inst/extdata/script/e_01_run_DEC_model.R
In GabrielNakamura/Herodotools: Tools for Historical Biogeography analysis
#######################################################
# SETUP -- libraries/BioGeoBEARS updates
#######################################################
# Load the package (after installation, see above).
library(GenSA) # GenSA is better than optimx (although somewhat slower)
library(FD) # for FD::maxent() (make sure this is up-to-date)
library(snow) # (if you want to use multicore functionality; some systems/R versions prefer library(parallel), try either)
library(parallel)
library(rexpokit)
library(cladoRcpp)
library(BioGeoBEARS)
library(dplyr)
library(here)
#######################################################
# SETUP: YOUR TREE FILE AND GEOGRAPHY FILE
#######################################################
#######################################################
# Phylogeny file
# Notes:
# 1. Must be binary/bifurcating: no polytomies
# 2. No negative branchlengths (e.g. BEAST MCC consensus trees sometimes have negative branchlengths)
# 3. Be careful of very short branches, as BioGeoBEARS will interpret ultrashort branches as direct ancestors
# 4. You can use non-ultrametric trees, but BioGeoBEARS will interpret any tips significantly below the
# top of the tree as fossils! This is only a good idea if you actually do have fossils in your tree,
# as in e.g. Wood, Matzke et al. (2013), Systematic Biology.
# 5. The default settings of BioGeoBEARS make sense for trees where the branchlengths are in units of
# millions of years, and the tree is 1-1000 units tall. If you have a tree with a total height of
# e.g. 0.00001, you will need to adjust e.g. the max values of d and e, or (simpler) multiply all
# your branchlengths to get them into reasonable units.
# 6. DON'T USE SPACES IN SPECIES NAMES, USE E.G. "_"
#######################################################
phy.path <- here("inst", "extdata", "akodon.new")
trfn = np(phy.path)
#######################################################
# Geography file
# Notes:
# 1. This is a PHYLIP-formatted file. This means that in the
# first line,
# - the 1st number equals the number of rows (species)
# - the 2nd number equals the number of columns (number of areas)
# - after a tab, put the areas in parentheses, with spaces: (A B C D)
#
# 1.5. Example first line:
# 10 4 (A B C D)
#
# 2. The second line, and subsequent lines:
# speciesA 0110
# speciesB 0111
# speciesC 0001
# ...
#
# 2.5a. This means a TAB between the species name and the area 0/1s
# 2.5b. This also means NO SPACE AND NO TAB between the area 0/1s.
#
# 3. See example files at:
# http://phylo.wikidot.com/biogeobears#files
#
# 4. Make you understand what a PLAIN-TEXT EDITOR is:
# http://phylo.wikidot.com/biogeobears#texteditors
#
# 3. The PHYLIP format is the same format used for C++ LAGRANGE geography files.
#
# 4. All names in the geography file must match names in the phylogeny file.
#
# 5. DON'T USE SPACES IN SPECIES NAMES, USE E.G. "_"
#
# 6. Operational taxonomic units (OTUs) should ideally be phylogenetic lineages,
# i.e. genetically isolated populations. These may or may not be identical
# with species. You would NOT want to just use specimens, as each specimen
# automatically can only live in 1 area, which will typically favor DEC+J
# models. This is fine if the species/lineages really do live in single areas,
# but you wouldn't want to assume this without thinking about it at least.
# In summary, you should collapse multiple specimens into species/lineages if
# data indicates they are the same genetic population.
######################################################
geog.path <- here("inst", "extdata" , "geo_area_akodon.data")
# This is the example geography file for Hawaiian Psychotria
# (from Ree & Smith 2008)
geogfn = np(geog.path)
# Look at your geographic range data:
tipranges = getranges_from_LagrangePHYLIP(lgdata_fn=geogfn)
tipranges
# Maximum range size observed:
max(rowSums(dfnums_to_numeric(tipranges@df)))
# Set the maximum number of areas any species may occupy; this cannot be larger
# than the number of areas you set up, but it can be smaller.
max_range_size = 4
numstates_from_numareas(numareas=5,
maxareas=4,
include_null_range=FALSE)
# run and save results of BioGeoBEARS -------------------------------------
trfn = np(phy.path)
geogfn = np(geog.path)
