From 9c8eadcfbb4f8d13bb437c3c2d8583ec31bb3955 Mon Sep 17 00:00:00 2001
From: ejd99 <85487082+ejd99@users.noreply.github.com>
Date: Wed, 16 Oct 2024 09:05:24 +0100
Subject: [PATCH] add inference (#79)
* Add inference and testing
* remove test/
* add phylolm
* add inference example
* hopefully will now pass tests
* include global skipR
* added estimaterates!
* Updated estimaterates, all info is on the tree
* add lambda calcs
* Fix inference testing
* examples folder needs manifest
* Only compare to R if packages are installed
* Update workflows
* Minor fixes to type stability
* Switch from Dict to a struct for trait storage
* fix problems with estimaterates and clean examples
* mult lambda
* manifest and project
* Fix authorship list
* multi lambda
* fix tests
* add orcid
* export traitdata
* create distribution for bayesian analysis
* example baysian analysis
* Change Float to Number, add bayes method for signal
* example for Bayes method to calculate signal
* include chain and plottinng
* change to d.tree
* Fixing node data types to allow Duals in a small union
* Whitespace and name fixes
* Update manifest to Julia 1.10
* Update Bayesian inference testing
* Whitespace fixes
* Whitespace fixes
* Bugfix in threepoint!() lambda calculation and speed up
* Clean up code for Bayesian example
* Removed redundant upper calculation
* Clean demo code
* Claire Analysis
* Fix case issue
* Inference fixes
* Fix for dropping no tips, metadata fix
* Allow retention of internal nodes with only one outbound connection after droptips!().
* keep_tips!() can have keep argument
* Report on whether R ran
---
.github/workflows/testing.yaml | 1 +
.zenodo.json | 6 +
Bayes/Manifest.toml | 2264 ++++++++++++++++++++++++
Bayes/Project.toml | 9 +
Bayes/testinf.jl | 292 ++++
LICENSE | 2 +-
NEWS.md | 4 +
Project.toml | 24 +-
codemeta.json | 19 +-
examples/Manifest.toml | 2989 ++++++++++++++++++++++++++++++++
examples/Myrtaceae_bayes.jl | 138 ++
examples/Project.toml | 15 +
examples/example_bayes.jl | 165 ++
examples/example_inference.jl | 428 +++++
examples/testmulti.jl | 156 ++
ext/PhyloRCallExt.jl | 2 +-
src/API.jl | 2 +-
src/Phylo.jl | 4 +
src/RecursiveTree.jl | 2 +-
src/inference.jl | 759 ++++++++
src/newick.jl | 2 +-
src/trim.jl | 81 +-
test/run_inference.jl | 50 +
test/run_rcall.jl | 6 +-
test/test_RecursiveTree.jl | 8 +-
test/test_inference.jl | 26 +
test/test_trim.jl | 8 +
27 files changed, 7412 insertions(+), 50 deletions(-)
create mode 100644 Bayes/Manifest.toml
create mode 100644 Bayes/Project.toml
create mode 100644 Bayes/testinf.jl
create mode 100644 examples/Manifest.toml
create mode 100644 examples/Myrtaceae_bayes.jl
create mode 100644 examples/Project.toml
create mode 100644 examples/example_bayes.jl
create mode 100644 examples/example_inference.jl
create mode 100644 examples/testmulti.jl
create mode 100644 src/inference.jl
create mode 100644 test/run_inference.jl
create mode 100644 test/test_inference.jl
diff --git a/.github/workflows/testing.yaml b/.github/workflows/testing.yaml
index c25474ab..0d75e819 100644
--- a/.github/workflows/testing.yaml
+++ b/.github/workflows/testing.yaml
@@ -69,6 +69,7 @@ jobs:
if: ${{ matrix.r-testing }}
run: |
install.packages("ape", repos="http://cran.r-project.org")
+ install.packages("phylolm", repos="http://cran.r-project.org")
shell: R --vanilla --file={0}
- name: Cache
uses: julia-actions/cache@v2
diff --git a/.zenodo.json b/.zenodo.json
index 2de32a8e..992f504b 100644
--- a/.zenodo.json
+++ b/.zenodo.json
@@ -9,6 +9,12 @@
"affiliation": "University of Glasgow",
"ror": "00vtgdb53"
},
+ {
+ "name": "Davison, Emily",
+ "orcid": "0009-0000-3656-8309",
+ "affiliation": "University of Glasgow",
+ "ror": "00vtgdb53"
+ },
{
"name": "Borregaard, Michael",
"orcid": "0000-0002-8146-8435",
diff --git a/Bayes/Manifest.toml b/Bayes/Manifest.toml
new file mode 100644
index 00000000..003909c1
--- /dev/null
+++ b/Bayes/Manifest.toml
@@ -0,0 +1,2264 @@
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diff --git a/Bayes/Project.toml b/Bayes/Project.toml
new file mode 100644
index 00000000..91ac9cc4
--- /dev/null
+++ b/Bayes/Project.toml
@@ -0,0 +1,9 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Phylo = "aea672f4-3940-5932-aa44-993d1c3ff149"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
diff --git a/Bayes/testinf.jl b/Bayes/testinf.jl
new file mode 100644
index 00000000..040213d2
--- /dev/null
+++ b/Bayes/testinf.jl
@@ -0,0 +1,292 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+using Turing
+using Phylo
+using Distributions
+using Random
+using BenchmarkTools
+using DataFrames
+using LinearAlgebra
+using Plots
+
+# set seed
+Random.seed!(678)
+
+# generate a random tree
+# number of tips on the tree
+n_tips = 100
+
+# generate tree
+nu = Ultrametric{TraitTree{1}}(n_tips);
+tree = rand(nu);
+distances(tree)
+
+# get phylogentic variance matrix - needed for method using built in Julia functions
+C = fill(1.0, (n_tips, n_tips)) - distances(tree) ./ 2
+C = abs.(Symmetric(C))
+
+# generate traits, store in vector z
+a = BrownianTrait(tree, "BMtrait", σ² = 1.0);
+bm_traits = rand(a)
+
+leafnames = getleafnames(tree);
+z = Vector{Float64}();
+
+for leaf in leafnames
+ push!(z, bm_traits[leaf])
+end
+
+# using built in Julia functions
+@model function phyloinf(z, C)
+ beta ~ Uniform(-100, 100)
+ sigma ~ Uniform(0, 100)
+
+ return z ~ MvNormal(beta * ones(length(z)), sigma * C)
+end
+
+c = sample(phyloinf(z, C), HMC(0.1, 5), 100000)
+
+plot(c[:beta])
+plot(c[:sigma])
+
+# Use threepoint:
+# pop trait data on tree
+nodes = getnodes(tree, postorder)
+rdat = DataFrame(species = leafnames, data = z)
+for i in eachrow(rdat)
+ setnodedata!(tree, i.species, Phylo.traitdata(["trait"], [i.data]))
+end
+
+# trait needs to be a vector of trait names, used for functions later
+trait = ["trait"]
+
+# add lengths to tree
+for node in nodes
+ val = getnodedata(tree, node).value
+ if hasinbound(tree, node)
+ len = Phylo.getlength(tree, Phylo.getinbound(tree, node))
+ td = traitdata(trait, val, len)
+ setnodedata!(tree, node, td)
+ else
+ td = traitdata(trait, val)
+ setnodedata!(tree, node, td)
+ end
+end
+
+# Need to create own distribution to use threepoint to calculate likelihood
+# needs to be a mutable struct as threepoint changes tree
+struct MyDist2{T <: AbstractTree, N <: Number} <:
+ ContinuousMultivariateDistribution
+ sigma::N
+ beta::N
+ tree::T
+end
+
+# rand creates a vector of tip trait values dependent on the tree, sigma (rate of evolution) and beta (root trait value)
+function Distributions.rand(rng::AbstractRNG, d::MyDist2)
+ a = BrownianTrait(d.tree, "BMtrait", σ² = d.sigma)
+ bm_traits = rand(a)
+
+ leafnames = getleafnames(d.tree, postorder)
+ z = Vector{Float64}()
+
+ for leaf in leafnames
+ push!(z, bm_traits[leaf])
+ end
+ return z
+end
+
+# define loglikelihood function, n is number of leaves, nd is node data from the root node, sigma is the rate of evolution, beta is the trait data for the root node
+function loglik(n, nd, sigma, beta)
+ return -(1.0 / 2.0) * (n * log(2π) + nd.logV + n * log(abs(sigma)) +
+ abs(sigma)^(-1) * (nd.yy[] - 2 * nd.Q[] * beta + nd.xx * beta^2))
+end
+
+# define logpdf for my dist
+function Distributions.logpdf(d::MyDist2, z::Vector{Float64})
+ # add errors for if tree doesnt have right data
+
+ n = nleaves(d.tree)
+ nodes = getnodes(d.tree, postorder)
+ trait = getnodedata(d.tree, nodes[1]).name
+
+ threepoint!(d.tree, trait, nodes)
+
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ return loglik(n, nd, d.sigma, d.beta)
+end
+
+@model function phyloinftree(tree, z) # z needs to be for leaves in postorder
+ beta ~ Uniform(-100, 100)
+ sigma ~ Uniform(0, 100)
+
+ return z ~ MyDist2(sigma, beta, tree) # tree.z ~ (impliment later)
+end
+
+c2 = sample(phyloinftree(tree, z), HMC(0.01, 5), 100000)
+plot(c2[:beta])
+plot(c2[:sigma])
+
+# define loglikelihood function, used in Bayes methods
+function loglik(n, nd, sigma, beta)
+ return -(1.0 / 2.0) * (n * log(2π) + nd.logV + n * log(abs(sigma)) +
+ abs(sigma)^(-1) * (nd.yy[] - 2 * nd.Q[] * beta + nd.xx * beta^2))
+end
+
+# Need to create own distribution to use threepoint to calculate likelihood
+# needs to be a mutable struct as threepoint changes tree
+# also needs renamed
+struct MyDist3{T <: AbstractTree, N <: Number} <:
+ ContinuousMultivariateDistribution
+ sigma::N
+ beta::N
+ lambda::N
+ tree::T
+end
+
+# rand creates a vector of tip trait values dependent on the tree, sigma (rate of evolution) and beta (root trait value)
+function Distributions.rand(rng::AbstractRNG, d::MyDist3) # incorrect but can fix later
+ a = BrownianTrait(d.tree, "BMtrait", σ² = d.sigma)
+ bm_traits = rand(a)
+
+ leafnames = getleafnames(d.tree, postorder)
+ z = Vector{Float64}()
+
+ for leaf in leafnames
+ push!(z, bm_traits[leaf])
+ end
+ return z
+end
+
+# define logpdf for my dist
+function Distributions.logpdf(d::MyDist3, z::Vector{Float64})
+ # add errors for if tree doesnt have right data
+
+ n = nleaves(d.tree)
+ nodes = getnodes(d.tree, postorder)
+ trait = getnodedata(d.tree, nodes[1]).name
+
+ # add lengths to tree - must be a better way
+ for node in nodes
+ val = getnodedata(tree, node).value
+ if hasinbound(tree, node)
+ len = Phylo.getlength(tree, Phylo.getinbound(tree, node))
+ td = traitdata(trait, val, len)
+ setnodedata!(tree, node, td)
+ else
+ td = traitdata(trait, val)
+ setnodedata!(tree, node, td)
+ end
+ end
+
+ # multiply internal branches by lambda
+ t = [getnodedata(d.tree, node).t for node in nodes]
+
+ for (i, node) in enumerate(nodes)
+ if !isleaf(d.tree, node)
+ tupdate = d.lambda * t[i]
+ getnodedata(d.tree, node).t = tupdate
+ end
+ end
+
+ threepoint!(d.tree, trait, nodes)
+
+ nN = last(nodes)
+ nd = getnodedata(d.tree, nN)
+
+ return loglik(n, nd, d.sigma, d.beta)
+end
+
+@model function phyloinftreelambda(tree, z) # z needs to be for leaves in postorder
+ intnodeheights = nodeheights(tree, noleaves = true)
+ longnodeheight = maximum(intnodeheights)
+
+ leafnodeheights = nodeheights(tree, onlyleaves = true)
+ shortleafheight = minimum(leafnodeheights)
+
+ upper = shortleafheight / longnodeheight
+
+ lambda ~ Uniform(0, upper)
+ beta ~ Uniform(-100, 100)
+ sigma ~ Uniform(0, 100)
+
+ return z ~ MyDist3(sigma, beta, lambda, tree) # tree.z ~ (impliment later)
+end
+
+c3 = sample(phyloinftreelambda(tree, z), HMC(0.01, 5), 10000) # add initial_params
+
+loglikelihood(phyloinftreelambda(tree, z), c3)
+loglikelihood(phyloinftreelambda(tree, z),
+ (lambda = 1.0, beta = 0.0, sigma = 1.0))
+# is the problem when I set lambda to 0?
