v1.0.0: transfer of functionalities for trait evolution from PhyloNetworks v0.16, with breaking changes and improvements #10

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Run julia-actions/julia-docdeploy@v1: src/fit_phylolm_continuous.jl#L1161

doctest failure in ~/work/PhyloTraits.jl/PhyloTraits.jl/src/fit_phylolm_continuous.jl:1161-1242 ```jldoctest descendence julia> net = readnewick("(A:2.5,((B:1,#H1:0.5::0.4):1,(C:1,(D:0.5)#H1:0.5::0.6):1):0.5);"); julia> preorder!(net) julia> using PhyloPlots julia> plot(net, shownodenumber=true); # to locate nodes julia> nodes_shifts = indexin([1,-5], [n.number for n in net.node]) # Put a shift on edges ending at nodes 1 and -5 2-element Vector{Union{Nothing, Int64}}: 1 7 julia> params = ParamsBM(10, 0.1, ShiftNet(net.node[nodes_shifts], [3.0, -3.0], net)) ParamsBM: Parameters of a BM with fixed root: mu: 10.0 Sigma2: 0.1 There are 2 shifts on the network: ────────────────────────── Edge Number Shift Value ────────────────────────── 8.0 -3.0 1.0 3.0 ────────────────────────── julia> using Random; Random.seed!(2468); # sets the seed for reproducibility julia> sim = rand(net, params); # simulate a dataset with shifts julia> using DataFrames # to handle data frames julia> dat = DataFrame(trait = sim[:tips], tipnames = sim.M.tipnames); julia> dat = DataFrame(trait = [13.391976856737717, 9.55741491696386, 7.17703734817448, 7.889062527849697], tipnames = ["A","B","C","D"]) # hard-coded, to be independent of random number generator 4×2 DataFrame Row │ trait tipnames │ Float64 String ─────┼──────────────────── 1 │ 13.392 A 2 │ 9.55741 B 3 │ 7.17704 C 4 │ 7.88906 D julia> dfr_shift = descendencedataframe(net, net.node[nodes_shifts]) # the regressors matching the shifts. 4×3 DataFrame Row │ shift_1 shift_8 tipnames │ Float64 Float64 String ─────┼──────────────────────────── 1 │ 1.0 0.0 A 2 │ 0.0 0.0 B 3 │ 0.0 1.0 C 4 │ 0.0 0.6 D julia> dfr = innerjoin(dat, dfr_shift, on=:tipnames); # join data and regressors in a single dataframe julia> using StatsModels # for statistical model formulas julia> fitBM = phylolm(@formula(trait ~ shift_1 + shift_8), dfr, net; reml=false) # actual fit PhyloNetworkLinearModel Formula: trait ~ 1 + shift_1 + shift_8 Model: Brownian motion Parameter Estimates, using ML: phylogenetic variance rate: 0.0112618 Coefficients: ──────────────────────────────────────────────────────────────────────── Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95% ──────────────────────────────────────────────────────────────────────── (Intercept) 9.48238 0.327089 28.99 0.0220 5.32632 13.6384 shift_1 3.9096 0.46862 8.34 0.0759 -2.04479 9.86399 shift_8 -2.4179 0.422825 -5.72 0.1102 -7.7904 2.95461 ──────────────────────────────────────────────────────────────────────── Log Likelihood: 1.8937302027 AIC: 4.2125395947 ``` Subexpression: using PhyloPlots Evaluated output: Precompiling PhyloPlots... Info Given PhyloPlots was explicitly requested, output will be shown live �[0K �[0K[ Info: No R installation found by RCall.jl. Precompilation of RCall and all dependent packages postponed. Importing RCall will fail until an R installation is configured beforehand. 3474.3 ms ? PhyloPlots [ Info: No R installation found by RCall.jl. Precompilation of RCall and all dependent packages postponed. Importing RCall will fail until an R installation is configured beforehand. [ Info: No R installation found by RCall.jl. Precompilation of RCall and all dependent packages postponed. Importing RCall will fail until an R installation is configured beforehand. [ Info: No R installation found by RCall.jl. Precompilation of RCall and all dependent packages postponed. Importing RCall will fail until an R installation is configured beforehand. ERROR: LoadError: InitError: No R installation was detected at RCall installation time. Please provided the location of R by setting the Rhome and libR preferences or else set R_HOME='*' and rerun Pkg.build("RCall") to use Conda.jl. Stacktrace: [1] e

Run julia-actions/julia-docdeploy@v1: src/fit_phylolm_continuous.jl#L1161

doctest failure in ~/work/PhyloTraits.jl/PhyloTraits.jl/src/fit_phylolm_continuous.jl:1161-1242 ```jldoctest descendence julia> net = readnewick("(A:2.5,((B:1,#H1:0.5::0.4):1,(C:1,(D:0.5)#H1:0.5::0.6):1):0.5);"); julia> preorder!(net) julia> using PhyloPlots julia> plot(net, shownodenumber=true); # to locate nodes julia> nodes_shifts = indexin([1,-5], [n.number for n in net.node]) # Put a shift on edges ending at nodes 1 and -5 2-element Vector{Union{Nothing, Int64}}: 1 7 julia> params = ParamsBM(10, 0.1, ShiftNet(net.node[nodes_shifts], [3.0, -3.0], net)) ParamsBM: Parameters of a BM with fixed root: mu: 10.0 Sigma2: 0.1 There are 2 shifts on the network: ────────────────────────── Edge Number Shift Value ────────────────────────── 8.0 -3.0 1.0 3.0 ────────────────────────── julia> using Random; Random.seed!(2468); # sets the seed for reproducibility julia> sim = rand(net, params); # simulate a dataset with shifts julia> using DataFrames # to handle data frames julia> dat = DataFrame(trait = sim[:tips], tipnames = sim.M.tipnames); julia> dat = DataFrame(trait = [13.391976856737717, 9.55741491696386, 7.17703734817448, 7.889062527849697], tipnames = ["A","B","C","D"]) # hard-coded, to be independent of random number generator 4×2 DataFrame Row │ trait tipnames │ Float64 String ─────┼──────────────────── 1 │ 13.392 A 2 │ 9.55741 B 3 │ 7.17704 C 4 │ 7.88906 D julia> dfr_shift = descendencedataframe(net, net.node[nodes_shifts]) # the regressors matching the shifts. 4×3 DataFrame Row │ shift_1 shift_8 tipnames │ Float64 Float64 String ─────┼──────────────────────────── 1 │ 1.0 0.0 A 2 │ 0.0 0.0 B 3 │ 0.0 1.0 C 4 │ 0.0 0.6 D julia> dfr = innerjoin(dat, dfr_shift, on=:tipnames); # join data and regressors in a single dataframe julia> using StatsModels # for statistical model formulas julia> fitBM = phylolm(@formula(trait ~ shift_1 + shift_8), dfr, net; reml=false) # actual fit PhyloNetworkLinearModel Formula: trait ~ 1 + shift_1 + shift_8 Model: Brownian motion Parameter Estimates, using ML: phylogenetic variance rate: 0.0112618 Coefficients: ──────────────────────────────────────────────────────────────────────── Coef. Std. Error t Pr(>|t|) Lower 95% Upper 95% ──────────────────────────────────────────────────────────────────────── (Intercept) 9.48238 0.327089 28.99 0.0220 5.32632 13.6384 shift_1 3.9096 0.46862 8.34 0.0759 -2.04479 9.86399 shift_8 -2.4179 0.422825 -5.72 0.1102 -7.7904 2.95461 ──────────────────────────────────────────────────────────────────────── Log Likelihood: 1.8937302027 AIC: 4.2125395947 ``` Subexpression: plot(net, shownodenumber=true); # to locate nodes Evaluated output: ERROR: UndefVarError: `plot` not defined in `Main` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] top-level scope @ none:1 Expected output: diff = Warning: Diff output requires color. ERROR: UndefVarError: `plot` not defined in `Main` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] top-level scope @ none:1