#######################################################
#######################################################
# DEC AND DEC+J ANALYSIS
#######################################################
#######################################################
# NOTE: The BioGeoBEARS "DEC" model is identical with
# the Lagrange DEC model, and should return identical
# ML estimates of parameters, and the same
# log-likelihoods, for the same datasets.
#
# Ancestral state probabilities at nodes will be slightly
# different, since BioGeoBEARS is reporting the
# ancestral state probabilities under the global ML
# model, and Lagrange is reporting ancestral state
# probabilities after re-optimizing the likelihood
# after fixing the state at each node. These will
# be similar, but not identical. See Matzke (2014),
# Systematic Biology, for discussion.
#
# Also see Matzke (2014) for presentation of the
# DEC+J model.
#######################################################
#######################################################
#######################################################
#######################################################
#######################################################
# Run DEC
#######################################################
# Intitialize a default model (DEC model)
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
# Give BioGeoBEARS the location of the phylogeny Newick file
BioGeoBEARS_run_object$trfn = trfn
# Give BioGeoBEARS the location of the geography text file
BioGeoBEARS_run_object$geogfn = geogfn
# Input the maximum range size
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
# 1. Here, un-comment ONLY the files you want to use.
# 2. Also un-comment "BioGeoBEARS_run_object = section_the_tree(...", below.
# 3. For example files see (a) extdata_dir,
# or (b) http://phylo.wikidot.com/biogeobears#files
# and BioGeoBEARS Google Group posts for further hints)
#
# Uncomment files you wish to use in time-stratified analyses:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 2
# (use more cores to speed it up; this requires
# library(parallel) and/or library(snow). The package "parallel"
# is now default on Macs in R 3.0+, but apparently still
# has to be typed on some Windows machines. Note: apparently
# parallel works on Mac command-line R, but not R.app.
# BioGeoBEARS checks for this and resets to 1
# core with R.app)
# Sparse matrix exponentiation is an option for huge numbers of ranges/states (600+)
# I have experimented with sparse matrix exponentiation in EXPOKIT/rexpokit,
# but the results are imprecise and so I haven't explored it further.
# In a Bayesian analysis, it might work OK, but the ML point estimates are
# not identical.
# Also, I have not implemented all functions to work with force_sparse=TRUE.
# Volunteers are welcome to work on it!!
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DEC model
# (nothing to do; defaults)
# Look at the BioGeoBEARS_run_object; it's just a list of settings etc.
BioGeoBEARS_run_object
# This contains the model object
BioGeoBEARS_run_object$BioGeoBEARS_model_object
# This table contains the parameters of the model
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table
# Run this to check inputs. Read the error messages if you get them!
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DEC model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDEC = res
#######################################################
# Run DEC+J
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DEC+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resDEC$outputs@params_table["d","est"]
estart = resDEC$outputs@params_table["e","est"]
jstart = 0.0001
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# Add j as a free parameter
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DECj model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDECj = res
#######################################################
#######################################################
# DIVALIKE AND DIVALIKE+J ANALYSIS
#######################################################
#######################################################
# NOTE: The BioGeoBEARS "DIVALIKE" model is not identical with
# Ronquist (1997)'s parsimony DIVA. It is a likelihood
# interpretation of DIVA, constructed by modelling DIVA's
# processes the way DEC does, but only allowing the
# processes DIVA allows (widespread vicariance: yes; subset
# sympatry: no; see Ronquist & Sanmartin 2011, Figure 4).
#
# DIVALIKE is a likelihood interpretation of parsimony
# DIVA, and it is "like DIVA" -- similar to, but not
# identical to, parsimony DIVA.
#
# I thus now call the model "DIVALIKE", and you should also. ;-)
#######################################################
#######################################################
#######################################################
# Run DIVALIKE
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
#BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DIVALIKE model
# Remove subset-sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "2-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "ysv*1/2"
# Allow classic, widespread vicariance; all events equiprobable
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","init"] = 0.5
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","est"] = 0.5
# No jump dispersal/founder-event speciation
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = 0.01
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = 0.01
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DIVALIKE model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDIVALIKE = res
#######################################################
# Run DIVALIKE+J
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
#BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DIVALIKE+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resDIVALIKE$outputs@params_table["d","est"]
estart = resDIVALIKE$outputs@params_table["e","est"]
jstart = 0.0001
estart <- ifelse(estart < BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","min"],
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","min"],
estart)
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# Remove subset-sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "2-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "ysv*1/2"
# Allow classic, widespread vicariance; all events equiprobable
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","init"] = 0.5
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","est"] = 0.5
# Add jump dispersal/founder-event speciation
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
# Under DIVALIKE+J, the max of "j" should be 2, not 3 (as is default in DEC+J)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","min"] = 0.00001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","max"] = 1.99999
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DIVALIKEj model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDIVALIKEj = res
