Dear Takele, <div><br></div><div>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.</div>
<div><br></div><div>Are you really waiting since one month? You are patient man.</div><div>Ok..nothing else to add</div><div><br></div><div>Cheers</div><div><br></div><div>Valeria</div><br><div class="gmail_quote">On 4 October 2012 22:02, takele taye <span dir="ltr"><<a href="mailto:takele_taye@yahoo.com" target="_blank">takele_taye@yahoo.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><table cellspacing="0" cellpadding="0" border="0"><tbody><tr><td valign="top" style="font:inherit"><font face="arial">Dear</font><div style="font-family:arial;font-size:10pt">
<br></div><div><font face="arial">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</font></div>
<div><font face="arial"><br></font></div><div><font face="arial"><div>library (adegenet)</div><div><br></div><div>library(ade4)</div><div><br></div><div>ttt<- read.structure(file="oh.str", n.ind=94, n.loc=47486, onerowperind=TRUE, col.lab=NULL, col.pop=1, ask=FALSE)</div>
<div>x <- ttt[sample(1:nrow(ttt@tab), )] </div><div>mat.obs <- pairwise.fst(x,
res.type="matrix") </div><div>NBPERM <- 1000 </div><div>mat.perm <- lapply(1:NBPERM, function(i) pairwise.fst(x, pop=sample(pop(x)), res.type="matrix"))</div><div><br></div><div>meanmatobs= mean(c(mat.obs[1,2] < na.omit(sapply(1:NBPERM, function(i) mat.perm[[i]][1,2])), TRUE))</div>
<div><br></div><div><br></div><div>test12 <- as.randtest(na.omit(sapply(1:NBPERM, function(i) mat.perm[[i]][1,2])), mat.obs[1,2], alter="greater")</div><div>test12</div><div>Monte-Carlo test</div><div>Call: as.randtest(sim = na.omit(sapply(1:NBPERM, function(i) mat.perm[[i]][1, </div>
<div> 2])), obs = mat.obs[1, 2], alter = "greater")</div><div><br></div><div><br></div><div>allTests <- list()</div><div> for(i in 1:(nrow(mat.obs)-1)){</div><div> for(j in 2:nrow(mat.obs)){</div><div> 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")</div><div> }</div><div>}</div><div><br></div><div>Any help is appreciated</div></font></div></td></tr></tbody></table><br>_______________________________________________<br>
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