+
+n = nleaves(tree)
+nodes = getnodes(tree, postorder)
+trait = getnodedata(tree, nodes[1]).name
+
+threepoint!(tree, trait, nodes)
+
+nN = last(nodes)
+nd = getnodedata(tree, nN)
+
+ndat = getnodedata(tree, nodes[5])
+ndat.t
+
+loglik(n, nd, 1.0, 0.0)
+
+plot(c3[:beta])
+plot(c3[:sigma])
+plot(c3[:lambda])
+
+# Other method - will likely delete but keep for now incase any of it needed
+#=
+struct MyDist <: ContinuousMultivariateDistribution
+ sigma
+ beta
+end
+
+# Distributions.rand(rng::AbstractRNG, d::MyDist) = # create random tree w/ beta sigma
+
+function Distributions.logpdf(d::MyDist, tree::TraitTree)
+ # add errors for if tree doesnt have right data
+
+ nodes = getnodes(tree, postorder)
+ trait = getnodedata(tree, nodes[1]).name
+
+ threepoint!(tree, trait, nodes)
+
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ return loglik(n, nd, d.sigma, d.beta) # my defined - may need to have threepoint in function
+end
+
+function Distributions.loglikelihood(d::MyDist, tree::TraitTree)
+ # add errors for if tree doesnt have right data
+
+ nodes = getnodes(tree, postorder)
+ trait = getnodedata(tree, nodes[1]).name
+
+ threepoint!(tree, trait, nodes)
+
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ return loglik(n, nd, d.sigma, d.beta) # my defined - may need to have threepoint in function
+end
+
+@model function phyloinftree(tree)
+ beta ~ Uniform(-100, 100)
+ sigma ~ Uniform(0, 100)
+
+ tree ~ MyDist(sigma, beta)
+ # return beta, sigma
+end
+
+c2 = sample(phyloinftree(tree), HMC(0.01, 5), 100000)
+plot(c2[:beta])
+plot(c2[:sigma])
+
+=#
diff --git a/LICENSE b/LICENSE
index 903c32f5..a2fb42a9 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,4 +1,4 @@
-Copyright (c) 2017-2024 Richard Reeve, Michael Borregaard and Claire Harris
+Copyright (c) 2017-2024 Richard Reeve, Emily Davison, Michael Borregaard and Claire Harris
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
diff --git a/NEWS.md b/NEWS.md
index e725f28e..77f7eefe 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
# NEWS
+- v0.6.0
+ - Add preliminary inference code
+ - Bugfix for dropping no tips
+ - Minor metadata fixes
- v0.5.4
- Use ResearchSoftwareMetadata package
- v0.5.3
diff --git a/Project.toml b/Project.toml
index 6ba87641..1d2a4248 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,8 +1,8 @@
name = "Phylo"
uuid = "aea672f4-3940-5932-aa44-993d1c3ff149"
license = "BSD-2-Clause"
-authors = ["Richard Reeve <richard.reeve@glasgow.ac.uk>", "Michael Borregaard", "Claire Harris"]
-version = "0.5.4"
+authors = ["Richard Reeve <richard.reeve@glasgow.ac.uk>", "Emily Davison", "Michael Borregaard", "Claire Harris"]
+version = "0.6.0"
[deps]
AxisArrays = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
@@ -10,9 +10,13 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
IterableTables = "1c8ee90f-4401-5389-894e-7a04a3dc0f4d"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
ParserCombinator = "fae87a5f-d1ad-5cf0-8f61-c941e1580b46"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -20,6 +24,7 @@ Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
Requires = "ae029012-a4dd-5104-9daa-d747884805df"
SimpleTraits = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Tokenize = "0796e94c-ce3b-5d07-9a54-7f471281c624"
Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
@@ -38,12 +43,16 @@ DataFrames = "1"
DataStructures = "0.17, 0.18"
Distances = "0.10"
Distributions = "0.24, 0.25"
+DynamicPPL = "0.28"
+ForwardDiff = "0.10"
Git = "1"
Graphs = "1"
IterableTables = "1"
JuliaFormatter = "1"
+LinearAlgebra = "1.6"
Logging = "1"
Missings = "1"
+Optim = "1"
ParserCombinator = "2"
Pkg = "1.6"
Plots = "1"
@@ -54,6 +63,7 @@ RecipesBase = "1"
Requires = "1"
ResearchSoftwareMetadata = "0.1.1"
SimpleTraits = "0.9"
+StaticArrays = "1"
Statistics = "1.6"
Test = "1.6"
Tokenize = "0.5"
@@ -67,6 +77,12 @@ orcid = "0000-0003-2589-8091"
[[author_details.affiliation]]
ror = "00vtgdb53"
[[author_details]]
+name = "Emily Davison"
+orcid = "0009-0000-3656-8309"
+
+ [[author_details.affiliation]]
+ ror = "00vtgdb53"
+[[author_details]]
name = "Michael Borregaard"
orcid = "0000-0002-8146-8435"
@@ -81,6 +97,8 @@ orcid = "0000-0003-0852-2340"
[extras]
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
Git = "d7ba0133-e1db-5d97-8f8c-041e4b3a1eb2"
JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
@@ -91,4 +109,4 @@ ResearchSoftwareMetadata = "58378933-4625-47fa-851e-05ee27d397bd"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[targets]
-test = ["DataFrames", "Git", "JuliaFormatter", "Logging", "Pkg", "Plots", "RCall", "ResearchSoftwareMetadata", "Test"]
+test = ["DataFrames", "DynamicPPL", "ForwardDiff", "Git", "JuliaFormatter", "Logging", "Pkg", "Plots", "RCall", "ResearchSoftwareMetadata", "Test"]
diff --git a/codemeta.json b/codemeta.json
index af76ee58..c8b3a270 100644
--- a/codemeta.json
+++ b/codemeta.json
@@ -4,10 +4,10 @@
"applicationCategory": "ecology",
"codeRepository": "https://github.com/EcoJulia/Phylo.jl",
"dateCreated": "2017-04-21",
- "dateModified": "2024-07-20",
+ "dateModified": "2024-10-14",
"datePublished": "2018-08-11",
"description": "Package for creating and manipulating phylogenies in Julia",
- "downloadUrl": "https://github.com/EcoJulia/Phylo.jl/archive/refs/tags/v0.5.4.tar.gz",
+ "downloadUrl": "https://github.com/EcoJulia/Phylo.jl/archive/refs/tags/v0.6.0.tar.gz",
"keywords": [
"EcoJulia",
"ecology",
@@ -23,7 +23,7 @@
"macOS"
],
"programmingLanguage": "julia",
- "version": "v0.5.4",
+ "version": "v0.6.0",
"codemeta:contIntegration": {
"id": "https://github.com/EcoJulia/Phylo.jl/actions/workflows/testing.yaml"
},
@@ -48,6 +48,19 @@
}
]
},
+ {
+ "type": "Person",
+ "givenName": "Emily",
+ "familyName": "Davison",
+ "id": "https://orcid.org/0009-0000-3656-8309",
+ "affiliation": [
+ {
+ "type": "Organization",
+ "name": "University of Glasgow",
+ "identifier": "https://ror.org/00vtgdb53"
+ }
+ ]
+ },
{
"type": "Person",
"givenName": "Michael",
diff --git a/examples/Manifest.toml b/examples/Manifest.toml
new file mode 100644
index 00000000..9a21ce0a
--- /dev/null
+++ b/examples/Manifest.toml
@@ -0,0 +1,2989 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.10.5"
+manifest_format = "2.0"
+project_hash = "e9dbbb14da69893375aec0e3d86929552c34e66c"
+
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+
+ [deps.ADTypes.extensions]
+ ADTypesChainRulesCoreExt = "ChainRulesCore"
+ ADTypesEnzymeCoreExt = "EnzymeCore"
+
+ [deps.ADTypes.weakdeps]
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+
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+version = "1.5.0"
+weakdeps = ["ChainRulesCore", "Test"]
+
+ [deps.AbstractFFTs.extensions]
+ AbstractFFTsChainRulesCoreExt = "ChainRulesCore"
+ AbstractFFTsTestExt = "Test"
+
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+
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+ AccessorsTestExt = "Test"
+ AccessorsUnitfulExt = "Unitful"
+
+ [deps.Accessors.weakdeps]
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+
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diff --git a/examples/Myrtaceae_bayes.jl b/examples/Myrtaceae_bayes.jl
new file mode 100644
index 00000000..44360b51
--- /dev/null
+++ b/examples/Myrtaceae_bayes.jl
@@ -0,0 +1,138 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+using Phylo
+using Turing
+using DataFrames
+using CSV
+using Plots
+
+# load the data
+df = DataFrame(CSV.File("Data/Myrtaceae.csv"))
+
+# load the tree
+const traittree::TraitTree{1} = open(f -> parsenewick(f, TraitTree{1}),
+ "Data/Qian2016.tree")
+
+# remove missing species from dataframe
+dropmissing!(df, :species)
+
+# add underscores to dataframe
+df.species = replace.(df.species, " " => "_")
+
+# filter the tree and dataframe for the species that are in both
+keep = intersect(getleafnames(traittree), df.species)
+keeptips!(traittree, keep)
+filter!(:species => x -> x ∈ keep, df)
+
+# use mean data for trait value
+gdf = groupby(df, :species)
+dat = combine(gdf,
+ [
+ :tmin,
+ :tmax,
+ :trng,
+ :stl1,
+ :stl2,
+ :stl3,
+ :stl4,
+ :swvl1,
+ :swvl2,
+ :swvl3,
+ :swvl4,
+ :ssr,
+ :tp
+ ] .=> mean; renamecols = false)
+
+# add the data for tmin to the tmintree
+for i in eachrow(dat)
+ setnodedata!(traittree, i.species,
+ Phylo.traitdata(eltype(nodedatatype(typeof(traittree))),
+ ["tmin"], [i.tmin]))
+end
+
+trait = ["tmin"]
+# add lengths to tree
+nodes = getnodes(traittree, postorder)
+for node in nodes
+ val = getnodedata(traittree, node).value
+ if hasinbound(traittree, node)
+ len = Phylo.getlength(traittree, Phylo.getinbound(traittree, node))
+ td = traitdata(eltype(nodedatatype(typeof(traittree))), trait, val, len)
+ setnodedata!(traittree, node, td)
+ else
+ td = traitdata(eltype(nodedatatype(typeof(traittree))), trait, val)
+ setnodedata!(traittree, node, td)
+ end
+end
+
+@model function βσ()
+ β ~ Uniform(-500, 500)