#######################################################
#######################################################
# BAYAREALIKE AND BAYAREALIKE+J ANALYSIS
#######################################################
#######################################################
# NOTE: As with DIVA, the BioGeoBEARS BayArea-like model is
# not identical with the full Bayesian model implemented
# in the "BayArea" program of Landis et al. (2013).
#
# Instead, this is a simplified likelihood interpretation
# of the model. Basically, in BayArea and BioGeoBEARS-BAYAREALIKE,
# "d" and "e" work like they do in the DEC model of Lagrange
# (and BioGeoBEARS), and then BayArea's cladogenesis assumption
# (which is that nothing in particular happens at cladogenesis) is
# replicated by BioGeoBEARS.
#
# This leaves out 3 important things that are in BayArea:
# 1. Distance dependence (you can add this with a distances
# matrix + the "x" parameter in BioGeoBEARS, however)
# 2. A correction for disallowing "e" events that drive
# a species extinct (a null geographic range)
# 3. The neat Bayesian sampling of histories, which allows
# analyses on large numbers of areas.
#
# The main purpose of having a "BAYAREALIKE" model is
# to test the importance of the cladogenesis model on
# particular datasets. Does it help or hurt the data
# likelihood if there is no special cladogenesis process?
#
# BAYAREALIKE is a likelihood interpretation of BayArea,
# and it is "like BayArea" -- similar to, but not
# identical to, Bayesian BayArea.
# I thus now call the model "BAYAREALIKE", and you should also. ;-)
#######################################################
#######################################################
#######################################################
# Run BAYAREALIKE
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up BAYAREALIKE model
# No subset sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
# No vicariance
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","est"] = 0.0
# No jump dispersal/founder-event speciation
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = 0.01
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = 0.01
# Adjust linkage between parameters
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "1-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/1"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "1-j"
# Only sympatric/range-copying (y) events allowed, and with
# exact copying (both descendants always the same size as the ancestor)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","init"] = 0.9999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","est"] = 0.9999
# Check the inputs
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run BAYAREALIKE model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resBAYAREALIKE = res
#######################################################
# Run BAYAREALIKE+J
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA"
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up BAYAREALIKE+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resBAYAREALIKE$outputs@params_table["d","est"]
estart = resBAYAREALIKE$outputs@params_table["e","est"]
jstart = 0.0001
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# No subset sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
# No vicariance
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","est"] = 0.0
# *DO* allow jump dispersal/founder-event speciation (set the starting value close to 0)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
# Under BAYAREALIKE+J, the max of "j" should be 1, not 3 (as is default in DEC+J) or 2 (as in DIVALIKE+J)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","max"] = 0.99999
# Adjust linkage between parameters
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "1-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/1"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "1-j"
# Only sympatric/range-copying (y) events allowed, and with
# exact copying (both descendants always the same size as the ancestor)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","init"] = 0.9999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","est"] = 0.9999
# NOTE (NJM, 2014-04): BAYAREALIKE+J seems to crash on some computers, usually Windows
# machines. I can't replicate this on my Mac machines, but it is almost certainly
# just some precision under-run issue, when optim/optimx tries some parameter value
# just below zero. The "min" and "max" options on each parameter are supposed to
# prevent this, but apparently optim/optimx sometimes go slightly beyond
# these limits. Anyway, if you get a crash, try raising "min" and lowering "max"
# slightly for each parameter:
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","min"] = 0.0000001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","max"] = 4.9999999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","min"] = 0.0000001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","max"] = 4.9999999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","min"] = 0.00001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","max"] = 0.99999
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run BAYAREALIKE model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resBAYAREALIKEj = res
#########################################################################
#########################################################################
#########################################################################
#########################################################################
#
# CALCULATE SUMMARY STATISTICS TO COMPARE
# DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYAREALIKE, BAYAREALIKE+J
#
#########################################################################
#########################################################################
#########################################################################
#########################################################################
#########################################################################
#########################################################################
# REQUIRED READING:
#
# Practical advice / notes / basic principles on statistical model
# comparison in general, and in BioGeoBEARS:
# http://phylo.wikidot.com/advice-on-statistical-model-comparison-in-biogeobears
#########################################################################
#########################################################################
# Set up empty tables to hold the statistical results
restable = NULL
teststable = NULL
#######################################################
# Statistics -- DEC vs. DEC+J
#######################################################
# We have to extract the log-likelihood differently, depending on the
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resDEC)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resDECj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# DEC, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resDEC, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# DEC+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resDECj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# The null hypothesis for a Likelihood Ratio Test (LRT) is that two models
# confer the same likelihood on the data. See: Brian O'Meara's webpage:
# http://www.brianomeara.info/tutorials/aic
# ...for an intro to LRT, AIC, and AICc
rbind(res2, res1)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#######################################################
# Statistics -- DIVALIKE vs. DIVALIKE+J
#######################################################
# We have to extract the log-likelihood differently, depending on the
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resDIVALIKE)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resDIVALIKEj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# DIVALIKE, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resDIVALIKE, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# DIVALIKE+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resDIVALIKEj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
rbind(res2, res1)
conditional_format_table(stats)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#######################################################
# Statistics -- BAYAREALIKE vs. BAYAREALIKE+J
#######################################################
# We have to extract the log-likelihood differently, depending on the
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resBAYAREALIKE)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resBAYAREALIKEj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# BAYAREALIKE, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resBAYAREALIKE, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# BAYAREALIKE+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resBAYAREALIKEj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
rbind(res2, res1)
conditional_format_table(stats)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#########################################################################
# ASSEMBLE RESULTS TABLES: DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYAREALIKE, BAYAREALIKE+J
#########################################################################
teststable$alt = c("DEC+J", "DIVALIKE+J", "BAYAREALIKE+J")
teststable$null = c("DEC", "DIVALIKE", "BAYAREALIKE")
row.names(restable) = c("DEC", "DEC+J", "DIVALIKE", "DIVALIKE+J", "BAYAREALIKE", "BAYAREALIKE+J")
restable = put_jcol_after_ecol(restable)
