Generating Random Trees Testing Cladogram Imbalance

```
##############################################################
# =============================================
# Lab 11: Tree simulation and tree shape in R
# =============================================
# by Nick Matzke (and whoever else adds to this PhyloWiki page)
# Copyright 2011-infinity
# matzkeATberkeley.edu
# March 2011
#
# Please link/cite if you use this, email me if you have
# thoughts/improvements/corrections.
#
##############################################################
#
# Free to use/redistribute under:
# Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the above license, linked here:
#
# http://creativecommons.org/licenses/by-nc-sa/3.0/
#
# Summary:
#
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# you may distribute the resulting work only under the same or
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###################################################################
#
# Assignment for Thursday, March 10
#
# Run the code below, and answer the questions at the end in email.
#
# Please make the subject of your email:
# "IB200B lab11 by [your name]"
# Thanks!
# Nick
#
###################################################################
# setup
library(ape)
# Rabosky's laser package
# does Yule trees, but in table form
#library(laser)
# Hmm, doesn't install correctly
# library(diversitree)
# for birthdeath.tree
library(geiger)
# Remote sourcing:
source("http://ib.berkeley.edu/courses/ib200b/scripts/_genericR_v1.R")
source("http://ib.berkeley.edu/courses/ib200b/scripts/_conus_analyses_v1.R")
source("http://ib.berkeley.edu/courses/ib200b/scripts/_R_tree_functions_v1.R")
# Let's generate some random trees
numtips = 20
# branching times determined by coalescent process (nodes pushed towards tips)
coalescent_tree = rcoal(numtips, br="coalescent")
plot(coalescent_tree)
title("Random coalescent tree")
# branching times under a uniform distribution (non-ultrametric)
runif_tree = rtree(numtips)
plot(runif_tree)
title("Random uniform tree")
# make branch lengths Grafen
rgraf_tree = compute.brlen(runif_tree, method="Grafen")
plot(rgraf_tree)
title("Random tree with Grafen branch lengths")
# Simulate trees under the Yule (pure-birth) process
birthrate = 1
deathrate = 0
yuletree = birthdeath.tree(b=birthrate, d=deathrate, taxa.stop=numtips)
plot(yuletree)
title("Tree simulated under the pure-birth process")
# Simulate trees under the birth-death process
# NOTE: Birth-death trees sometimes die out by accident.
# So, re-run the code several times until you get a tree
# that actually gets up to 20 taxa.
birthrate = 1
deathrate = 0.5
bdtree = birthdeath.tree(b=birthrate, d=deathrate, taxa.stop=numtips)
plot(bdtree)
title("Tree simulated under the birth-death process")
# the bdtree contains extinct ("fossil") lineages, which means it's not
# ultrametric. So we'll do Grafen branch lengths again. NOTE: DON'T
# DO THIS AT HOME, IT IS PRETTY MEANINGLESS EVEN AS A SIMULATION,
# THIS IS JUST A "FOR INSTANCE" TO SHOW HOW THE CODE WORKS
bdtree2 = compute.brlen(bdtree, method="Grafen")
plot(bdtree2)
title("Tree simulated under the birth-death process, and arbitrarily changing\nthe branch lengths using Grafen method to make ultrametric")
# Calculate tree balance for each tree;
# we are going to calculate the number of
# branches on each side of the basal split
bal_ctree = balance(coalescent_tree)
(ctree_RminusL = bal_ctree[1,2] - bal_ctree[1,1])
bal_rtree = balance(runif_tree)
(rtree_RminusL = bal_rtree[1,2] - bal_rtree[1,1])
bal_rgtree = balance(rgraf_tree)
(rgtree_RminusL = bal_rgtree[1,2] - bal_rgtree[1,1])
bal_ytree = balance(yuletree)
(ytree_RminusL = bal_ytree[1,2] - bal_ytree[1,1])
bal_bdtree = balance(bdtree)
(bdtree_RminusL = bal_bdtree[1,2] - bal_bdtree[1,1])
bal_bdtree2 = balance(bdtree2)
(bdtree2_RminusL = as.numeric(bal_bdtree2[1,2] - bal_bdtree2[1,1]) )