+ σ ~ Uniform(0, 1000)
+ return β, σ
+end
+
+@model function βσ_threepoint(tree, z) # z needs to be for leaves in postorder
+ @submodel β, σ = βσ()
+ z ~ Phylo.MyDist2(σ, β, tree) # tree.z ~ (implement later)
+ return nothing
+end
+
+@model function βσλ_threepoint(tree, z, upper = 1.0) # z needs to be for leaves in postorder
+ @submodel β, σ = βσ()
+ λ ~ Uniform(0, upper)
+ z ~ Phylo.MyDist3(σ, β, λ, tree)
+ return nothing
+end
+
+z = dat.tmin
+
+spl = sample(βσ_threepoint(traittree, z), HMC(0.001, 10), 5000,
+ initial_params = [290.0, 8.0]) # add initial_params
+
+spl = sample(βσλ_threepoint(traittree, z), HMC(0.001, 10), 5000,
+ initial_params = [290.0, 8.0, 0.8])
+
+for i in eachrow(dat)
+ setnodedata!(traittree, i.species,
+ Phylo.traitdata(eltype(nodedatatype(typeof(traittree))),
+ ["tmax"], [i.tmax]))
+end
+
+z = dat.tmax
+
+spl = sample(βσ_threepoint(traittree, z), HMC(0.01, 10), 5000,
+ initial_params = [290.0, 8.0])
+
+spl = sample(βσλ_threepoint(traittree, z), HMC(0.01, 10), 5000,
+ initial_params = [290.0, 8.0, 0.8])
+
+for i in eachrow(dat)
+ setnodedata!(traittree, i.species,
+ Phylo.traitdata(eltype(nodedatatype(typeof(traittree))),
+ ["trng"], [i.trng]))
+end
+
+z = dat.trng
+
+spl = sample(βσ_threepoint(traittree, z), HMC(0.01, 10), 5000,
+ initial_params = [4.0, 1.0])
+
+spl = sample(βσλ_threepoint(traittree, z), HMC(0.01, 10), 5000,
+ initial_params = [4.0, 1.0, 0.8])
+
+for i in eachrow(dat)
+ setnodedata!(traittree, i.species,
+ Phylo.traitdata(eltype(nodedatatype(typeof(traittree))),
+ ["swvl1"], [i.swvl1]))
+end
+
+z = dat.swvl1
+
+estimaterates(traittree, ["swvl1"], lambda = 0.5)
+
+spl = sample(βσ_threepoint(traittree, z), HMC(0.0001, 10), 5000,
+ initial_params = [0.1, 1.0])
+
+spl = sample(βσλ_threepoint(traittree, z), HMC(0.0001, 10), 5000,
+ initial_params = [0.1, 1.0, 0.5])
+plot(spl[:λ])
diff --git a/examples/Project.toml b/examples/Project.toml
new file mode 100644
index 00000000..292972b1
--- /dev/null
+++ b/examples/Project.toml
@@ -0,0 +1,15 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+Phylo = "aea672f4-3940-5932-aa44-993d1c3ff149"
+PhyloNetworks = "33ad39ac-ed31-50eb-9b15-43d0656eaa72"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Profile = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"
+ProfileView = "c46f51b8-102a-5cf2-8d2c-8597cb0e0da7"
+RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
diff --git a/examples/example_bayes.jl b/examples/example_bayes.jl
new file mode 100644
index 00000000..99b99bb4
--- /dev/null
+++ b/examples/example_bayes.jl
@@ -0,0 +1,165 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+using Turing
+using Phylo
+using Distributions
+using Random
+using BenchmarkTools
+using DataFrames
+using LinearAlgebra
+using Plots
+
+# set seed
+Random.seed!(678)
+
+@model function βσ()
+ β ~ Uniform(-100, 100)
+ σ ~ Uniform(0, 100)
+ return β, σ
+end
+
+@model function βσ_covariance(z, C)
+ @submodel β, σ = βσ()
+ z ~ MvNormal(β * ones(length(z)), σ * C)
+ return nothing
+end
+
+@model function βσ_threepoint(tree, z) # z needs to be for leaves in postorder
+ @submodel β, σ = βσ()
+ z ~ Phylo.MyDist2(σ, β, tree) # tree.z ~ (implement later)
+ return nothing
+end
+
+@model function βσλ_threepoint(tree, z, upper = 1.0) # z needs to be for leaves in postorder
+ @submodel β, σ = βσ()
+ λ ~ Uniform(0, 1.0)
+ z ~ Phylo.MyDist3(σ, β, λ, tree)
+ return nothing
+end
+
+function gen_βσ_covariance(::Type{T}, n_tips) where {T}
+ # generate tree
+ nu = Ultrametric{T}(n_tips)
+ tree = rand(nu)
+
+ # get phylogenetic variance matrix - needed for method using built in Julia functions
+ C = fill(1.0, (n_tips, n_tips)) - distances(tree) ./ 2
+ C = abs.(Symmetric(C))
+
+ # generate traits, store in vector z
+ a = BrownianTrait(tree, "BMtrait", σ² = 1.0)
+ bm_traits = rand(a)
+ leafnames = getleafnames(tree)
+ z = [bm_traits[leaf] for leaf in leafnames]
+
+ # using built in Julia functions
+ return βσ_covariance(z, C)
+end
+
+function gen_βσ_threepoint(::Type{T}, n_tips) where {T}
+ # generate tree
+ nu = Ultrametric{T}(n_tips)
+ tree = rand(nu)
+ # generate traits, store in vector z
+ a = BrownianTrait(tree, "BMtrait", σ² = 1.0)
+ bm_traits = rand(a)
+ leafnames = getleafnames(tree)
+ z = [bm_traits[leaf] for leaf in leafnames]
+
+ # Use threepoint:
+ # pop trait data on tree
+ nodes = getnodes(tree, postorder)
+ rdat = DataFrame(species = leafnames, data = z)
+ for i in eachrow(rdat)
+ setnodedata!(tree, i.species,
+ Phylo.traitdata(eltype(nodedatatype(typeof(tree))),
+ ["trait"],
+ [i.data]))
+ end
+
+ # trait needs to be a vector of trait names, used for functions later
+ trait = ["trait"]
+
+ # add lengths to tree
+ for node in nodes
+ val = getnodedata(tree, node).value
+ if hasinbound(tree, node)
+ len = Phylo.getlength(tree, Phylo.getinbound(tree, node))
+ td = traitdata(eltype(nodedatatype(typeof(tree))), trait, val, len)
+ setnodedata!(tree, node, td)
+ else
+ td = traitdata(eltype(nodedatatype(typeof(tree))), trait, val)
+ setnodedata!(tree, node, td)
+ end
+ end
+
+ return βσ_threepoint(tree, z)
+end
+
+function gen_βσλ_threepoint(::Type{T}, n_tips) where {T}
+ # generate tree
+ nu = Ultrametric{T}(n_tips)
+ tree = rand(nu)
+ # distances(tree)
+
+ # generate traits, store in vector z
+ a = BrownianTrait(tree, "BMtrait", σ² = 1.0)
+ bm_traits = rand(a)
+ leafnames = getleafnames(tree)
+ z = [bm_traits[leaf] for leaf in leafnames]
+
+ # Use threepoint:
+ # pop trait data on tree
+ nodes = getnodes(tree, postorder)
+ rdat = DataFrame(species = leafnames, data = z)
+ for i in eachrow(rdat)
+ setnodedata!(tree, i.species,
+ Phylo.traitdata(eltype(nodedatatype(typeof(tree))),
+ ["trait"], [i.data]))
+ end
+
+ # trait needs to be a vector of trait names, used for functions later
+ trait = ["trait"]
+
+ # add lengths to tree
+ for node in nodes
+ val = getnodedata(tree, node).value
+ if hasinbound(tree, node)
+ len = Phylo.getlength(tree, Phylo.getinbound(tree, node))
+ td = traitdata(eltype(nodedatatype(typeof(tree))), trait, val, len)
+ setnodedata!(tree, node, td)
+ else
+ td = traitdata(eltype(nodedatatype(typeof(tree))), trait, val)
+ setnodedata!(tree, node, td)
+ end
+ end
+
+ return βσλ_threepoint(tree, z)
+end
+
+# number of tips on the tree
+n_tips = 200
+n_samples = 10_000
+
+model = gen_βσ_covariance(TraitTree{1}, n_tips);
+sample(model, HMC(0.01, 5), 1);
+spl = sample(model, HMC(0.01, 5), n_samples) # add initial_params
+plot(spl[:β])
+plot(spl[:σ])
+
+model = gen_βσ_threepoint(Phylo.TraitTreeNum{1}, n_tips);
+model = gen_βσ_threepoint(TraitTree{1}, n_tips);
+model = gen_βσ_threepoint(Phylo.TraitTreeFloat64{1}, n_tips);
+sample(model, HMC(0.01, 5), 1);
+spl = sample(model, HMC(0.01, 5), n_samples) # add initial_params
+plot(spl[:β])
+plot(spl[:σ])
+
+model = gen_βσλ_threepoint(Phylo.TraitTreeNum{1}, n_tips);
+model = gen_βσλ_threepoint(Phylo.TraitTreeDual{1}, n_tips);
+model = gen_βσλ_threepoint(TraitTree{1}, n_tips);
+sample(model, HMC(0.01, 5), 1);
+spl = sample(model, HMC(0.01, 5), n_samples) # add initial_params
+plot(spl[:β])
+plot(spl[:σ])
+plot(spl[:λ])
diff --git a/examples/example_inference.jl b/examples/example_inference.jl
new file mode 100644
index 00000000..ce3f0be9
--- /dev/null
+++ b/examples/example_inference.jl
@@ -0,0 +1,428 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+using BenchmarkTools
+using Phylo
+using DataFrames
+using Random
+using CSV
+using Profile
+using ProfileView
+using Statistics
+
+# needed for phylolm comaparison
+using RCall
+
+# needed for Phyloetworks comaparison
+using PhyloNetworks
+using StatsModels
+
+Random.seed!(1234)
+
+# Compare Phylo against phylolm and PhyloNetworks on a tree with uniform random tips
+# Load the hummingbirds tree from test
+const jtree::TraitTree{1} = open(f -> parsenewick(f, TraitTree{1}),
+ "test/hummingbirds.tree")
+
+# Create dataframe with leafnames and random trait values
+species = getleaves(jtree)
+data = 1000 .* rand(length(species))
+
+# Create dataframe
+speciesnames = getleafnames(jtree);
+rdat = DataFrame(species = speciesnames, data = data);
+
+# add the data to the tree
+for i in eachrow(rdat)
+ setnodedata!(jtree, i.species, Phylo.traitdata(["trait"], [i.data]))
+end
+
+# run inference
+estimaterates(jtree, ["trait"])
+
+Profile.clear_malloc_data()
+
+ProfileView.@profview for i in 1:1000
+ estimaterates(jtree, ["trait"])
+end
+
+# check inference run time
+@benchmark estimaterates(jtree, ["trait"])
+
+# run inference to calculate signal
+estimaterates(jtree, ["trait"], lambda = 0.1)
+
+# check inference run time
+@benchmark estimaterates(jtree, ["trait"], lambda = 0.1)
+
+Profile.clear_malloc_data()
+
+ProfileView.@profview for i in 1:10
+ estimaterates(jtree, ["trait"], lambda = 0.1)
+end
+
+# Compare against phylolm in R
+# load R packages and read tree into R
+R"
+library('ape')
+library('phylolm')"
+