########################################################
## Save the results tables for later -- check for e.g.
## convergence issues
########################################################
#
## Loads to "restable"
#save(restable, file="restable_v1.Rdata")
#load(file="restable_v1.Rdata")
#
## Loads to "teststable"
#save(teststable, file="teststable_v1.Rdata")
#load(file="teststable_v1.Rdata")
#
## Also save to text files
#write.table(restable, file="restable.txt", quote=FALSE, sep="\t")
#write.table(unlist_df(teststable), file="teststable.txt", quote=FALSE, sep="\t")
#######################################################
# Model weights of all six models
#######################################################
restable2 = restable
# With AICs:
AICtable = calc_AIC_column(LnL_vals=restable$LnL, nparam_vals=restable$numparams)
restable = cbind(restable, AICtable)
restable_AIC_rellike = AkaikeWeights_on_summary_table(restable=restable, colname_to_use="AIC")
restable_AIC_rellike = put_jcol_after_ecol(restable_AIC_rellike)
restable_AIC_rellike
# With AICcs -- factors in sample size
tr <- read.tree(trfn)
samplesize = length(tr$tip.label)
AICtable = calc_AICc_column(LnL_vals=restable$LnL, nparam_vals=restable$numparams, samplesize=samplesize)
restable2 = cbind(restable2, AICtable)
restable_AICc_rellike = AkaikeWeights_on_summary_table(restable=restable2, colname_to_use="AICc")
restable_AICc_rellike = put_jcol_after_ecol(restable_AICc_rellike)
restable_AICc_rellike
list.results.BioGeoBears <- list(resDEC = resDEC,
resDECj = resDECj,
resDIVALIKE = resDIVALIKE ,
resDIVALIKEj = resDIVALIKEj,
resBAYAREALIKE = resBAYAREALIKE ,
resBAYAREALIKEj = resBAYAREALIKEj,
table_AIC = restable_AIC_rellike ,
table_AICc = restable_AICc_rellike)
list.results.BioGeoBears$table_AICc %>%
dplyr::arrange(AICc) %>%
mutate(delta_AICc = AICc - min(AICc))
save(resDEC, file = here("inst", "extdata", "resDEC_akodon.RData"))
cat("The best models are 'resDEC', 'resBAYAREALIKEj', and 'resBAYAREALIKE'")
GabrielNakamura/Herodotools documentation built on Aug. 27, 2023, 2:33 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#######################################################
# SETUP -- libraries/BioGeoBEARS updates
#######################################################
# Load the package (after installation, see above).
library(GenSA) # GenSA is better than optimx (although somewhat slower)
library(FD) # for FD::maxent() (make sure this is up-to-date)
library(snow) # (if you want to use multicore functionality; some systems/R versions prefer library(parallel), try either)
library(parallel)
library(rexpokit)
library(cladoRcpp)
library(BioGeoBEARS)
library(dplyr)
library(here)
#######################################################
# SETUP: YOUR TREE FILE AND GEOGRAPHY FILE
#######################################################
#######################################################
# Phylogeny file
# Notes:
# 1. Must be binary/bifurcating: no polytomies
# 2. No negative branchlengths (e.g. BEAST MCC consensus trees sometimes have negative branchlengths)
# 3. Be careful of very short branches, as BioGeoBEARS will interpret ultrashort branches as direct ancestors
# 4. You can use non-ultrametric trees, but BioGeoBEARS will interpret any tips significantly below the
# top of the tree as fossils! This is only a good idea if you actually do have fossils in your tree,
# as in e.g. Wood, Matzke et al. (2013), Systematic Biology.
# 5. The default settings of BioGeoBEARS make sense for trees where the branchlengths are in units of
# millions of years, and the tree is 1-1000 units tall. If you have a tree with a total height of
# e.g. 0.00001, you will need to adjust e.g. the max values of d and e, or (simpler) multiply all
# your branchlengths to get them into reasonable units.
# 6. DON'T USE SPACES IN SPECIES NAMES, USE E.G. "_"
#######################################################
phy.path <- here("inst", "extdata", "akodon.new")
trfn = np(phy.path)
