[adegenet-forum] pairwise.fst

[Valeria Montano]

Dear Takele,

I don't know if this can be the issue in your case, but I experimented
problems with the pairwise.fst function as well (and I had forgotten about
that). My desktop tried hard though, I remember the CPU running at its
maximum for quite a while staring at the script and trying to decide what
to do with it... in the end I switched to the dist.genpop function,
although in my case a pairwise fst would have been much more appropriate
than a molecular distance. After shuffling the inds you will get new pops
with different allele frequencies but I am not sure you are interested in
doing it... I am not sure either whether there is an issue with the
pairwise.fst function, maybe it was just me and my desktop (and I) and it
has nothing to do with your case. You can try to run just the pairwise.fst
function on a small dataset and see what happens? Uhm, in the meanwhile the
package hierfstat was back on CRAN so you could do pairwise fst and
bootstrap with it, if I am not wrong.

Are you really waiting since one month? You are patient man.
Ok..nothing else to add

Cheers

Valeria

On 4 October 2012 22:02, takele taye <takele_taye at yahoo.com> wrote:

> Dear
>
> I get difficulties to estimate the significance level for
> pairwise.fst values. It is running on a PC for a month no output yet.
> Moreover, I  couldn't able to run a parallel computation as this does not
> allow to perform this. The input data is the one I used for STRUCTURE
> analysis. My script is
>
> library (adegenet)
>
> library(ade4)
>
> ttt<- read.structure(file="oh.str", n.ind=94, n.loc=47486,
>  onerowperind=TRUE, col.lab=NULL, col.pop=1, ask=FALSE)
> x <- ttt[sample(1:nrow(ttt at tab), )]
> mat.obs <- pairwise.fst(x, res.type="matrix")
> NBPERM <- 1000
> mat.perm <- lapply(1:NBPERM, function(i) pairwise.fst(x,
> pop=sample(pop(x)), res.type="matrix"))
>
> meanmatobs= mean(c(mat.obs[1,2] < na.omit(sapply(1:NBPERM, function(i)
> mat.perm[[i]][1,2])), TRUE))
>
>
> test12 <- as.randtest(na.omit(sapply(1:NBPERM, function(i)
> mat.perm[[i]][1,2])), mat.obs[1,2], alter="greater")
> test12
> Monte-Carlo test
> Call: as.randtest(sim = na.omit(sapply(1:NBPERM, function(i)
> mat.perm[[i]][1,
>     2])), obs = mat.obs[1, 2], alter = "greater")
>
>
> allTests <- list()
>  for(i in 1:(nrow(mat.obs)-1)){
>    for(j in 2:nrow(mat.obs)){
>    allTests[[paste(rownames(mat.obs)[i],rownames(mat.obs)[j],sep="-")]] <-
> as.randtest(na.omit(sapply(1:NBPERM, function(k) mat.perm[[k]][i,j])),
> mat.obs[i,j], alter="greater")
>    }
> }
>
> Any help is appreciated
>
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