+R"rtree = ape::read.tree(file = 'test/hummingbirds.tree')"
+
+# send dataframe into R
+@rput rdat
+
+# species need to be the row names in R
+R"row.names(rdat) <- rdat$species"
+
+# run inference
+rcopy(R"phylolm(data~1,data=rdat,phy=rtree)")
+
+# check inference run time
+@benchmark rcopy(R"phylolm(data~1,data=rdat,phy=rtree)")
+
+# run inference to calculate signal
+rcopy(R"phylolm(data~1,data=rdat,phy=rtree, model = c('lambda'))")
+
+# check inference run time
+@benchmark rcopy(R"phylolm(data~1,data=rdat,phy=rtree, model = c('lambda'))")
+
+# Compare against PhyloNetworks
+# load the tree using PhyloNetworks
+tree2 = readTopology("test/hummingbirds.tree");
+
+# get tip names
+tipnames = tipLabels(tree2)
+
+# dataframe needs column called tipNames
+dat = DataFrame(tipNames = tipnames, data = data)
+
+# run inference
+phylolm(@formula(data~1), dat, tree2)
+
+# check inference run time
+@benchmark plm = phylolm(@formula(data~1), dat, tree2)
+
+# run inference to calculate signal
+phylolm(@formula(data~1), dat, tree2, model = "lambda")
+
+# check inference run time
+@benchmark plm = phylolm(@formula(data~1), dat, tree2, model = "lambda")
+
+# Comparison done using the same tree, but with tip trait data generated using Brownian Motion
+# Generate data using Brownian Motion
+a = BrownianTrait(jtree, "BMtrait")
+bm_traits = rand(a)
+
+# create a vector to store trait data
+nodes = getnodenames(jtree)
+leafnames = getleafnames(jtree);
+traitvector = [bm_traits[leaf] for leaf in leafnames]
+
+# Crate dataframe
+rdat = DataFrame(species = leafnames, data = traitvector)
+
+# add data to the tree
+for i in eachrow(rdat)
+ setnodedata!(jtree, i.species, Phylo.traitdata(["trait"], [i.data]))
+end
+
+# run inference
+estimaterates(jtree, ["trait"])
+
+# check inference run time
+@benchmark estimaterates(jtree, ["trait"])
+
+# run inference to calculate signal
+estimaterates(jtree, ["trait"], lambda = 0.1)
+
+# check inference run time
+@benchmark estimaterates(jtree, ["trait"], lambda = 0.1)
+
+# Compare against phylolm in R
+# load R packages and read tree into R
+R"
+library('ape')
+library('phylolm')"
+
+R"rtree = ape::read.tree(file = 'test/hummingbirds.tree')"
+
+# send dataframe into R
+@rput rdat
+
+# species need to be the row names in R
+R"row.names(rdat) <- rdat$species"
+
+# run inference
+rcopy(R"phylolm(data~1,data=rdat,phy=rtree)")
+
+# check inference run time
+@benchmark rcopy(R"phylolm(data~1,data=rdat,phy=rtree)")
+
+# run inference to calculate signal
+rcopy(R"phylolm(data~1,data=rdat,phy=rtree, model = c('lambda'))")
+
+# check inference run time
+@benchmark rcopy(R"phylolm(data~1,data=rdat,phy=rtree, model = c('lambda'))")
+
+# Compare against PhyloNetworks
+# dataframe needs column called tipNames
+dat = DataFrame(tipNames = tipnames, data = traitvector)
+
+# dataframe needs column called tipNames
+dat = DataFrame(tipNames = tipnames, data = data)
+
+# run inference
+phylolm(@formula(data~1), dat, tree2)
+
+# check inference run time
+@benchmark plm = phylolm(@formula(data~1), dat, tree2)
+
+# run inference to calculate signal
+phylolm(@formula(data~1), dat, tree2, model = "lambda")
+
+# check inference run time
+@benchmark plm = phylolm(@formula(data~1), dat, tree2, model = "lambda")
+
+# Using the Myrtaceae tree, compare packages using real world data
+# load the data
+df = DataFrame(CSV.File("Data/Myrtaceae.csv"))
+
+# load the tree
+const bigtree::TraitTree{1} = open(f -> parsenewick(f, TraitTree{1}),
+ "Data/Qian2016.tree")
+
+# remove missing species from dataframe
+dropmissing!(df, :species)
+
+# add underscores to dataframe
+df.species = replace.(df.species, " " => "_")
+
+# filter the tree and dataframe for the species that are in both
+keep = intersect(getleafnames(bigtree), df.species)
+keeptips!(bigtree, keep)
+filter!(:species => x -> x ∈ keep, df)
+
+# use mean data for trait value
+gdf = groupby(df, :species)
+dat = combine(gdf,
+ [
+ :tmin,
+ :tmax,
+ :trng,
+ :stl1,
+ :stl2,
+ :stl3,
+ :stl4,
+ :swvl1,
+ :swvl2,
+ :swvl3,
+ :swvl4,
+ :ssr,
+ :tp
+ ] .=> mean; renamecols = false)
+
+# add the data for tmin to the tree
+for i in eachrow(dat)
+ setnodedata!(bigtree, i.species, Phylo.traitdata(["tmin"], [i.tmin]))
+end
+
+# run inference
+estimaterates(bigtree, ["tmin"])
+
+# check inference run time
+@benchmark estimaterates(bigtree, ["tmin"])
+
+# run inference to calculate signal
+estimaterates(bigtree, ["tmin"], lambda = 0.8)
+
+# check inference run time
+@benchmark estimaterates(bigtree, ["tmin"], lambda = 0.8)
+
+# Compare against phylolm in R
+# load packages and tree
+R"
+library('ape')
+library('phylolm')"
+
+R"rbigtree = ape::read.tree(file = 'Data/Qian2016.tree')"
+
+# transfer data and list of species in both the tree and data into R
+@rput dat
+@rput keep
+
+# species need to be the row names in R
+R"row.names(dat) <- dat$species"
+
+# filter the tree
+R"filteredtree <- keep.tip(rbigtree, keep)"
+
+# run inference
+rcopy(R"phylolm(tmin~1,data=dat,phy=filteredtree)")
+
+# check inference run time
+@benchmark rcopy(R"phylolm(tmin~1,data=dat,phy=filteredtree)")
+
+# run inference to calculate signal
+rcopy(R"phylolm(tmin~1,data=dat,phy=filteredtree, model = c('lambda'))")
+
+# check inference run time
+@benchmark rcopy(R"phylolm(tmin~1,data=dat,phy=filteredtree, model = c('lambda'))")
+
+# Compare against PhyloNetworks
+# load the tree and get the names of the leaves
+tree = readTopology("Data/Qian2016.tree");
+tipnames = tipLabels(tree)
+
+# Filter for species in the tree and the data
+missing_species = setdiff(tipnames, keep)
+for i in eachindex(missing_species)
+ deleteleaf!(tree, join(split(missing_species[i], " "), "_"))
+end
+
+# species column in the data needs to be tipNames
+rename!(dat, :species => :tipNames)
+
+# run inference
+phylolm(@formula(tmin~1), dat, tree)
+
+# check inference run time
+@benchmark phylolm(@formula(tmin~1), dat, tree)
+
+# run inference to calculate signal
+phylolm(@formula(tmin~1), dat, tree, model = "lambda")
+
+# check run time
+@benchmark phylolm(@formula(tmin~1), dat, tree, model = "lambda")
+
+# Preform inference on multiple traits, both generated uniformly
+
+# load the hummingbirds tree that can store data for two trait values
+const multtree::TraitTree{2} = open(f -> parsenewick(f, TraitTree{2}),
+ "test/hummingbirds.tree")
+
+# load leafnames and generate uniform random trait values
+species = getleaves(multtree)
+data1 = 1000 .* rand(length(species))
+data2 = 1000 .* rand(length(species))
+
+# create dataframe
+speciesnames = getleafnames(multtree);
+rdat = DataFrame(species = speciesnames, data1 = data1, data2 = data2);
+
+# add the data to the tree
+for i in eachrow(rdat)
+ setnodedata!(multtree, i.species,
+ Phylo.traitdata(["trait1", "trait2"], [i.data1, i.data2]))
+end
+
+# run inference
+estimaterates(multtree, ["trait1", "trait2"])
+
+# check inference run time
+@benchmark estimaterates(multtree, ["trait1", "trait2"])
+
+# run inference to calculate Signal
+estimaterates(multtree, ["trait1", "trait2"], lambda = 0.1)
+
+# check inference run time
+@benchmark estimaterates(multtree, ["trait1", "trait2"], lambda = 0.1)
+
+# Preform inference on multiple traits, both generated through Brownian Motion
+# load a new tree
+const multtree2::TraitTree{2} = open(f -> parsenewick(f, TraitTree{2}),
+ "test/hummingbirds.tree")
+
+# gennerate data
+a = BrownianTrait(multtree2, "BMtrait")
+b = BrownianTrait(multtree2, "BMtrait")
+BMtrait1 = rand(a)
+BMtrait2 = rand(b)
+
+# get leaf names
+leafnames = getleafnames(multtree2);
+
+# add data to a vector
+traitvector1 = Vector{Float64}();
+traitvector2 = Vector{Float64}();
+for leaf in leafnames
+ push!(traitvector1, BMtrait1[leaf])
+ push!(traitvector2, BMtrait2[leaf])
+end
+
+# create a dataframe
+rdat = DataFrame(species = leafnames, data1 = traitvector1,
+ data2 = traitvector2)
+
+# add data to the tree
+for i in eachrow(rdat)
+ setnodedata!(multtree2, i.species,
+ Phylo.traitdata(["trait1", "trait2"], [i.data1, i.data2]))
+end
+
+# run inference
+estimaterates(multtree2, ["trait1", "trait2"])
+
+# check inference run time
+@benchmark estimaterates(multtree2, ["trait1", "trait2"])
+
+# run inference to calculate Signal
+estimaterates(multtree2, ["trait1", "trait2"], lambda = 0.1)
+
+# check inference run time
+@benchmark estimaterates(multtree2, ["trait1", "trait2"], lambda = 0.1)
+
+# Preform inference on multiple traits using real world data
+# load the tree
+const multtree3::TraitTree{2} = open(f -> parsenewick(f, TraitTree{2}),
+ "Data/Qian2016.tree")
+
+# load the data
+df = DataFrame(CSV.File("Data/Myrtaceae.csv"))
+
+# remove missing species from dataframe
+dropmissing!(df, :species)
+
+# add underscores to dataframe
+df.species = replace.(df.species, " " => "_")
+
+# filter the tree and dataframe for the species that are in both
+keep = intersect(getleafnames(multtree3), df.species)
