This function will execute a built-in msprime script and run a compiled slendr demographic model.
Usage
msprime(
model,
sequence_length,
recombination_rate,
samples = NULL,
output = NULL,
random_seed = NULL,
load = TRUE,
verbose = FALSE,
debug = FALSE,
run = TRUE
)
Arguments
- model
Model object created by the
compile
function- sequence_length
Total length of the simulated sequence (in base-pairs)
- recombination_rate
Recombination rate of the simulated sequence (in recombinations per basepair per generation)
- samples
A data frame of times at which a given number of individuals should be remembered in the tree-sequence (see
schedule_sampling
for a function that can generate the sampling schedule in the correct format). If missing, only individuals present at the end of the simulation will be recorded in the tree-sequence output file.- output
Path to the output tree-sequence file. If
NULL
(the default), tree sequence will be saved to a temporary file.- random_seed
Random seed (if missing, SLiM's own seed will be used)
- load
Should the final tree sequence be immediately loaded and returned? Default is
TRUE
. The alternative (FALSE
) is useful when a tree-sequence file is written to a custom location to be loaded at a later point.- verbose
Write the output log to the console (default
FALSE
)?- debug
Write msprime's debug log to the console (default
FALSE
)?- run
Should the msprime engine be run? If
FALSE
, the command line msprime command will be printed (and returned invisibly as a character vector) but not executed.
Value
A tree-sequence object loaded via Python-R reticulate interface function ts_load
(internally represented by the Python object tskit.trees.TreeSequence
). Optionally,
depending on the value of the arguments load =
or run =
, nothing or a character
vector, respectively.
Examples
check_dependencies(python = TRUE) # make sure dependencies are present
init_env()
#> The interface to all required Python modules has been activated.
# load an example model
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))
# afr and eur objects would normally be created before slendr model compilation,
# but here we take them out of the model object already compiled for this
# example (in a standard slendr simulation pipeline, this wouldn't be necessary)
afr <- model$populations[["AFR"]]
eur <- model$populations[["EUR"]]
chimp <- model$populations[["CH"]]
# schedule the sampling of a couple of ancient and present-day individuals
# given model at 20 ky, 10 ky, 5ky ago and at present-day (time 0)
modern_samples <- schedule_sampling(model, times = 0, list(afr, 10), list(eur, 100), list(chimp, 1))
ancient_samples <- schedule_sampling(model, times = c(40000, 30000, 20000, 10000), list(eur, 1))
# sampling schedules are just data frames and can be merged easily
samples <- rbind(modern_samples, ancient_samples)
# run a simulation using the msprime back end from a compiled slendr model object
ts <- msprime(model, sequence_length = 1e5, recombination_rate = 0, samples = samples)
# automatic loading of a simulated output can be prevented by `load = FALSE`, which can be
# useful when a custom path to a tree-sequence output is given for later downstream analyses
output_file <- tempfile(fileext = ".trees")
msprime(model, sequence_length = 1e5, recombination_rate = 0, samples = samples,
output = output_file, load = FALSE, random_seed = 42)
# ... at a later stage:
ts <- ts_load(output_file, model)
summary(ts)
#> ╔═══════════════════════════╗
#> ║TreeSequence ║
#> ╠═══════════════╤═══════════╣
#> ║Trees │ 1║
#> ╟───────────────┼───────────╢
#> ║Sequence Length│ 100000║
#> ╟───────────────┼───────────╢
#> ║Time Units │generations║
#> ╟───────────────┼───────────╢
#> ║Sample Nodes │ 230║
#> ╟───────────────┼───────────╢
#> ║Total Size │ 38.4 KiB║
#> ╚═══════════════╧═══════════╝
#> ╔═══════════╤════╤═════════╤════════════╗
#> ║Table │Rows│Size │Has Metadata║
#> ╠═══════════╪════╪═════════╪════════════╣
#> ║Edges │ 458│ 14.3 KiB│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Individuals│ 115│ 3.2 KiB│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Migrations │ 0│ 8 Bytes│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Mutations │ 0│ 16 Bytes│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Nodes │ 459│ 12.6 KiB│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Populations│ 4│338 Bytes│ Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Provenances│ 1│ 2.9 KiB│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Sites │ 0│ 16 Bytes│ No║
#> ╚═══════════╧════╧═════════╧════════════╝
#>