Make trees in R, test its stability by bootstrapping

From Augix' Wiki

• my boot.tree.R

boot.tree <- function(data, B = 100, tree = "upgma") {
	library(phangorn)
	func <- function(x) upgma(dist(x, method = "euclidean"), method="average")
	if (tree == "nj") {
		func <- function(x) nj(dist(x, method = "euclidean"))
	}
	if (tree == "hclust") {
		func <- function(x) {
			tr = hclust(dist(x, method = "euclidean"))
			tr = as.phylo(tr)
			return(tr)
		}
	}
	tr_real = func(data)
	plot(tr_real)
	library(ape)
	bp <- boot.phylo(tr_real, data, FUN=func, B=B)
	nodelabels(bp)
	return(bp)
}
 
data = t(USArrests) # columns are the branches
boot.tree(data, B=1000, tree='hclust')
 
 
# Description:
#	This function builds a upgma or nj tree and tests its stability by bootstrapping. It returns a tree, and bootstrap result.
#
# Usage:
#	boot.tree(data, B = 100, tree = "upgma")
#
# Arguments:
#	data: a numeric matrix, data fram
#	
#	B: the number of bootstrap replicates. (100 by default).
#
#	tree: tree type. It could be "upgma", or "nj". ("upgma" by default.)
#
# Values:
#	It return a numeric vector which _i_th element is the times that we observe the _i_th node of the real tree.
#
# Examples:
#	# compare it with the hclust
#	par(ask = TRUE)
#	plot(hclust(dist(t(USJudgeRatings))))
#	boot.tree(t(USJudgeRatings), 100)

File:Boot tree.png

• a script from ape package

install.packages("ape")
library(ape)
data(woodmouse)
d <- dist.dna(woodmouse)
tr <- nj(d)
bp <- boot.phylo(tr, woodmouse, function(xx) nj(dist.dna(xx)))
plot(tr)
nodelabels(bp)