+keeptips!(multtree3, keep)
+filter!(:species => x -> x ∈ keep, df)
+
+# use mean data for trait value
+gdf = groupby(df, :species)
+dat = combine(gdf,
+ [
+ :tmin,
+ :tmax,
+ :trng,
+ :stl1,
+ :stl2,
+ :stl3,
+ :stl4,
+ :swvl1,
+ :swvl2,
+ :swvl3,
+ :swvl4,
+ :ssr,
+ :tp
+ ] .=> mean; renamecols = false)
+
+# add the data for tmin to the tree
+for i in eachrow(dat)
+ setnodedata!(multtree3, i.species,
+ Phylo.traitdata(["tmin", "tmax"], [i.tmin, i.tmax]))
+end
+
+# run inference
+estimaterates(multtree3, ["tmin", "tmax"])
+
+# check inference run time
+@benchmark estimaterates(multtree3, ["tmin", "tmax"])
+
+# run inference to calculate Signal
+estimaterates(multtree3, ["tmin", "tmax"], lambda = 0.5)
+
+# check inference run time
+@benchmark estimaterates(multtree3, ["tmin", "tmax"], lambda = 0.5)
diff --git a/examples/testmulti.jl b/examples/testmulti.jl
new file mode 100644
index 00000000..53bafb33
--- /dev/null
+++ b/examples/testmulti.jl
@@ -0,0 +1,156 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+using Phylo
+using DataFrames
+using Random
+using CSV
+using Statistics
+using Profile
+using ProfileView
+using Optim # need to remember to remove
+
+estimaterates(multtree2, ["trait1", "trait2"], lambda = [0.8, 0.8])
+
+Random.seed!(110)
+
+n_tips = 10
+# generate tree
+nu = Ultrametric{TraitTree{2}}(n_tips);
+tree = rand(nu)
+
+a = BrownianTrait(tree, "BMtrait")
+b = BrownianTrait(tree, "BMtrait")
+bm_traits1 = rand(a)
+bm_traits2 = rand(b)
+
+leafnames = getleafnames(tree);
+z1 = [bm_traits1[leaf] for leaf in leafnames];
+z2 = [bm_traits2[leaf] for leaf in leafnames];
+
+nodes = getnodes(tree, postorder)
+
+# Crate dataframe
+rdat = DataFrame(species = leafnames, data1 = z1, data2 = z2)
+
+# add data to the tree
+for i in eachrow(rdat)
+ setnodedata!(tree, i.species,
+ Phylo.traitdata(["trait1", "trait2"], [i.data1, i.data2]))
+end
+
+estimaterates(tree, ["trait1", "trait2"], lambda = [0.8, 0.8])
+
+# Preform inference on multiple traits using real world data
+# load the tree
+const multtree3::TraitTree{2} = open(f -> parsenewick(f, TraitTree{2}),
+ "Data/Qian2016.tree");
+
+# load the data
+df = DataFrame(CSV.File("Data/Myrtaceae.csv"))
+
+# remove missing species from dataframe
+dropmissing!(df, :species)
+
+# add underscores to dataframe
+df.species = replace.(df.species, " " => "_")
+
+# filter the tree and dataframe for the species that are in both
+keep = intersect(getleafnames(multtree3), df.species)
+keeptips!(multtree3, keep)
+filter!(:species => x -> x ∈ keep, df)
+
+# use mean data for trait value
+gdf = groupby(df, :species)
+dat = combine(gdf,
+ [
+ :tmin,
+ :tmax,
+ :trng,
+ :stl1,
+ :stl2,
+ :stl3,
+ :stl4,
+ :swvl1,
+ :swvl2,
+ :swvl3,
+ :swvl4,
+ :ssr,
+ :tp
+ ] .=> mean; renamecols = false)
+
+# add the data for tmin to the tree
+for i in eachrow(dat)
+ setnodedata!(multtree3, i.species,
+ Phylo.traitdata(["tmin", "tmax"],
+ [i.tmin, i.tmax]))
+end
+
+lambda = fill(0.8, 2);
+
+@time estimaterates(multtree3, ["tmin", "tmax"], lambda = lambda)
+
+Profile.clear_malloc_data()
+
+ProfileView.@profview for i in 1:3
+ estimaterates(multtree3, ["tmin", "tmax"], lambda = lambda)
+end
+
+#=
+nodes = getnodes(multtree3, postorder)
+
+nN = last(nodes)
+nd = getnodedata(multtree3, nN)
+
+k = length(2)
+
+n = nleaves(multtree3)
+=#
+
+# test w/o signal to see if getting the thing I expect
+
+const multtree2::TraitTree{2} = open(f -> parsenewick(f, TraitTree{2}),
+ "test/hummingbirds.tree");
+
+# gennerate data
+a = BrownianTrait(multtree2, "BMtrait", σ² = 100)
+b = BrownianTrait(multtree2, "BMtrait", σ² = 3)
+BMtrait1 = rand(a)
+BMtrait2 = rand(b)
+
+# get leaf names
+leafnames = getleafnames(multtree2);
+
+# add data to a vector
+traitvector1 = Vector{Float64}();
+traitvector2 = Vector{Float64}();
+for leaf in leafnames
+ push!(traitvector1, BMtrait1[leaf])
+ push!(traitvector2, BMtrait2[leaf])
+end
+
+# create a dataframe
+rdat = DataFrame(species = leafnames, data1 = traitvector1,
+ data2 = traitvector2)
+
+# add data to the tree
+for i in eachrow(rdat)
+ setnodedata!(multtree2, i.species,
+ Phylo.traitdata(["trait1", "trait2"], [i.data1, i.data2]))
+end
+
+# run inference
+estimaterates(multtree2, ["trait1", "trait2"])
+
+estimaterates(multtree2, ["trait1", "trait2"], lambda = [1.0, 1.0])
+
+nodes = getnodes(multtree2, postorder)
+
+nN = last(nodes)
+nd = getnodedata(multtree2, nN)
+
+k = 2
+
+n = nleaves(multtree2)
+
+betahat = inv(nd.xx) * nd.Q
+sigmahat = (nd.yy - 2 * betahat * nd.Q' + betahat * nd.xx * betahat') / n
diff --git a/ext/PhyloRCallExt.jl b/ext/PhyloRCallExt.jl
index 11afafd6..bf37b8cf 100644
--- a/ext/PhyloRCallExt.jl
+++ b/ext/PhyloRCallExt.jl
@@ -78,7 +78,7 @@ function sexp(tree::T) where {T <: AbstractTree}
tor[:edge] = edges
tor[Symbol("edge.length")] = lengths
sobj = protect(sexp(tor))
- #setattrib!(sobj, :order, sexp("cladewise"))
+ # setattrib!(sobj, :order, sexp("cladewise"))
setclass!(sobj, sexp("phylo"))
unprotect(1)
return sobj
diff --git a/src/API.jl b/src/API.jl
index c3cc7588..d26930aa 100644
--- a/src/API.jl
+++ b/src/API.jl
@@ -922,7 +922,7 @@ function _src end
_src(::T, ::B) where {T, B} = error("No _src() function for $T, $B")
"""
- _dst(branch::AbstractBranch)
+ _dst(tree::AbstractTree, branch::AbstractBranch)
Return destination node for a branch. Must be implemented for any rooted
AbstractBranch subtype.
diff --git a/src/Phylo.jl b/src/Phylo.jl
index d6a24fc2..05ba1a53 100644
--- a/src/Phylo.jl
+++ b/src/Phylo.jl
@@ -215,6 +215,10 @@ include("metrics.jl")
export mrca, nodeheights
export distance, distances, heighttoroot, heightstoroot
+# Inference on tree
+include("inference.jl")
+export estimaterates, TraitTree, traitdata, threepoint!
+
# Path into package
path(path...; dir::String = "test") = joinpath(@__DIR__, "..", dir, path...)
diff --git a/src/RecursiveTree.jl b/src/RecursiveTree.jl
index 14333266..5f46a962 100644
--- a/src/RecursiveTree.jl
+++ b/src/RecursiveTree.jl
@@ -600,7 +600,7 @@ _getnodedata(::RecursiveTree, node::RecursiveNode) = node.data
import Phylo.API: _setnodedata!
function _setnodedata!(::RecursiveTree{RT, NL, NodeData, BranchData},
node::RecursiveNode{RT, NL, NodeData, BranchData},
- data::NodeData) where {RT, NL, NodeData, BranchData}
+ data) where {RT, NL, NodeData, BranchData}
return (node.data = data)
end
diff --git a/src/inference.jl b/src/inference.jl
new file mode 100644
index 00000000..635fee75
--- /dev/null
+++ b/src/inference.jl
@@ -0,0 +1,759 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+using DataFrames
+using LinearAlgebra
+using Optim
+using Distributions
+using ForwardDiff
+using DynamicPPL
+
+abstract type AbstractTraitData end
+
+mutable struct TraitData{T <: Number, NTraits} <: AbstractTraitData # had to change types to Number for Bayes to work w/ lambda
+ name::Vector{String} # Name of traits
+ value::Vector{Float64} # Trait values
+ t::T # branch length
+ logV::T
+ p::T # 1' V^(-1) 1
+ yl::Vector{T} # 1' V^(-1) y (y is trait values)
+ xl::T # 1' V^(-1) x (we set x as 1)
+ Q::Vector{T} # x' V^(-1) y
+ xx::T # x' V^(-1) x
+ yy::T # y' V^(-1) y
+ v::Vector{Float64} # used for PIC
+ xlmult::Vector{Float64} # 1' W^(-1) x
+ pmult::Matrix{Float64} # 1' W^(-1) C1
+ xxmult::Matrix{Float64} # x' W^(-1) x
+ yymult::Float64 # y' W^(-1) y
+ Qmult::Float64 # x' W^(-1) y
+ ylmult::Matrix{Float64} # 1' W^(-1) y
+end
+
+import Base.eltype
+eltype(::TD) where {T <: Number, NT, TD <: TraitData{T, NT}} = T
+eltype(::Type{TD}) where {T <: Number, NT, TD <: TraitData{T, NT}} = T
+
+# change these from Float to <: Number too
+function traitdata(::Type{T}, name::String, value::Float64,
+ t = 0.0) where {T <: Number}
+ return traitdata(T, [name], [value], t)
+end
+
+function traitdata(::Type{T}, name::Vector{String}, value::Vector{Float64},
+ t = 0.0) where {T <: Number}
+ return TraitData{T, length(name)}(name, value, T(t), T(NaN), T(NaN),
+ fill(T(NaN), length(name)), T(NaN),
+ fill(T(NaN), length(name)), T(NaN),
+ T(NaN), fill(NaN, length(name)),
+ fill(NaN, length(name)),
+ fill(NaN, length(name), length(name)),
+ fill(NaN, length(name), length(name)),
+ NaN, NaN,
+ fill(NaN, length(name), length(name)))
+end
+
+function TraitData{T, NTraits}() where {T <: Number, NTraits}
+ return traitdata(T, fill("", NTraits), fill(NaN, NTraits))
+end
+
+const TReB{T, NT, RT, BT, LenUnits} = RecursiveBranch{RT, String,
+ TraitData{T, NT}, Nothing,
+ BT, LenUnits}
+const TReN{T, NT, RT, BT, LenUnits} = RecursiveNode{RT, String,
+ TraitData{T, NT}, Nothing,
+ BT, LenUnits}
+const TReT{T, NT, RT, TD, BT, LenUnits} = RecursiveTree{RT, String,
+ TraitData{T, NT},
+ Nothing, BT, LenUnits,
+ TD}
+const TReTD{T, NT, RT, BT, LenUnits} = TReT{T, NT, RT, Dict{String, Any}, BT,
+ LenUnits}
+const TraitTreeFloat64{NTraits} = TReTD{Float64, NTraits, OneRoot,
+ PolytomousBranching, Float64}