#######################################################
# Geography file
# Notes:
# 1. This is a PHYLIP-formatted file. This means that in the
# first line,
# - the 1st number equals the number of rows (species)
# - the 2nd number equals the number of columns (number of areas)
# - after a tab, put the areas in parentheses, with spaces: (A B C D)
#
# 1.5. Example first line:
# 10 4 (A B C D)
#
# 2. The second line, and subsequent lines:
# speciesA 0110
# speciesB 0111
# speciesC 0001
# ...
#
# 2.5a. This means a TAB between the species name and the area 0/1s
# 2.5b. This also means NO SPACE AND NO TAB between the area 0/1s.
#
# 3. See example files at:
# http://phylo.wikidot.com/biogeobears#files
#
# 4. Make you understand what a PLAIN-TEXT EDITOR is:
# http://phylo.wikidot.com/biogeobears#texteditors
#
# 3. The PHYLIP format is the same format used for C++ LAGRANGE geography files.
#
# 4. All names in the geography file must match names in the phylogeny file.
#
# 5. DON'T USE SPACES IN SPECIES NAMES, USE E.G. "_"
#
# 6. Operational taxonomic units (OTUs) should ideally be phylogenetic lineages,
# i.e. genetically isolated populations. These may or may not be identical
# with species. You would NOT want to just use specimens, as each specimen
# automatically can only live in 1 area, which will typically favor DEC+J
# models. This is fine if the species/lineages really do live in single areas,
# but you wouldn't want to assume this without thinking about it at least.
# In summary, you should collapse multiple specimens into species/lineages if
# data indicates they are the same genetic population.
######################################################
geog.path <- here("inst", "extdata" , "geo_area_akodon.data")
# This is the example geography file for Hawaiian Psychotria
# (from Ree & Smith 2008)
geogfn = np(geog.path)
# Look at your geographic range data:
tipranges = getranges_from_LagrangePHYLIP(lgdata_fn=geogfn)
tipranges
# Maximum range size observed:
max(rowSums(dfnums_to_numeric(tipranges@df)))
# Set the maximum number of areas any species may occupy; this cannot be larger
# than the number of areas you set up, but it can be smaller.
max_range_size = 4
numstates_from_numareas(numareas=5,
maxareas=4,
include_null_range=FALSE)
# run and save results of BioGeoBEARS -------------------------------------
trfn = np(phy.path)
geogfn = np(geog.path)