# QUESTION: Do the branch lengths matter for this statistic?
# What's the distribution of:
#
# (number of tips on left side of the tree
# ...minus...
# number of tips on the right side of tree)
#
# ...if you do a bunch of simulations?
# set number of simulations
numsims = 100
numtips = 20
# make an empty array
balance_matrix = matrix(NA, nrow=numsims, ncol=4)
for (i in 1:numsims)
{
cat("Simulation #", i, "\n", sep="")
# simulate the trees:
coalescent_tree = rcoal(numtips, br="coalescent")
runif_tree = rtree(numtips)
# Yule tree
birthrate = 1
deathrate = 0
yuletree = birthdeath.tree(b=birthrate, d=deathrate, taxa.stop=numtips)
# simulate a birth-death tree until you get 20 tips
birthrate = 1
deathrate = 0.5
for (j in 1:100)
{
#cat("Trying bdtree #", j, "\n", sep="")
bdtree = birthdeath.tree(b=birthrate, d=deathrate, taxa.stop=numtips)
bdtree_table = prt(bdtree, printflag=FALSE)
#print(sum(round(bdtree_table$time_bp, digits=5) == 0))
numtips_observed = sum(round(bdtree_table$time_bp, digits=5) == 0)
if (numtips_observed >= numtips)
{
rows_to_drop = bdtree_table[bdtree_table$node.type=="tip", ]
labels_to_drop = rows_to_drop[(round(rows_to_drop$time_bp, digits=5) > 0), ]$label
bdtree = drop.tip(bdtree, labels_to_drop)
#bdtree = read.tree(text=fixtree(bdtree))
# Sometime drop.tip messes up the tree, control for this
if (length(bdtree$tip.label) < numtips)
{
foo = 2
} else {
# yay, you have a good tree, break out and continue
break
}
}
}
# Calculate tree balance for each tree;
# we are going to calculate the number of
# branches on each side of the basal split
bal_ctree = balance(coalescent_tree)
(ctree_RminusL = bal_ctree[1,2] - bal_ctree[1,1])
bal_rtree = balance(runif_tree)
(rtree_RminusL = bal_rtree[1,2] - bal_rtree[1,1])
bal_ytree = balance(yuletree)
(ytree_RminusL = bal_ytree[1,2] - bal_ytree[1,1])
bal_bdtree = balance(bdtree)
(bdtree_RminusL = bal_bdtree[1,2] - bal_bdtree[1,1])
# fill in a temporary row
tmprow = c(ctree_RminusL, rtree_RminusL, ytree_RminusL, bdtree_RminusL)
balance_matrix[i, ] = tmprow
}
# convert to a data frame (adf)
balance_matrix = adf(balance_matrix)
# add column names
names(balance_matrix) = c("ctree", "rtree", "ytree", "bdtree")
# plot histograms
# 4 subplots
par(mfrow=c(2,2))
for (i in 1:ncol(balance_matrix))
{
hist(balance_matrix[,i], breaks=numsims/10, main=names(balance_matrix)[i], xlab="#right tips - # left tips", ylab="count")
}
# It will take a few seconds to run 100 simulations for each of the 4 tree processes.
# QUESTION: Do you see any differences in the distributions?
#
# Now, change numsims to 1000 and re-run (this will take a few minutes, smoke 'em
# if you got 'em).
#
# QUESTION: What do the distributions look like now? If your observed data
# gave you a tree with 1 tip on the left, and 19 on the right, would you say
# that this observation has a low p-value and thus was unlikely to be produced
# by your null model?
```

page revision: 9, last edited: 07 Jun 2013 04:06