+const TraitTreeDual{NTraits} = TReTD{ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag,
+ Float64},
+ Float64, 3}, NTraits,
+ OneRoot, PolytomousBranching, Float64}
+const TraitTreeNum{NTraits} = TReTD{Number, NTraits, OneRoot,
+ PolytomousBranching, Float64}
+const TraitTree{NTraits} = TReTD{Union{Float64,
+ ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag,
+ Float64},
+ Float64, 3}}, NTraits,
+ OneRoot, PolytomousBranching, Float64}
+
+function threepoint!(tree::T, trait::Vector{String},
+ nodes::Vector{N}) where
+ {TT, RT, NL, N <: AbstractElt{RT, NL}, B <: AbstractElt{RT, NL},
+ T <: AbstractTree{TT, RT, NL, N, B}}
+ # prefrom algortihm in Ho & Ane 2014
+ # function estimaterates gets inputs into right form
+ # nodes - vector of nodes in the traversal order
+
+ for node in nodes
+ nd = getnodedata(tree, node)
+ nodet = nd.t
+
+ # need to see if node is a tip (leaf)
+ if isleaf(tree, node)
+ nodetrait = nd.value
+
+ # update node data
+ nd.logV = log(nodet)
+ nd.p = inv(nodet)
+ nd.yl = nodetrait
+ nd.xl = 1.0
+ nd.Q = nodetrait / nodet
+ nd.xx = inv(nodet)
+ nd.yy = nodetrait' * nodetrait / nodet
+
+ else
+ # need to find direct desendents
+ children = getchildren(tree, node)
+
+ # child data
+ childdata = getnodedata.(tree, children)
+
+ # calculations
+ childp = [s.p for s in childdata]
+ pA = sum(childp)
+ ws = childp / pA
+
+ calc = 1.0 + nodet * pA
+ nd.logV = sum(s.logV for s in childdata) + log(calc)
+ nd.p = pA / calc
+ nd.xl = sum(ws .* [s.xl for s in childdata])
+ nd.yl = sum(ws .* [s.yl for s in childdata])
+
+ c2 = nodet * pA^2 / calc
+ nd.Q = sum(s.Q for s in childdata) - c2 * nd.xl * nd.yl
+ nd.xx = sum(s.xx for s in childdata) - c2 * nd.xl * nd.xl
+ nd.yy = sum(s.yy for s in childdata) - c2 * nd.yl' * nd.yl
+ end
+ # @assert getnodedata(tree, node) === nd
+ end
+
+ return tree
+end
+
+function estimaterates!(tree::T, trait::Vector{String},
+ lambda) where {T <: AbstractTree}
+
+ # get information from tree in order to preform threepoint
+ nodes = getnodes(tree, postorder)
+ n = nleaves(tree)
+
+ if lambda !== missing
+ t = [getnodedata(tree, node).t for node in nodes]
+
+ # info for optimiser
+ lower = [0.0]
+ start = [lambda]
+
+ # upper is going to be largest lambda can be without going past the leaves
+ # want to find longest internal branch
+ intnodeheights = nodeheights(tree, noleaves = true)
+ longnodeheight = maximum(intnodeheights)
+
+ leafnodeheights = nodeheights(tree, onlyleaves = true)
+ shortleafheight = minimum(leafnodeheights)
+
+ upper = [shortleafheight / longnodeheight]
+
+ # optimise to find lambda
+ opts = optimize(x -> tooptimise(x, tree, nodes, trait, n),
+ lower, upper, start, Fminbox(LBFGS()),
+ Optim.Options(time_limit = 600))
+ lambda = Optim.minimizer(opts)
+
+ # update internal branches
+ for (i, node) in enumerate(nodes)
+ if !isleaf(tree, node)
+ tupdate = lambda[1] * t[i]
+ getnodedata(tree, node).t = tupdate
+ end
+ end
+ end
+
+ threepoint!(tree, trait, nodes)
+
+ n = nleaves(tree)
+
+ # information from last node
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ betahat = inv(nd.xx) * nd.Q
+ sigmahat = ((nd.yy .- 2 * betahat .* nd.Q' .+ betahat .* nd.xx .* betahat') ./
+ n)
+
+ # NEED TO THINK ABOUT THIS
+ while any(i -> i < 0, diag(sigmahat))
+ leaves = getleaves(tree)
+ for leaf in leaves
+ ld = getnodedata(tree, leaf)
+ ld.value = betahat - ld.value
+ end
+
+ threepoint!(tree, trait, nodes)
+ sigmahat = nd.yy / n
+ end
+
+ k = length(trait)
+
+ negloglik = (1.0 / 2.0) *
+ (n * k * log(2π) + nd.logV + n + n * log(abs(det(sigmahat))))
+
+ return betahat, sigmahat, negloglik, lambda # only return lambda if used
+end
+
+function estimaterates(tree::T, trait::Vector{String};
+ lambda = missing) where {T <: AbstractTree}
+ # Returns evolution rate, starting value and negative log loglikelihood for traits on tip of tree
+ # INPUTS
+ # tree = tree with lengths, leaves all same length, trait data on leaves
+ # trait = string with name of trait as found on leaves
+ # OUTPUTS
+ # sigmahat - evolution rate
+ # betahat - estimated root trait value
+ # negloglik - negative loglikelihood
+
+ # need to add meaningful error when cant find traits on tree leaves
+
+ nodes = getnodes(tree, anyorder)
+ NTraits = length(trait)
+
+ for node in nodes
+ val = getnodedata(tree, node).value
+ if hasinbound(tree, node)
+ len = _getlength(tree, _getinbound(tree, node))
+ td = traitdata(eltype(nodedatatype(T)), trait, val, len)
+ setnodedata!(tree, node, td)
+ else
+ td = traitdata(eltype(nodedatatype(T)), trait, val)
+ setnodedata!(tree, node, td)
+ end
+ end
+
+ return estimaterates!(tree, trait, lambda)
+end
+
+function tooptimise(lambda::Vector{Float64}, tree::T, nodes::Vector{N},
+ trait::Vector{String},
+ n::Int) where
+ {TT, RT, NL, N <: AbstractElt{RT, NL}, B <: AbstractElt{RT, NL},
+ T <: AbstractTree{TT, RT, NL, N, B}}
+ # lambda - value for Signal
+ # tree - tree
+ # nodes - nodes of the tree in traversal order
+ # t - vector of og branch lengths
+ # trait - string of what trait is called on the tree
+ # N - total number of nodes
+ # n - number of leaves
+
+ for node in nodes
+ # val = getnodedata(tree, node).value
+ if hasinbound(tree, node)
+ len = _getlength(tree, _getinbound(tree, node)) * lambda[1]
+ if isleaf(tree, node)
+ full = getheight(tree, node)
+ len = len + full * (1 - lambda[1])
+ end
+ getnodedata(tree, node).t = len
+ else
+ getnodedata(tree, node).t = 0.0
+ end
+ end
+
+ threepoint!(tree, trait, nodes)
+
+ # information from last node
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ betahat = inv(nd.xx) * nd.Q
+ sigmahat = (nd.yy .- 2 * betahat * nd.Q' .+ betahat * nd.xx * betahat') ./ n
+
+ while any(i -> i < 0, sigmahat)
+ leaves = getleaves(tree)
+ for leaf in leaves
+ ld = getnodedata(tree, leaf)
+ ld.value = betahat - ld.value
+ end
+
+ threepoint!(tree, trait, nodes)
+ sigmahat = nd.yy / n
+ end
+
+ negloglik = (1.0 / 2.0) *
+ (n * log(2π) .+ nd.logV + n + n * log(det(sigmahat)))
+ return negloglik
+end
+
+# ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## ## #
+
+function threepointmultlambda!(tree::T, trait::Vector{String}, nodes::Vector{N},
+ C::Matrix{Float64},
+ lambda::Vector{Float64}) where
+ {TT, RT, NL, N <: AbstractElt{RT, NL}, B <: AbstractElt{RT, NL},
+ T <: AbstractTree{TT, RT, NL, N, B}}
+ # prefrom algortihm in Ho & Ane 2014 for multiple lambda
+ # function estimaterates gets inputs into right form
+
+ for node in nodes
+ nd = getnodedata(tree, node)
+ nodet = nd.t
+
+ C1 = diagm(sqrt.(lambda)) * C * diagm(sqrt.(lambda))
+
+ C1inv = C1^(-1)
+
+ k = length(trait)
+
+ I = diagm(ones(k))
+
+ if isleaf(tree, node)
+ nodetrait = nd.value
+
+ height = heighttoroot(tree, node)
+
+ # update node data
+ nd.logV = log(abs(det(C * height - (height - nodet) * C1)))
+ nd.pmult = inv(height * C * C1inv - (height - nodet) * I)
+ nd.ylmult = repeat(nodetrait, 1, k)
+ nd.xlmult = ones(k)
+ nd.Q = nd.ylmult' * nd.pmult * C1inv * ones(k)
+ nd.xx = ones(k)' * nd.pmult * C1inv * ones(k)
+ nd.yy = nd.ylmult' * nd.pmult * C1inv * nd.ylmult
+
+ else
+ # find direct desendents
+ children = getchildren(tree, node)
+
+ # child data
+ childdata = getnodedata.(tree, children)
+
+ # calc pA and ws
+ childp = [s.pmult for s in childdata]
+ pA = sum(childp)
+
+ nochildren = length(childp)
+
+ ws = fill(fill(NaN, k, k), nochildren)
+
+ pAinv = pA^(-1)
+ for i in 1:nochildren
+ ws[i] = pAinv * childp[i]
+ end
+
+ # update node data
+ nd.logV = sum(s.logV for s in childdata) +
+ log(abs(det(I + nodet * pA))) # need to think about why the determinant is negative and if its okay
+ nd.pmult = pA * (I + nodet * pA)^(-1)
+ nd.xlmult = sum(ws .* [s.xlmult for s in childdata]) # dimensions may be wrong here
+ nd.ylmult = sum(ws .* [s.ylmult for s in childdata])
+
+ nd.Q = sum(s.Q for s in childdata) +
+ nd.ylmult' * nodet * pA^2 * (I + nodet * pA)^(-1) * C1inv *
+ nd.xlmult
+ nd.xx = sum(s.xx for s in childdata) +
+ nd.xlmult' * (nodet * pA^2) * (I + nodet * pA)^(-1) *
+ C1inv * nd.xlmult
+ nd.yy = sum(s.yy for s in childdata) +
+ nd.ylmult' * nodet * pA^2 * (I + nodet * pA)^(-1) * C1inv *
+ nd.ylmult
+ end
+ # @assert getnodedata(tree, node) === nd
+ end
+
+ return tree
+end
+
+function tooptimisemultlambda(lambda::Vector{Float64}, C::Matrix{Float64},
+ tree::T, nodes::Vector{N}, trait::Vector{String},
+ n::Int) where
+ {TT, RT, NL, N <: AbstractElt{RT, NL}, B <: AbstractElt{RT, NL},
+ T <: AbstractTree{TT, RT, NL, N, B}}
+ # lambda - value for Signal
+ # C - evolutionary rates and covariances matrix
+ # tree - tree
+ # nodes - nodes of the tree in traversal order
+ # t - vector of og branch lengths
+ # trait - string of what trait is called on the tree
+ # N - total number of nodes
+ # n - number of leaves
+
+ threepointmultlambda!(tree, trait, nodes, C, lambda)
+ # print after each call to double check runninng
+ print('.')