#######################################################
#######################################################
# DEC AND DEC+J ANALYSIS
#######################################################
#######################################################
# NOTE: The BioGeoBEARS "DEC" model is identical with
# the Lagrange DEC model, and should return identical
# ML estimates of parameters, and the same
# log-likelihoods, for the same datasets.
#
# Ancestral state probabilities at nodes will be slightly
# different, since BioGeoBEARS is reporting the
# ancestral state probabilities under the global ML
# model, and Lagrange is reporting ancestral state
# probabilities after re-optimizing the likelihood
# after fixing the state at each node. These will
# be similar, but not identical. See Matzke (2014),
# Systematic Biology, for discussion.
#
# Also see Matzke (2014) for presentation of the
# DEC+J model.
#######################################################
#######################################################
#######################################################
#######################################################
#######################################################
# Run DEC
#######################################################
# Intitialize a default model (DEC model)
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
# Give BioGeoBEARS the location of the phylogeny Newick file
BioGeoBEARS_run_object$trfn = trfn
# Give BioGeoBEARS the location of the geography text file
BioGeoBEARS_run_object$geogfn = geogfn
# Input the maximum range size
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
# 1. Here, un-comment ONLY the files you want to use.
# 2. Also un-comment "BioGeoBEARS_run_object = section_the_tree(...", below.
# 3. For example files see (a) extdata_dir,
# or (b) http://phylo.wikidot.com/biogeobears#files
# and BioGeoBEARS Google Group posts for further hints)
#
# Uncomment files you wish to use in time-stratified analyses:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 2
# (use more cores to speed it up; this requires
# library(parallel) and/or library(snow). The package "parallel"
# is now default on Macs in R 3.0+, but apparently still
# has to be typed on some Windows machines. Note: apparently
# parallel works on Mac command-line R, but not R.app.
# BioGeoBEARS checks for this and resets to 1
# core with R.app)
# Sparse matrix exponentiation is an option for huge numbers of ranges/states (600+)
# I have experimented with sparse matrix exponentiation in EXPOKIT/rexpokit,
# but the results are imprecise and so I haven't explored it further.
# In a Bayesian analysis, it might work OK, but the ML point estimates are
# not identical.
# Also, I have not implemented all functions to work with force_sparse=TRUE.
# Volunteers are welcome to work on it!!
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DEC model
# (nothing to do; defaults)
# Look at the BioGeoBEARS_run_object; it's just a list of settings etc.
BioGeoBEARS_run_object
# This contains the model object
BioGeoBEARS_run_object$BioGeoBEARS_model_object
# This table contains the parameters of the model
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table
# Run this to check inputs. Read the error messages if you get them!
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DEC model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDEC = res
#######################################################
# Run DEC+J
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DEC+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resDEC$outputs@params_table["d","est"]
estart = resDEC$outputs@params_table["e","est"]
jstart = 0.0001
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# Add j as a free parameter
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DECj model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDECj = res
#######################################################
#######################################################
# DIVALIKE AND DIVALIKE+J ANALYSIS
#######################################################
#######################################################
# NOTE: The BioGeoBEARS "DIVALIKE" model is not identical with
# Ronquist (1997)'s parsimony DIVA. It is a likelihood
# interpretation of DIVA, constructed by modelling DIVA's
# processes the way DEC does, but only allowing the
# processes DIVA allows (widespread vicariance: yes; subset
# sympatry: no; see Ronquist & Sanmartin 2011, Figure 4).
#
# DIVALIKE is a likelihood interpretation of parsimony
# DIVA, and it is "like DIVA" -- similar to, but not
# identical to, parsimony DIVA.
#
# I thus now call the model "DIVALIKE", and you should also. ;-)
#######################################################
#######################################################
#######################################################
# Run DIVALIKE
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
#BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DIVALIKE model
# Remove subset-sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "2-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "ysv*1/2"
# Allow classic, widespread vicariance; all events equiprobable
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","init"] = 0.5
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","est"] = 0.5
# No jump dispersal/founder-event speciation
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = 0.01
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = 0.01
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DIVALIKE model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDIVALIKE = res
#######################################################
# Run DIVALIKE+J
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
#BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up DIVALIKE+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resDIVALIKE$outputs@params_table["d","est"]
estart = resDIVALIKE$outputs@params_table["e","est"]
jstart = 0.0001
estart <- ifelse(estart < BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","min"],
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","min"],
estart)
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# Remove subset-sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "2-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "ysv*1/2"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "ysv*1/2"
# Allow classic, widespread vicariance; all events equiprobable
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","init"] = 0.5
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01v","est"] = 0.5
# Add jump dispersal/founder-event speciation
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
# Under DIVALIKE+J, the max of "j" should be 2, not 3 (as is default in DEC+J)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","min"] = 0.00001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","max"] = 1.99999
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run DIVALIKEj model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resDIVALIKEj = res
#######################################################
#######################################################
# BAYAREALIKE AND BAYAREALIKE+J ANALYSIS
#######################################################
#######################################################
# NOTE: As with DIVA, the BioGeoBEARS BayArea-like model is
# not identical with the full Bayesian model implemented
# in the "BayArea" program of Landis et al. (2013).
#
# Instead, this is a simplified likelihood interpretation
# of the model. Basically, in BayArea and BioGeoBEARS-BAYAREALIKE,
# "d" and "e" work like they do in the DEC model of Lagrange
# (and BioGeoBEARS), and then BayArea's cladogenesis assumption
# (which is that nothing in particular happens at cladogenesis) is
# replicated by BioGeoBEARS.
#
# This leaves out 3 important things that are in BayArea:
# 1. Distance dependence (you can add this with a distances
# matrix + the "x" parameter in BioGeoBEARS, however)
# 2. A correction for disallowing "e" events that drive
# a species extinct (a null geographic range)
# 3. The neat Bayesian sampling of histories, which allows
# analyses on large numbers of areas.
#
# The main purpose of having a "BAYAREALIKE" model is
# to test the importance of the cladogenesis model on
# particular datasets. Does it help or hurt the data
# likelihood if there is no special cladogenesis process?
#
# BAYAREALIKE is a likelihood interpretation of BayArea,
# and it is "like BayArea" -- similar to, but not
# identical to, Bayesian BayArea.
# I thus now call the model "BAYAREALIKE", and you should also. ;-)
#######################################################
#######################################################
#######################################################
# Run BAYAREALIKE
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA" # if FALSE, use optim() instead of optimx()
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up BAYAREALIKE model
# No subset sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
# No vicariance
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","est"] = 0.0
# No jump dispersal/founder-event speciation
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = 0.01
# BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = 0.01
# Adjust linkage between parameters
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "1-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/1"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "1-j"
# Only sympatric/range-copying (y) events allowed, and with
# exact copying (both descendants always the same size as the ancestor)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","init"] = 0.9999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","est"] = 0.9999
# Check the inputs
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run BAYAREALIKE model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resBAYAREALIKE = res
#######################################################
# Run BAYAREALIKE+J
#######################################################
BioGeoBEARS_run_object = define_BioGeoBEARS_run()
BioGeoBEARS_run_object$trfn = trfn
BioGeoBEARS_run_object$geogfn = geogfn
BioGeoBEARS_run_object$max_range_size = max_range_size
BioGeoBEARS_run_object$min_branchlength = 0.000001 # Min to treat tip as a direct ancestor (no speciation event)
BioGeoBEARS_run_object$include_null_range = TRUE # set to FALSE for e.g. DEC* model, DEC*+J, etc.
