R version 2.6.2 (2008-02-08)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> library(genealogicalSorting)
Loading required package: ape

> # begin working with cricket data
> cyt.tree <- read.tree("cyt-c.tree")
> cyt.assignments <- readAssignmentFile("cyt-c.groups",cyt.tree)
> gsi(cyt.tree,"firmus",cyt.assignments)
[1] 0.84
> gsi(cyt.tree,"pennsylvanicus",cyt.assignments)
[1] 0.8333333
> gsi(cyt.tree,"ovisopis",cyt.assignments)
[1] 1
> gsi(cyt.tree,"veletis",cyt.assignments)
[1] 1
> results <- singleTreeAnalysis(cyt.tree,"cyt-c.groups",10000,10)
Loading required package: snow
Loading required package: Rmpi
        10 slaves are spawned successfully. 0 failed.
[1] "Doing 1000 reps on each client"
$groups
[1] "pennsylvanicus" "firmus"         "ovisopis"      "veletis"

$gsi
[1] 0.8333333 0.8400000 1.0000000 1.0000000

$pvals
[1] 0.00009999 0.00009999 0.00719928 0.00809919

$nperms
[1] 10000

> # begin working with dolphin data -- over 800 trees in four separate files
> act_info <- readWeightedNexus("act_bs.trees")
> act_trees <- act_info$trees
> act_weights <- act_info$weights
> for(i in 1:length(act_trees)) {
    # root each tree at a particular outgroup
    act_trees[[i]] <- root(act_trees[[i]],"LAC9300")
    # remove the outgroup from each tree
    act_trees[[i]] <- drop.tip(act_trees[[i]],"LAC9300")
  }
> # here, one could perform an analysis on this particular set of trees like so...
act_results <- multiTreeAnalysis(act_trees,dolphin_mapping.txt,10000,10,act_weights)
> but_info <- readWeightedNexus("but_bs.trees")
> but_trees <- but_info$trees
> but_weights <- but_info$weights
> for(i in 1:length(but_trees)) {
    but_trees[[i]] <- root(but_trees[[i]],"LAC9300")
    but_trees[[i]] <- drop.tip(but_trees[[i]],"LAC9300")
  }
> cam_info <- readWeightedNexus("cam_bs.trees")
> cam_trees <- cam_info$trees
> cam_weights <- cam_info$weights
> for(i in 1:length(cam_trees)) {
    cam_trees[[i]] <- root(cam_trees[[i]],"LAC9300")
    cam_trees[[i]] <- drop.tip(cam_trees[[i]],"LAC9300")
  }
> hex2_info <- readWeightedNexus("hex2_bs.trees")
> hex2_trees <- hex2_info$trees
> hex2_weights <- hex2_info$weights
> for(i in 1:length(hex2_trees)) {
    hex2_trees[[i]] <- root(hex2_trees[[i]],"LAC9300")
    # remove both outgroups from this set of trees
    hex2_trees[[i]] <- drop.tip(hex2_trees[[i]],"LAC9300")
    hex2_trees[[i]] <- drop.tip(hex2_trees[[i]],"LAC9300b")
  }
> # concatenate the four sets of trees together
> all_trees <- act_trees
> for(i in 1:length(but_trees)) {
    all_trees[[1 + length(all_trees)]] <- but_trees[[i]]
  }
> for(i in 1:length(cam_trees)) {
    all_trees[[1 + length(all_trees)]] <- cam_trees[[i]]
  }
> for(i in 1:length(hex2_trees)) {
    all_trees[[1 + length(all_trees)]] <- hex2_trees[[i]]
  }
> # concatenate the four sets of weights together
> all_weights <- array()
> for(i in 1:length(act_weights)) {
    all_weights[i] <- act_weights[i]
  }
> for(i in 1:length(but_weights)) {
    all_weights[1 + length(all_weights)] <- but_weights[i]
  }
> for(i in 1:length(cam_weights)) {
    all_weights[1 + length(all_weights)] <- cam_weights[i]
  }
> for(i in 1:length(hex2_weights)) {
    all_weights[1 + length(all_weights)] <- hex2_weights[i]
  }
> # now we will perform an analysis on the combined set of trees...
> all_results <- multiTreeAnalysis(all_trees,"dolphin_mapping.txt",10000,10,all_weights)
        10 slaves are spawned successfully. 0 failed.
[1] "Doing 1000 reps on each client"
"treenum" "group" "gsi" "pval"
  1 "lob" 1.0000000 0.00239976
  1 "los" 0.8000000 0.00239976
  2 "lob" 1.0000000 0.00069993
  2 "los" 0.8000000 0.00079992
  3 "lob" 0.8750000 0.00219978
  3 "los" 0.3333333 0.98050195
  4 "lob" 1.0000000 0.00069993
  4 "los" 0.8000000 0.00079992
  5 "lob" 0.7000000 0.03289671
  5 "los" 0.3333333 0.98050195
  6 "lob" 1.0000000 0.00239976
  6 "los" 0.8000000 0.00239976
  7 "lob" 0.7777778 0.01739826
  7 "los" 0.3333333 0.98050195
  8 "lob" 1.0000000 0.00069993
  8 "los" 0.8000000 0.00079992
  9 "lob" 0.5833333 0.19728027
  9 "los" 0.3333333 0.98050195
 10 "lob" 0.5833333 0.18288171
 10 "los" 0.3333333 0.97180282
 11 "lob" 0.5833333 0.19728027
 11 "los" 0.3636364 0.89321068
 12 "lob" 0.8750000 0.00219978
 12 "los" 0.3333333 0.97510249
[output truncated]

"group" "egsi" "epval"
"lob" 0.6094504 0.00169983
"los" 0.6150855 0.00009999