+
+ # information from last node
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ betahat = inv(nd.xx) * nd.Q
+
+ k = length(trait)
+
+ negloglik = (1.0 / 2.0) * (n * k * log(2π) + nd.logV + n +
+ log(abs(det((nd.yy - 2 * betahat * nd.Q' +
+ betahat * nd.xx * betahat')))))
+ return negloglik
+end
+
+function estimaterates!(tree::T, trait::Vector{String},
+ lambda::Vector{Float64}) where {T <: AbstractTree}
+
+ # gather information from tree in order to preform threepoint
+ nodes = getnodes(tree, postorder)
+ n = nleaves(tree)
+
+ # Get C by running algorithm w/o lambda
+ betahat, C, negloglik = estimaterates(tree, trait)
+ print(C)
+
+ t = [getnodedata(tree, node).t for node in nodes]
+
+ k = length(lambda)
+
+ # info for optimiser
+ lower = fill(floatmin(), k)
+
+ # upper is going to be largest lambda can be without going past the leaves
+ # want to find longest internal branch
+ intnodeheights = nodeheights(tree, noleaves = true)
+ longnodeheight = maximum(intnodeheights)
+
+ leafnodeheights = nodeheights(tree, onlyleaves = true)
+ shortleafheight = minimum(leafnodeheights)
+
+ upper = fill(shortleafheight / longnodeheight, k)
+
+ # lowest value of C, diagonals cant be lower than zero, off diagonals dont have restriction
+ lowerC = fill(-Inf, k, k)
+
+ for i in 1:k
+ lowerC[i, i] = 0
+ end
+
+ # upper value of C, no restirction
+ upperC = fill(Inf, k, k)
+
+ for i in 1:3
+ # optimise to find lambda
+ optslambda = optimize(x -> tooptimisemultlambda(x, C, tree, nodes,
+ trait, n),
+ lower, upper, lambda, Fminbox(LBFGS()),
+ Optim.Options(time_limit = 30))
+ # get lambda value
+ lambda = Optim.minimizer(optslambda)
+ # print to ensure its running and see how it's going
+ print(lambda)
+
+ # optimise to find C
+ optsC = optimize(x -> tooptimisemultlambda(lambda, x, tree, nodes,
+ trait, n),
+ lowerC, upperC, C, Fminbox(LBFGS()),
+ Optim.Options(time_limit = 30))
+ # get C value
+ C = Optim.minimizer(optsC)
+ end
+
+ # information from last node
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ betahat = inv(nd.xx) * nd.Q
+
+ k = length(trait)
+
+ negloglik = (1.0 / 2.0) * (n * k * log(2π) + nd.logV + n +
+ log(abs(det((nd.yy - 2 * betahat * nd.Q' +
+ betahat * nd.xx * betahat')))))
+
+ return betahat, C, negloglik, lambda
+end
+
+# used for Bayes calculations (may move parts to other places)
+
+# define loglikelihood function, used in Bayes methods
+function loglik(n, nd, sigma, beta)
+ return -(1.0 / 2.0) * (n * log(2π) + nd.logV + n * log(abs(sigma)) +
+ abs(sigma)^(-1) * (nd.yy[] - 2 * nd.Q[] * beta + nd.xx * beta^2))
+end
+
+# Need to create own distribution to use threepoint to calculate likelihood
+# also needs renamed
+mutable struct MyDist2{T <: AbstractTree, N <: Number} <:
+ ContinuousMultivariateDistribution
+ sigma::N
+ beta::N
+ tree::T
+end
+
+eltype(::MD) where {T, N <: Number, MD <: MyDist2{T, N}} = N
+eltype(::Type{MD}) where {T, N <: Number, MD <: MyDist2{T, N}} = N
+
+# rand creates a vector of tip trait values dependent on the tree, sigma (rate of evolution) and beta (root trait value)
+function Distributions.rand(rng::AbstractRNG, d::MyDist2)
+ a = BrownianTrait(d.tree, "BMtrait", σ² = d.sigma)
+ bm_traits = rand(a)
+ z = [bm_traits[leaf] for leaf in getleafnames(d.tree, postorder)]
+ return z
+end
+
+# define logpdf for my dist
+function Distributions.logpdf(d::MyDist2, z::Vector{Float64})
+ # add errors for if tree doesnt have right data
+
+ n = nleaves(d.tree)
+ nodes = getnodes(d.tree, postorder)
+ trait = getnodedata(d.tree, nodes[1]).name
+
+ threepoint!(d.tree, trait, nodes)
+
+ nN = last(nodes)
+ nd = getnodedata(d.tree, nN)
+
+ return loglik(n, nd, d.sigma, d.beta)
+end
+
+# Bayes threepoint signal
+# needs renamed
+struct MyDist3{T <: AbstractTree, N <: Number} <:
+ ContinuousMultivariateDistribution
+ sigma::N
+ beta::N
+ lambda::N
+ tree::T
+end
+
+function eltype(::MD) where {T <: AbstractTree, N <: Number, MD <:
+ MyDist3{T, N}}
+ return eltype(nodedatatype(T))
+end
+function eltype(::Type{MD}) where {T <: AbstractTree, N <: Number,
+ MD <: MyDist3{T, N}}
+ return eltype(nodedatatype(T))
+end
+
+# rand creates a vector of tip trait values dependent on the tree, sigma (rate of evolution) and beta (root trait value)
+function Distributions.rand(rng::AbstractRNG, d::MyDist3) # incorrect but can fix later
+ a = BrownianTrait(d.tree, "BMtrait", σ² = d.sigma)
+ bm_traits = rand(rng, a)
+
+ z = [bm_traits[leaf] for leaf in getleafnames(d.tree, postorder)]
+ return z
+end
+
+# define logpdf for my dist
+function Distributions.logpdf(d::MD, z::Vector{Float64}) where {MD <: MyDist3}
+ # add errors for if tree doesnt have right data
+
+ n = nleaves(d.tree)
+ nodes = getnodes(d.tree, postorder)
+ trait = getnodedata(d.tree, nodes[1]).name
+
+ # multiply internal branches by lambda
+ for node in nodes
+ if isleaf(d.tree, node)
+ getnodedata(d.tree, node).t = d.lambda *
+ getlength(d.tree,
+ getinbound(d.tree, node)) +
+ (1.0 - d.lambda) *
+ heighttoroot(d.tree, node)
+ elseif isroot(d.tree, node)
+ getnodedata(d.tree, node).t = zero(d.lambda)
+ else
+ getnodedata(d.tree, node).t = d.lambda *
+ getlength(d.tree,
+ getinbound(d.tree, node))
+ end
+ end
+
+ threepoint!(d.tree, trait, nodes)
+
+ nN = last(nodes)
+ nd = getnodedata(d.tree, nN)
+
+ return loglik(n, nd, d.sigma, d.beta)
+end
+
+#= Need to use method at then of three point paper
+mutable struct MyDistMult{T <: AbstractTree, M <: Matrix, V <: Vector} <:
+ ContinuousMultivariateDistribution
+ sigma::M
+ beta::V
+ tree::T
+end
+
+eltype(::MD) where {T, N <: Number, MD <: MyDistMult{T, N}} = N
+eltype(::Type{MD}) where {T, N <: Number, MD <: MyDistMult{T, N}} = N
+
+# rand creates a vector of tip trait values dependent on the tree, sigma (rate of evolution) and beta (root trait value)
+function Distributions.rand(rng::AbstractRNG, d::MyDistMult) #need to think about will depend on the number of traits
+ a = BrownianTrait(d.tree, "BMtrait", σ² = d.sigma)
+ bm_traits = rand(a)
+ z = [bm_traits[leaf] for leaf in getleafnames(d.tree, postorder)]
+ return z
+end
+
+function loglik2(n, nd, sigma, beta)
+ return -(1.0 / 2.0) * (n * log(2π) + nd.logV + n * log(abs(sigma)) +
+ abs(sigma)^(-1) * (nd.yy[] - 2 * nd.Q[] * beta + nd.xx * beta^2))
+end
+
+# define logpdf for my dist
+function Distributions.logpdf(d::MyDistMult, z::Vector{Float64})
+ # add errors for if tree doesnt have right data
+
+ n = nleaves(d.tree)
+ nodes = getnodes(d.tree, postorder)
+ trait = getnodedata(d.tree, nodes[1]).name
+
+ threepoint!(d.tree, trait, nodes)
+
+ nN = last(nodes)
+ nd = getnodedata(d.tree, nN)
+
+ return loglik2(n, nd, d.sigma, d.beta)
+end
+
+=#
+# PIC calculations, ignore for now
+# ## ## ## ## ## ## ## ## ## ## ## ## ## ## #
+#=
+function pic!(tree::T, trait::Vector{String}, nodes::Vector{N}, lambda::Vector{Float64}) where
+ {TT, RT, NL, N <: AbstractElt{RT, NL}, B <: AbstractElt{RT, NL},
+ T <: AbstractTree{TT, RT, NL, N, B}}
+ # prefrom algortihm in Ho & Ane 2014
+ # function estimaterates gets inputs into right form
+ # nodes - vector of nodes in the traversal order
+
+ # number of internal nodes
+ internal = nleaves(tree)
+
+ # number or traits
+ k = length(trait)
+
+ # get length of whole tree
+ full = getheight(tree, node)
+
+ # create empty vectors, u and V to store things in for later
+ U = fill(fill(NaN, k), internal)
+ V = fill(fill(NaN, k), internal)
+
+ # use i to count which elements of u and V to be updated
+ i=1
+
+ for node in nodes
+ nd = getnodedata(tree, node)
+ nodet = nd.t
+
+ # need to see if node is a tip (leaf)
+ if isleaf(tree, node)
+
+ # for Freckleton likelihood calculations
+ nd.v = nodet * ones(k) .+ full * ones(k) .* (ones(k) .- lambda)
+
+ else
+ # need to find direct desendents
+ children = getchildren(tree, node)
+
+ # child data
+ childdata = getnodedata.(tree, children)
+
+ # for Freckleton likelihood calculations
+ # assuming two children
+ child1value = childdata[1].value
+ child2value = childdata[2].value
+ child1v = childdata[1].v
+ child2v = childdata[2].v
+
+ U[i] = abs.(child1value - child2value)
+ V[i] = child1v .+ child2v
+
+ nd.value = ((child1value ./ child1v) .+ (child2value ./ child2v)) ./ ((1 / child1v) .+ (1 / child2v))
+ nd.v = nodet * ones(k) .* lambda .+ (child1v .* child2v) ./ (child1v .+ child2v)
+
+ i=i+1
+ end
+ # @assert getnodedata(tree, node) === nd
+ end
+end
+
+ function estimateratespic!(tree::T, trait::Vector{String}, lambda) where T <: AbstractTree
+
+ # get information from tree in order to preform threepoint
+ nodes = getnodes(tree, postorder)
+ n = nleaves(tree)
+
+ a = threepointflambda!(tree, trait, nodes)
+
+ tree = a[1]
+ U = a[2]
+ V = a[3]
+
+ # information from last node
+ nN = last(nodes)
+ nd = getnodedata(tree, nN)
+
+ # Freckleton calculations
+ k = length(trait)
+
+ UU = fill(fill(NaN, k, k), n)
+
+ for i in 1:n
+ UU[i] = U[i] * U[i]' / V[i]
+ end
+
+ sigma2 = 1/(n) * sum(UU) # I have wrong off diagonals - try and use R code?
+
+ UCU = fill(NaN, n)
+
+ for i in 1:n
+ UCU[i] = ((V[i] .* U[i])' * sigma2^(-1) * (V[i] .* U[i]))
+ end
+
+ nll2 = (1.0 / 2.0) * (n * k * log(2π) + n * log(det(sigma2)) + sum(log.(V)) + sum(UCU))
+
+ beta = nd.value
+
+ return lambda, beta, sigma2, nll2 # only return lambda if used
+ end
+
+ function estimateratespic(tree::T, trait::Vector{String}; lambda = missing) where T <: AbstractTree
+ # Returns evolution rate, starting value and negative log loglikelihood for traits on tip of tree
+ # INPUTS
+ # tree = tree with lengths, leaves all same length, trait data on leaves
+ # trait = string with name of trait as found on leaves
+ # OUTPUTS
+ # sigmahat - evolution rate
+ # betahat - estimated root trait value
+ # negloglik - negative loglikelihood
+
+ # need to add meaningful error when cant find traits on tree leaves
+
+ nodes = getnodes(tree, anyorder)
+ NTraits = length(trait)
+
+ for node in nodes
+ val = getnodedata(tree, node).value
+ if hasinbound(tree, node)
+ len = _getlength(tree, _getinbound(tree, node))
+ td = traitdata(trait, val, len)
+ setnodedata!(tree, node, td)
+ else
+ td = traitdata(trait, val)
+ setnodedata!(tree, node, td)
+ end
+ end
+
+ if lambda == missing
+ lambda = ones(NTraits)
+ end
+
+ return estimateratesflambda!(tree, trait, lambda)
+ end
+
+=#
diff --git a/src/newick.jl b/src/newick.jl
index 94ce2a70..162bb0e8 100644
--- a/src/newick.jl
+++ b/src/newick.jl
@@ -2,7 +2,7 @@
#=
# Tree → Subtree ";"
-# Subtree → Leaf | Internal
+# Subtree → Leaf | Internal
# Leaf → Name
# Internal → "(" BranchSet ")" Name_or_Support
# BranchSet → Branch | Branch "," BranchSet
diff --git a/src/trim.jl b/src/trim.jl
index e7a70dbe..d95fc787 100644
--- a/src/trim.jl
+++ b/src/trim.jl
@@ -13,24 +13,30 @@ include tips or root.
getinternalnodes(t::AbstractTree) = collect(nodenamefilter(isinternal, t))
"""
- droptips!(tree::AbstractTree{OneTree}, tips)
+ droptips!(tree::AbstractTree{OneTree}, tips; keep = false)
Function to drop tips from a phylogenetic tree `tree`, which are found in
-the vector of tips or tip names, `tips`.
