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382 changes: 382 additions & 0 deletions LICENSE

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1 change: 1 addition & 0 deletions README
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21 changes: 21 additions & 0 deletions data/amara_aflp.txt
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r62 r65 r69 r76 r81 r82 r86 r91 r96 r97 r100 r119 r121 r127 r129 r153 r154 r162 r177 r180 r186 r189 r193 r195 r198 r201 r207 r213 r221
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21 changes: 21 additions & 0 deletions data/amara_pop.txt
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pop
pop75681
pop75682
pop75683
pop75684
pop75685
pop75686
pop78681
pop78682
pop78683
pop70461
pop63451
pop63452
pop63453
pop63454
pop63455
pop62491
pop62492
pop62493
pop62494
pop62495
49 changes: 49 additions & 0 deletions data/boot_phylo_nj_pop.r
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## Bootstrap of NJ tree of populations. As input takes bootstraped tree, source data in geneind format and number of permutations.

boot.phylo.nj.pop <- function(origtree, genindata, nperm) {

# Custom variant of function sample to correct its behaviour when number of individuals within respective population is 1.
mysample <- function(x){
if (length(x) == 1) {out <- x}
else {out <- sample(x, replace=TRUE)}
return(out)
}

# Construction of NJ tree from bootstraped dataset for further comparison with original tree.
## Here it is possible to adjust type and/or parameters of distance matrix and/or tree constructioning.
treemaker <- function (ind, listgenpopobj) {
OpravaMatice <- dist.genpop(listgenpopobj[[ind]], method=1, diag=TRUE, upper=TRUE)
OpravaMatice[OpravaMatice == "Inf"] <- 10
nj(OpravaMatice)
}

# Extraction of needed information from orginal data and preparation of temporal variables.
genindata2 <- genindata
data <- genindata@tab
pop <- genindata@pop
indiv <- 1:nrow(genindata@tab)
res <- vector(mode="list", length=nperm)

# In every step (from 1 to number of permutations) sample within respective populations of source data and write it into list of geneind objects.
for (index in 1:nperm) {
struct <- unlist(tapply(indiv, pop, mysample))
new.tab <- data[struct,]
genindata2@tab <- new.tab
# For correct writing of modified data to the list.
res[[index]] <- genindata2
}

# Conversion of all genind objects in the list to genpop objects.
population <- lapply(res, genind2genpop)
# Construction of trees and saving of them into the list of trees.
trees <- vector("list", nperm)
trees <- lapply(1:nperm, treemaker, population)
# Compare every bootstraped tree with original one and return vector of bootstrap support values.
for (i in 1:nperm) storage.mode(trees[[i]]$Nnode) <- "integer"
storage.mode(origtree$Nnode) <- "integer"
pp <- prop.part(trees)
pp <- postprocess.prop.part(pp)
ans <- prop.clades(origtree, part=pp, rooted=FALSE)
return(ans)

}
1,398 changes: 1,398 additions & 0 deletions data/course_commands.html

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1,385 changes: 1,385 additions & 0 deletions data/course_commands.r

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95 changes: 95 additions & 0 deletions data/hauss_stru.in
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48 changes: 48 additions & 0 deletions data/haussknechtii_coordinates.csv
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Ind lon lat
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47 changes: 47 additions & 0 deletions data/haussknechtii_geneland.txt
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48 changes: 48 additions & 0 deletions data/haussknechtii_ssrs.txt
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pop msta93 msta101 msta102 msta103 msta105 msta131 msta143 msta44B msta53 msta61 msta73 msta78
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