knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
library(eve)
The package eve
is an evolution emulator which provides pipelines to
do phylogenetic-diversity-dependent simulation, analyse outputs and
generate publication-ready plots and statistics conveniently.
eve
supports mainly three different scenarios, the species diversification process is regulated by phylogenetic diversity (PD) or evolutionary distinctiveness (ED) or nearest neighbor distance (NND) respectively.
eve
supports parallel computing , this feature is implemented with furrr, read through its documentation before using this feature. Note that parralel computing may use a huge amount of memory and may have large overhead.
You can install the developmental version from GitHub by running the following commands in R console:
# install.packages("remotes") remotes::install_github("EvoLandEco/eve")
library(eve) # make a combo of parameter sets combo <- edd_combo_maker( la = c(0.5, 0.8), mu = c(0.1, 0.2), beta_n = c(-0.001, 0), beta_phi = c(-0.001, 0.001), gamma_n = c(-0.001, 0.001), gamma_phi = c(-0.001, 0.001), age = 5, model = "dsde2", metric = c("ed"), offset = "none" ) # have a look at the combo combo[1:3]
All the unique and possible combinations of parameters will be created automatically.
eve
supports parallel simulation, the following example shows how to do an 8-session parallel EDD simulation given the combo, with 3 replications for each parameter set . The result will be saved to /result/example
. If name
is not specified, a folder will be created according to time and date at the moment.
# result will be save as .RData file # edd_go( # combo = combo, # nrep = 3, # name = "example", # strategy = future::multisession, # workers = 8 # )
edd_go
will not return any object unless you set name = "no_save"
.
# set name = "no_save" if you want to assign the output to a variable # output <- edd_go( # combo = combo, # nrep = 3, # name = "no_save", # strategy = future::multisession, # workers = 8 # ) # examine the result # output[[1]]
Sequential simulation is also possible:
# edd <- edd_go( # combo = combo, # nrep = 3, # name = "example2", # strategy = future::sequential, # workers = 8 # )
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