+the vector of tips or tip names, `tips`. `keep` determines whether to keep
+internal and root nodes that now only have one child (default is `false`).
+Internal nodes with no children will always be removed.
"""
function droptips! end
@traitfn function droptips!(tree::T,
- tips::AbstractVector{N}) where
- {N, RT,
- T <: AbstractTree{OneTree, RT, N}; !MatchNodeType{T, N}}
- return droptips!(tree, [getnode(tree, tip) for tip in tips])
+ tips::AbstractVector{N};
+ keep::Bool = false) where
+ {N, RT, NL,
+ T <: AbstractTree{OneTree, RT, NL}; !MatchNodeType{T, N}}
+ return isempty(tips) ? NL[] :
+ droptips!(tree, [getnode(tree, tip) for tip in tips], keep = keep)
end
@traitfn function droptips!(tree::T,
- tips::AbstractVector{N}) where
- {N, RT, T <: AbstractTree{OneTree, RT}; MatchNodeType{T, N}}
- keep_tips = [tip for tip in getleaves(tree) if tip ∉ tips]
- tipnames = [getnodename(tree, tip) for tip in tips]
+ tips::AbstractVector{N};
+ keep::Bool = false) where
+ {N, RT, NL,
+ T <: AbstractTree{OneTree, RT, NL}; MatchNodeType{T, N}}
+ keep_tips = N[tip for tip in getleaves(tree) if tip ∉ tips]
+ tipnames = NL[getnodename(tree, tip) for tip in tips]
# Remove nodes that are not in tip names
for tip in tips
@@ -43,26 +49,28 @@ end
map(x -> deletenode!(tree, x), nodes)
end
- # Merge internal nodes that no longer have multiple children
- while any(map(x -> length(getchildren(tree, x)) .< 2,
- getinternalnodes(tree)))
- inner_nodes = getinternalnodes(tree)
- remove_nodes = findall(map(x -> length(getchildren(tree, x)) .< 2,
- inner_nodes))
- for i in remove_nodes
- parent = getparent(tree, inner_nodes[i])
- parentbranch = getinbound(tree, inner_nodes[i])
+ if !keep
+ # Merge internal nodes that no longer have multiple children
+ while any(map(x -> length(getchildren(tree, x)) .< 2,
+ getinternalnodes(tree)))
+ inner_nodes = getinternalnodes(tree)
+ remove_nodes = findall(map(x -> length(getchildren(tree, x)) .< 2,
+ inner_nodes))
+ for i in remove_nodes
+ parent = getparent(tree, inner_nodes[i])
+ parentbranch = getinbound(tree, inner_nodes[i])
- child = getchildren(tree, inner_nodes[i])[1]
- childbranch = getoutbounds(tree, getnode(tree, inner_nodes[i]))[1]
+ child = getchildren(tree, inner_nodes[i])[1]
+ childbranch = getoutbounds(tree, getnode(tree, inner_nodes[i]))[1]
- len = distance(tree, parent, child)
+ len = distance(tree, parent, child)
- deletebranch!(tree, parentbranch)
- deletebranch!(tree, childbranch)
- deletenode!(tree, inner_nodes[i])
+ deletebranch!(tree, parentbranch)
+ deletebranch!(tree, childbranch)
+ deletenode!(tree, inner_nodes[i])
- createbranch!(tree, parent, child, len)
+ createbranch!(tree, parent, child, len)
+ end
end
end
@@ -78,26 +86,31 @@ end
getiterator(getleafinfo(tree))))
setleafinfo!(tree, li)
end
+
return tipnames
end
"""
- keeptips!(tree::AbstractTree{OneTree}, tips)
+ keeptips!(tree::AbstractTree{OneTree}, tips; keep = false)
Function to keep only the tips in a phylogenetic tree, `tree`, that are found in
-the vector of tips or tip names, `tips`.
+the vector of tips or tip names, `tips`. `keep` determines whether to keep
+internal and root nodes that now only have one child (default is `false`).
+Internal nodes with no children will always be removed.
"""
function keeptips! end
@traitfn function keeptips!(tree::T,
- tips::AbstractVector{N}) where
+ tips::AbstractVector{N};
+ keep::Bool = false) where
{N, RT,
- T <: AbstractTree{OneTree, RT, N}; !MatchNodeType{T, N}}
- return keeptips!(tree, [getnode(tree, tip) for tip in tips])
+ T <: AbstractTree{OneTree, RT}; !MatchNodeType{T, N}}
+ return keeptips!(tree, [getnode(tree, tip) for tip in tips], keep = keep)
end
@traitfn function keeptips!(tree::T,
- tips::AbstractVector{N}) where
+ tips::AbstractVector{N};
+ keep::Bool = false) where
{N, RT, T <: AbstractTree{OneTree, RT}; MatchNodeType{T, N}}
- drop_tips = [tip for tip in getleaves(tree) if tip ∉ tips]
- return droptips!(tree, drop_tips)
+ drop_tips = N[tip for tip in getleaves(tree) if tip ∉ tips]
+ return droptips!(tree, drop_tips, keep = keep)
end
diff --git a/test/run_inference.jl b/test/run_inference.jl
new file mode 100644
index 00000000..31d1fdd4
--- /dev/null
+++ b/test/run_inference.jl
@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+module TestInference_wrapped
+
+using Test
+using Phylo
+using RCall
+using DataFrames
+using DynamicPPL
+using ForwardDiff
+
+global skipR = !(rcopy(R"require(ape)") && rcopy(R"require(phylolm)"))
+
+@testset "Compare estimaterates output to phylolm" begin
+ jtree = open(f -> parsenewick(f, TraitTree{1}),
+ Phylo.path("hummingbirds.tree"))
+
+ # Create dataframe with leafnames and random trait values
+ species = getleafnames(jtree)
+ data = 1000 .* rand(length(species))
+ dat = DataFrame(species = species, data = data)
+
+ # Save data on leaves so can test on estimaterates 2
+ traits = Phylo.traitdata.(Union{Float64,
+ ForwardDiff.Dual{ForwardDiff.Tag{DynamicPPL.DynamicPPLTag,
+ Float64},
+ Float64, 3}}, Ref("trait"),
+ dat.data)
+ setnodedata!.(jtree, dat.species, traits)
+
+ jfit = nothing
+ @test_nowarn jfit = estimaterates(jtree, ["trait"])
+
+ if !skipR
+ rtree = rcall(Symbol("read.tree"), Phylo.path("hummingbirds.tree"))
+ @rput rtree
+ @rput dat
+
+ # Species need to be the row names in R
+ R"row.names(dat) <- dat$species"
+
+ rfit = rcopy(R"phylolm(data ~ 1, data = dat, phy = rtree)")
+
+ @test rfit[:coefficients] ≈ jfit[1][1]
+ @test rfit[:sigma2] ≈ jfit[2][1]
+ @test rfit[:logLik] ≈ -jfit[3][1]
+ end
+end
+
+end
diff --git a/test/run_rcall.jl b/test/run_rcall.jl
index 5a9a17d3..39132fdd 100644
--- a/test/run_rcall.jl
+++ b/test/run_rcall.jl
@@ -7,7 +7,11 @@ using RCall
using DataFrames
global skipR = !rcopy(R"require(ape)")
-
+if skipR
+ @warn "Skipping RCall tests, no ape package found"
+else
+ @info "Running RCall tests"
+end
# Run tree comparisons on increasing numbers of tips
@testset "RCall - testing Phylo vs ape" begin
@testset "For $TreeType" for TreeType in (skipR ? [] :
diff --git a/test/test_RecursiveTree.jl b/test/test_RecursiveTree.jl
index f2719714..631b24d9 100644
--- a/test/test_RecursiveTree.jl
+++ b/test/test_RecursiveTree.jl
@@ -126,23 +126,23 @@ end
tree = rand(Nonultrametric(species))
@test validate!(tree)
- # Missing node input
+ # Missing node input
tree = rand(Nonultrametric(species))
getnode(tree, species[1]).in = nothing
@test !validate!(tree)
- # Missing node exit
+ # Missing node exit
tree = rand(Nonultrametric(species))
empty!(getnode(tree, species[1]).in.in.conns)
@test !validate!(tree)
- # Missing branch input
+ # Missing branch input
tree = rand(Nonultrametric(species))
b = getinbound(tree, species[1])
b.in = nothing
@test !validate!(tree)
- # Missing branch exit
+ # Missing branch exit
tree = rand(Nonultrametric(species))
b = getinbound(tree, species[1])
empty!(b.conns)
diff --git a/test/test_inference.jl b/test/test_inference.jl
new file mode 100644
index 00000000..8a466e1e
--- /dev/null
+++ b/test/test_inference.jl
@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: BSD-2-Clause
+
+module TestInference
+
+# Environment variable to avoid boring R package builds
+mustCrossvalidate = haskey(ENV, "JULIA_MUST_CROSSVALIDATE") &&
+ ENV["JULIA_MUST_CROSSVALIDATE"] == "1"
+
+# Only run R on unix or when R is installed because JULIA_MUST_CROSSVALIDATE is set to 1
+global skipR = !(mustCrossvalidate || Sys.isunix())
+try
+ skipR && error("Not on unix...")
+ using RCall
+ global skipR = false
+catch
+ global skipR = true
+ if mustCrossvalidate
+ error("R not installed, JULIA_MUST_CROSSVALIDATE set")
+ else
+ @warn "R not installed, skipping R cross-validation."
+ end
+end
+
+!skipR && include("run_inference.jl")
+
+end
diff --git a/test/test_trim.jl b/test/test_trim.jl
index 3b17c5db..0bef0eab 100644
--- a/test/test_trim.jl
+++ b/test/test_trim.jl
@@ -35,10 +35,18 @@ end
@test Set(tips) == Set(tips2)
@test validate!(test_tree)
@test validate!(test_tree2)
+ @test isempty(droptips!(test_tree2, nodetype(typeof(test_tree2))[]))
+ @test isempty(droptips!(test_tree, String[]))
tdf = rand(Ultrametric(df))
@test ["Dog"] == droptips!(tdf, ["Dog"])
@test length(getiterator(getleafinfo(tdf))) == 2
+
+ test_keep = rand(Ultrametric(10))
+ droptips!(test_keep, ["tip 1"], keep = true)
+ test_dont = rand(Ultrametric(10))
+ droptips!(test_dont, ["tip 1"])
+ @test nnodes(test_keep) == nnodes(test_dont) + 1
end
end