# (For DEC* and other "*" models, please cite: Massana, Kathryn A.; Beaulieu,
# Jeremy M.; Matzke, Nicholas J.; O’Meara, Brian C. (2015). Non-null Effects of
# the Null Range in Biogeographic Models: Exploring Parameter Estimation in the
# DEC Model. bioRxiv, http://biorxiv.org/content/early/2015/09/16/026914 )
# Also: search script on "include_null_range" for other places to change
# Set up a time-stratified analysis:
#BioGeoBEARS_run_object$timesfn = "data/BioGeoBEARS/timeperiods.txt"
#BioGeoBEARS_run_object$dispersal_multipliers_fn = "manual_dispersal_multipliers.txt"
#BioGeoBEARS_run_object$areas_allowed_fn = "data/BioGeoBEARS/areas_allowed.txt"
#BioGeoBEARS_run_object$areas_adjacency_fn = "areas_adjacency.txt"
#BioGeoBEARS_run_object$distsfn = "distances_matrix.txt"
# See notes on the distances model on PhyloWiki's BioGeoBEARS updates page.
# Speed options and multicore processing if desired
BioGeoBEARS_run_object$on_NaN_error = -1e50 # returns very low lnL if parameters produce NaN error (underflow check)
BioGeoBEARS_run_object$speedup = TRUE # shorcuts to speed ML search; use FALSE if worried (e.g. >3 params)
BioGeoBEARS_run_object$use_optimx = "GenSA"
BioGeoBEARS_run_object$num_cores_to_use = 3
BioGeoBEARS_run_object$force_sparse = FALSE # force_sparse=TRUE causes pathology & isn't much faster at this scale
# This function loads the dispersal multiplier matrix etc. from the text files into the model object. Required for these to work!
# (It also runs some checks on these inputs for certain errors.)
BioGeoBEARS_run_object = readfiles_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Divide the tree up by timeperiods/strata (uncomment this for stratified analysis)
# BioGeoBEARS_run_object = section_the_tree(inputs=BioGeoBEARS_run_object, make_master_table=TRUE, plot_pieces=FALSE)
# The stratified tree is described in this table:
#BioGeoBEARS_run_object$master_table
# Good default settings to get ancestral states
BioGeoBEARS_run_object$return_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_TTL_loglike_from_condlikes_table = TRUE
BioGeoBEARS_run_object$calc_ancprobs = TRUE # get ancestral states from optim run
# Set up BAYAREALIKE+J model
# Get the ML parameter values from the 2-parameter nested model
# (this will ensure that the 3-parameter model always does at least as good)
dstart = resBAYAREALIKE$outputs@params_table["d","est"]
estart = resBAYAREALIKE$outputs@params_table["e","est"]
jstart = 0.0001
# Input starting values for d, e
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","init"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","est"] = dstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","init"] = estart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","est"] = estart
# No subset sympatry
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["s","est"] = 0.0
# No vicariance
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","init"] = 0.0
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["v","est"] = 0.0
# *DO* allow jump dispersal/founder-event speciation (set the starting value close to 0)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","type"] = "free"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","init"] = jstart
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","est"] = jstart
# Under BAYAREALIKE+J, the max of "j" should be 1, not 3 (as is default in DEC+J) or 2 (as in DIVALIKE+J)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","max"] = 0.99999
# Adjust linkage between parameters
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ysv","type"] = "1-j"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["ys","type"] = "ysv*1/1"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["y","type"] = "1-j"
# Only sympatric/range-copying (y) events allowed, and with
# exact copying (both descendants always the same size as the ancestor)
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","type"] = "fixed"
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","init"] = 0.9999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["mx01y","est"] = 0.9999
# NOTE (NJM, 2014-04): BAYAREALIKE+J seems to crash on some computers, usually Windows
# machines. I can't replicate this on my Mac machines, but it is almost certainly
# just some precision under-run issue, when optim/optimx tries some parameter value
# just below zero. The "min" and "max" options on each parameter are supposed to
# prevent this, but apparently optim/optimx sometimes go slightly beyond
# these limits. Anyway, if you get a crash, try raising "min" and lowering "max"
# slightly for each parameter:
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","min"] = 0.0000001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["d","max"] = 4.9999999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","min"] = 0.0000001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["e","max"] = 4.9999999
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","min"] = 0.00001
BioGeoBEARS_run_object$BioGeoBEARS_model_object@params_table["j","max"] = 0.99999
check_BioGeoBEARS_run(BioGeoBEARS_run_object)
# Run BAYAREALIKE model and save results
res = bears_optim_run(BioGeoBEARS_run_object)
resBAYAREALIKEj = res
#########################################################################
#########################################################################
#########################################################################
#########################################################################
#
# CALCULATE SUMMARY STATISTICS TO COMPARE
# DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYAREALIKE, BAYAREALIKE+J
#
#########################################################################
#########################################################################
#########################################################################
#########################################################################
#########################################################################
#########################################################################
# REQUIRED READING:
#
# Practical advice / notes / basic principles on statistical model
# comparison in general, and in BioGeoBEARS:
# http://phylo.wikidot.com/advice-on-statistical-model-comparison-in-biogeobears
#########################################################################
#########################################################################
# Set up empty tables to hold the statistical results
restable = NULL
teststable = NULL
#######################################################
# Statistics -- DEC vs. DEC+J
#######################################################
# We have to extract the log-likelihood differently, depending on the
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resDEC)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resDECj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# DEC, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resDEC, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# DEC+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resDECj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# The null hypothesis for a Likelihood Ratio Test (LRT) is that two models
# confer the same likelihood on the data. See: Brian O'Meara's webpage:
# http://www.brianomeara.info/tutorials/aic
# ...for an intro to LRT, AIC, and AICc
rbind(res2, res1)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#######################################################
# Statistics -- DIVALIKE vs. DIVALIKE+J
#######################################################
# We have to extract the log-likelihood differently, depending on the
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resDIVALIKE)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resDIVALIKEj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# DIVALIKE, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resDIVALIKE, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# DIVALIKE+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resDIVALIKEj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
rbind(res2, res1)
conditional_format_table(stats)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#######################################################
# Statistics -- BAYAREALIKE vs. BAYAREALIKE+J
#######################################################
# We have to extract the log-likelihood differently, depending on the
# version of optim/optimx
LnL_2 = get_LnL_from_BioGeoBEARS_results_object(resBAYAREALIKE)
LnL_1 = get_LnL_from_BioGeoBEARS_results_object(resBAYAREALIKEj)
numparams1 = 3
numparams2 = 2
stats = AICstats_2models(LnL_1, LnL_2, numparams1, numparams2)
stats
# BAYAREALIKE, null model for Likelihood Ratio Test (LRT)
res2 = extract_params_from_BioGeoBEARS_results_object(results_object=resBAYAREALIKE, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
# BAYAREALIKE+J, alternative model for Likelihood Ratio Test (LRT)
res1 = extract_params_from_BioGeoBEARS_results_object(results_object=resBAYAREALIKEj, returnwhat="table", addl_params=c("j"), paramsstr_digits=4)
rbind(res2, res1)
conditional_format_table(stats)
tmp_tests = conditional_format_table(stats)
restable = rbind(restable, res2, res1)
teststable = rbind(teststable, tmp_tests)
#########################################################################
# ASSEMBLE RESULTS TABLES: DEC, DEC+J, DIVALIKE, DIVALIKE+J, BAYAREALIKE, BAYAREALIKE+J
#########################################################################
teststable$alt = c("DEC+J", "DIVALIKE+J", "BAYAREALIKE+J")
teststable$null = c("DEC", "DIVALIKE", "BAYAREALIKE")
row.names(restable) = c("DEC", "DEC+J", "DIVALIKE", "DIVALIKE+J", "BAYAREALIKE", "BAYAREALIKE+J")
restable = put_jcol_after_ecol(restable)
########################################################
## Save the results tables for later -- check for e.g.
## convergence issues
########################################################
#
## Loads to "restable"
#save(restable, file="restable_v1.Rdata")
#load(file="restable_v1.Rdata")
#
## Loads to "teststable"
#save(teststable, file="teststable_v1.Rdata")
#load(file="teststable_v1.Rdata")
#
## Also save to text files
#write.table(restable, file="restable.txt", quote=FALSE, sep="\t")
#write.table(unlist_df(teststable), file="teststable.txt", quote=FALSE, sep="\t")
#######################################################
# Model weights of all six models
#######################################################
restable2 = restable
# With AICs:
AICtable = calc_AIC_column(LnL_vals=restable$LnL, nparam_vals=restable$numparams)
restable = cbind(restable, AICtable)
restable_AIC_rellike = AkaikeWeights_on_summary_table(restable=restable, colname_to_use="AIC")
restable_AIC_rellike = put_jcol_after_ecol(restable_AIC_rellike)
restable_AIC_rellike
# With AICcs -- factors in sample size
tr <- read.tree(trfn)
samplesize = length(tr$tip.label)
AICtable = calc_AICc_column(LnL_vals=restable$LnL, nparam_vals=restable$numparams, samplesize=samplesize)
restable2 = cbind(restable2, AICtable)
restable_AICc_rellike = AkaikeWeights_on_summary_table(restable=restable2, colname_to_use="AICc")
restable_AICc_rellike = put_jcol_after_ecol(restable_AICc_rellike)
restable_AICc_rellike
list.results.BioGeoBears <- list(resDEC = resDEC,
resDECj = resDECj,
resDIVALIKE = resDIVALIKE ,
resDIVALIKEj = resDIVALIKEj,
resBAYAREALIKE = resBAYAREALIKE ,
resBAYAREALIKEj = resBAYAREALIKEj,
table_AIC = restable_AIC_rellike ,
table_AICc = restable_AICc_rellike)
list.results.BioGeoBears$table_AICc %>%
dplyr::arrange(AICc) %>%
mutate(delta_AICc = AICc - min(AICc))
save(resDEC, file = here("inst", "extdata", "resDEC_akodon.RData"))
cat("The best models are 'resDEC', 'resBAYAREALIKEj', and 'resBAYAREALIKE'")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.