Run julia-actions/julia-docdeploy@v1: src/ancestral_continuous.jl#L344

doctest failure in ~/work/PhyloTraits.jl/PhyloTraits.jl/src/ancestral_continuous.jl:344-490 ```jldoctest; filter = [r" PhyloTraits .*:\d+", ] julia> using DataFrames, CSV # to read data file julia> phy = readnewick(joinpath(dirname(pathof(PhyloTraits)), "..", "examples", "carnivores_tree.txt")); julia> dat = CSV.read(joinpath(dirname(pathof(PhyloTraits)), "..", "examples", "carnivores_trait.txt"), DataFrame); julia> using StatsModels # for statistical model formulas julia> fitBM = phylolm(@formula(trait ~ 1), dat, phy); julia> ancStates = ancestralreconstruction(fitBM) # Should produce a warning, as variance is unknown. ┌ Warning: These prediction intervals show uncertainty in ancestral values, │ assuming that the estimated variance rate of evolution is correct. │ Additional uncertainty in the estimation of this variance rate is │ ignored, so prediction intervals should be larger. └ @ PhyloTraits ~/build/JuliaPhylo/PhyloTraits.jl/src/traits_continuous.jl:2601 ReconstructedStates: ─────────────────────────────────────────────── Node index Pred. Min. Max. (95%) ─────────────────────────────────────────────── -5.0 1.32139 -0.33824 2.98102 -8.0 1.03258 -0.589695 2.65485 -7.0 1.41575 -0.140705 2.97221 -6.0 1.39417 -0.107433 2.89577 -4.0 1.39961 -0.102501 2.90171 -3.0 1.51341 -0.220523 3.24733 -13.0 5.3192 3.92279 6.71561 -12.0 4.51176 2.89222 6.13131 -16.0 1.50947 -0.0186118 3.03755 -15.0 1.67425 0.196069 3.15242 -14.0 1.80309 0.309992 3.29618 -11.0 2.7351 1.17608 4.29412 -10.0 2.73217 1.12361 4.34073 -9.0 2.41132 0.603932 4.21871 -2.0 2.04138 -0.0340955 4.11686 14.0 1.64289 1.64289 1.64289 8.0 1.67724 1.67724 1.67724 5.0 0.331568 0.331568 0.331568 2.0 2.27395 2.27395 2.27395 4.0 0.275237 0.275237 0.275237 6.0 3.39094 3.39094 3.39094 13.0 0.355799 0.355799 0.355799 15.0 0.542565 0.542565 0.542565 7.0 0.773436 0.773436 0.773436 10.0 6.94985 6.94985 6.94985 11.0 4.78323 4.78323 4.78323 12.0 5.33016 5.33016 5.33016 1.0 -0.122604 -0.122604 -0.122604 16.0 0.73989 0.73989 0.73989 9.0 4.84236 4.84236 4.84236 3.0 1.0695 1.0695 1.0695 ─────────────────────────────────────────────── julia> using StatsBase # for predict function julia> predict(ancStates) 31×2 DataFrame Row │ nodenumber prediction │ Int64 Float64 ─────┼──────────────────────── 1 │ -5 1.32139 2 │ -8 1.03258 3 │ -7 1.41575 4 │ -6 1.39417 5 │ -4 1.39961 6 │ -3 1.51341 7 │ -13 5.3192 8 │ -12 4.51176 ⋮ │ ⋮ ⋮ 25 │ 10 6.94985 26 │ 11 4.78323 27 │ 12 5.33016 28 │ 1 -0.122604 29 │ 16 0.73989 30 │ 9 4.84236 31 │ 3 1.0695 16 rows omitted julia> predict(ancStates, interval = :prediction) 31×4 DataFrame Row │ nodenumber prediction lower upper │ Int64 Float64 Float64 Float64 ─────┼─────────────────────────────────────────────── 1 │ -5 1.32139 -0.33824 2.98102 2 │ -8 1.03258 -0.589695 2.65485 3 │ -7 1.41575 -0.140705 2.97221 4 │ -6 1.39417 -0.107433 2.89577 5 │ -4 1.39961 -0.102501 2.90171 6 │ -3 1.51341 -0.220523 3.24733 7 │ -13 5.3192 3.92279 6.71561 8 │ -12 4.51176 2.89222 6.13131 ⋮ │ ⋮ ⋮ ⋮ ⋮ 25 │ 10 6.94985 6.94985 6.94985 26 │ 11 4.7

Run julia-actions/julia-docdeploy@v1: src/ancestral_continuous.jl#L344

Run julia-actions/julia-docdeploy@v1: src/ancestral_posterior_discrete.jl#L139

doctest failure in ~/work/PhyloTraits.jl/PhyloTraits.jl/src/ancestral_posterior_discrete.jl:139-168 ```jldoctest julia> net = readnewick("(((A:2.0,(B:1.0)#H1:0.1::0.9):1.5,(C:0.6,#H1:1.0::0.1):1.0):0.5,D:2.0);"); julia> m1 = BinaryTraitSubstitutionModel([0.1, 0.1], ["lo", "hi"]); julia> using DataFrames julia> dat = DataFrame(species=["C","A","B","D"], trait=["hi","lo","lo","hi"]); julia> fit1 = fitdiscrete(net, m1, dat); julia> asr = ancestralreconstruction(fit1) 9×4 DataFrame Row │ nodenumber nodelabel lo hi │ Int64 String Float64 Float64 ─────┼─────────────────────────────────────────── 1 │ 1 A 1.0 0.0 2 │ 2 B 1.0 0.0 3 │ 3 C 0.0 1.0 4 │ 4 D 0.0 1.0 5 │ 5 5 0.286021 0.713979 6 │ 6 6 0.319456 0.680544 7 │ 7 7 0.16855 0.83145 8 │ 8 8 0.767359 0.232641 9 │ 9 H1 0.782776 0.217224 julia> using PhyloPlots julia> plot(fit1.net, nodelabel = asr[!,[:nodenumber, :lo]], tipoffset=0.2); # pp for "lo" state ``` Subexpression: plot(fit1.net, nodelabel = asr[!,[:nodenumber, :lo]], tipoffset=0.2); # pp for "lo" state Evaluated output: ERROR: UndefVarError: `plot` not defined in `Main` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] top-level scope @ none:1 Expected output: diff = Warning: Diff output requires color. ERROR: UndefVarError: `plot` not defined in `Main` Suggestion: check for spelling errors or missing imports. Stacktrace: [1] top-level scope @ none:1

Run julia-actions/julia-docdeploy@v1

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v1.0.0: transfer of functionalities for trait evolution from PhyloNetworks v0.16, with breaking changes and improvements #10

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