Package that implements a joint model of trait evolution and biogeographic history described in: Quintero, I. and Landis, Michael J. Interdependent Phenotypic and Biogeographic Evolution Driven by Biotic Interactions. bioRxiv 560912 link.
- Julia v1.1.x
TapestreePackage installed along withRCall,OptimandProgressMeter. Install packages by typing]in the julia prompt and typingadd <package_name>. For example, for Tapestree:add Tapestree.- R installed
- R
apepackage installed.
-
Open Julia v1.1.x
-
Load Tapestree package:
using Tapestree- Specify the path to the phylogenetic tree (in a format that
apecan read):
finches_tree_file = "/directory_where_Tapestree_was_cloned/Tapestree/data/finches_rescaled.tre"- Specify data. Data should be a
.txtfile where each row is a species, first the species name that matches the tree tip labels, second the phenotypic data and then the species presence in each area (0if absent and1if present) . Openfinches_pca1.txtin the data folder to see an example.
finches_data_file = "/directory_where_Tapestree_was_cloned/Tapestree/data/finches_pca1.txt"- Specify output file (
homedir()is an alias to your home folder)
out_file = *(homedir(),"...")- Run the
tribe()(TRIBE: Trait and Range Interspecific Biogeographic Evolution) model:
tribe(finches_tree_file, finches_data_file, out_file)- Further options for
tribe()are
min_dt = 0.01 # a float describing the percentage of tree height allowed for discretization (lower values are more precise but take longer).
niter = 10_000 # an integer for the number of iterations.
nburn = 5_000 # an integer for the number of iterations in the adaptive burn-in phase.
nthin = 100 # an integer for the iteration sampling frequency.
saveXY = (true, 1_000) # a tuple of length 2: first is a boolean to save (or not) data augmented histories, second an integer for sampling frequency.
saveDM = (true, 1_000) # a tuple of length 2: first is a boolean to save (or not) data augmented deterministic effects, second an integer for sampling frequency.
ωxprior = (0.,10.) # a tuple of length 2 for the normal prior of ωx, first the mean, second the variance.
ω1prior = (0.,10.) # a tuple of length 2 for the normal prior of ω1, first the mean, second the variance.
ω0prior = (0.,10.) # a tuple of length 2 for the normal prior of ω0, first the mean, second the variance.
σ²prior = 1e-1 # a float for the mean of the exponential prior for σ².
λprior = 1e-1 # a float for the mean of the exponential prior for both λs.
weight = (0.15,0.05,0.02,0.02,5e-3) # a tuple of length 5 specifying the probabilities to update σ², ωx, ω1 & ω0, and λ1 & λ0 respectively.
λ1i = 1.0 # a float for the starting value for λ1.
λ0i = 0.5 # a float for the starting value for λ0.
ωxi = 0.0 # a float for the starting value for ωx.
ω1i = 0.0 # a float for the starting value for ω1.
ω0i = 0.0 # a float for the starting value for ω0.
fix_ωx = false # a boolean to make inference without ωx.
fix_ω1 = false # a boolean to make inference without ω1.
fix_ω0 = false # a boolean to make inference without ω0.- The output is a
.logfile with the results of the MCMC chain, and optionally (ifsaveXY = (true, k)), an R data file (.Rdata) with the augmented data histories. R code to manipulate and visualize this output are provided upon request.
- Specify the path to the phylogenetic tree (in a format that
apecan read):
finches_tree_file = "/directory_where_Tapestree_was_cloned/Tapestree/data/finches_rescaled.tre"- Perform simulation (here with 0.0 as the inital trait value and 6 areas on the finches tree)
x_init = 0.0
n_areas = 6
tip_values, tip_areas, tree, bts = simulate_tribe(x_init, n_areas, finches_tree_file)- Further options for
simulate_tribe()are
ωx = 0.0 # a float for simulated value of ωx.
σ² = 0.5 # a float for simulated value of σ².
ω1 = 0.0 # a float for simulated value of ω1.
ω0 = 0.0 # a float for simulated value of ω0.
λ1 = 0.5 # a float for simulated value of λ1.
λ0 = 0.2 # a float for simulated value of λ0.
const_δt = 1e-4 # a float for the delta t used to approximate the simulation (lower values are more accurate but at a slight computation cost).- Specify output file (
homedir()is an alias to your home folder)
out_file = *(homedir(),"...")- Run the
tribe()(optional parameters are the same as with inference):
tribe(tip_values, tip_areas, tree, bts, out_file)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent