0 0 0 1 0 0 1 1 1 1 0 0
> 0 0
> 0 0 1 0 0 1 0 0 1 1 1 1 0 0
> 0 0
>
> ...but the number of possible communities increases exponentially with each
> added taxon.
>
> n<-11 #number of taxa
> sum(for (i in 0:n) choose(i, k = 0:i)) #number of combos
>
> So all possible combinations of 11 taxa taken 1:11 at a time is 2048, all
> combos of 12 taken 1:12 is 4096, 13 taken 1:13 = 8192...etc etc such that
> when I reach about 25 taken 1:25 the number of combos is 33554432 and I get
> a memory error.
>
> I have found that the number of combos of x taxa taken from a pool of n
> creates a very kurtotic unimodal distribution,...
>
> x<-vector("integer",20)
> for (i in 1:20) {x[i]<-choose(20,i)}
> plot(x)
>
> ...but have found that limiting the number of samples for any community size
> to 1000 is good enough for the further analyses I wish to do.
> My problem lies in sampling all possible combos without having to calculate
> all possible combos. I have tried two methods but both give memory errors at
> about 25 taxa.
>
> The expand.grid() method:
>
> n <- 11
> toto <- vector("list",n)
> titi <- lapply(toto,function(x) c(0,1))
> tutu <- expand.grid(titi)
>
> The combn() method (a slightly lengthlier function):
>
> samplecommunityD<- function(n,numsamples)
> {
> super<-mat.or.vec(,n)
> for (numspploop in 1:n)
> {
> minor<-t(combn(n,numspploop))
> if (dim(minor)[1]<numsamples)
> {
> minot<-mat.or.vec(dim(minor)[1],n)
> for (loopi in 1:dim(minor)[1])
> {
> for (loopbi in 1:dim(minor)[2])
> {
> minot[loopi,minor[loopi,loopbi]] <- 1
> }
> }
> super<-rbind(super,minot)
> rm(minot)
> }
> else
> {
> minot<-mat.or.vec(numsamples,n)
> for (loopii in 1:numsamples)
> {
> thousand<-sample(dim(minor)[1],numsamples)
> for (loopbii in 1:dim(minor)[2])
> {
> minot[loopii,minor[thousand[loopii],loopbii]] <- 1
> }
> }
> super<-rbind(super,minot)
> rm(minot)
> }
> }
> super<-super[!rowSums(super)>n-1&!rowSums(super)<2,]
> return(super)
> }
>
> samplecommunityD(11,1000)
>
>
> So unless anyone knows of another function I could try my next step would be
> to modify the combn or expand.grid functions to generate subsamples, but
> their coding beyond me at this stage (I'm a 3.5 month newbie). Can anyone
> identify where in the code I would need to introduce a sampling term or
> skipping sequence?
>
> Thanks for your time
> Jasper
>
> --
> View this message in context: http://www.nabble.com/how-to-subsample-all-possible-combinations-of-n-species-taken-1%3An-at-a-time--tp22911399p22911399.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Jim Holtman
Cincinnati, OH
+1 513 646 9390
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