This function will execute a SLiM script generated by the compile
function during the compilation of a slendr demographic model.
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.- burnin
Length of the burnin (in model's time units, i.e. years)
- max_attempts
How many attempts should be made to place an offspring near one of its parents? Serves to prevent infinite loops on the SLiM backend. Default value is 1.
- spatial
Should the model be executed in spatial mode? By default, if a world map was specified during model definition, simulation will proceed in a spatial mode.
- coalescent_only
Should
initializeTreeSeq(retainCoalescentOnly = <...>)
be set toTRUE
(the default) orFALSE
? See "retainCoalescentOnly" in the SLiM manual for more detail.- method
How to run the script? ("gui" - open in SLiMgui, "batch" - run on the command line)
- random_seed
Random seed (if missing, SLiM's own seed will be used)
- run
Should the SLiM engine be run? If
FALSE
, the command line SLiM command will be printed (and returned invisibly as a character vector) but not executed.- verbose
Write the SLiM output log to the console (default
FALSE
)?- 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.- locations
If
NULL
, locations are not saved. Otherwise, the path to the file where locations of each individual throughout the simulation will be saved (most likely for use withanimate_model
).- slim_path
Optional way to specify path to an appropriate SLiM binary (this is useful if the
slim
binary is not on the$PATH
).
Value
A tree-sequence object loaded via Python-R reticulate interface function ts_load
(internally represented by the Python object tskit.trees.TreeSequence
). Optionally,
depending on the value of the arguments load =
or run =
, nothing or a character
vector, respectively.
Examples
check_dependencies(python = TRUE, slim = TRUE, quit = TRUE) # dependencies must be present
init_env()
#> The interface to all required Python modules has been activated.
# load an example model
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))
# afr and eur objects would normally be created before slendr model compilation,
# but here we take them out of the model object already compiled for this
# example (in a standard slendr simulation pipeline, this wouldn't be necessary)
afr <- model$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, 5), list(eur, 5), list(chimp, 1))
ancient_samples <- schedule_sampling(model, times = c(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 SLiM back end from a compiled slendr model object and return
# a tree-sequence output
ts <- slim(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")
slim(model, sequence_length = 1e5, recombination_rate = 0, samples = samples,
output = output_file, load = FALSE)
# ... at a later stage:
ts <- ts_load(output_file, model)
ts
#> ╔═════════════════════════╗
#> ║TreeSequence ║
#> ╠═══════════════╤═════════╣
#> ║Trees │ 1║
#> ╟───────────────┼─────────╢
#> ║Sequence Length│ 100000║
#> ╟───────────────┼─────────╢
#> ║Time Units │ ticks║
#> ╟───────────────┼─────────╢
#> ║Sample Nodes │ 1046║
#> ╟───────────────┼─────────╢
#> ║Total Size │320.3 KiB║
#> ╚═══════════════╧═════════╝
#> ╔═══════════╤════╤═════════╤════════════╗
#> ║Table │Rows│Size │Has Metadata║
#> ╠═══════════╪════╪═════════╪════════════╣
#> ║Edges │1916│ 59.9 KiB│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Individuals│1339│132.5 KiB│ Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Migrations │ 0│ 8 Bytes│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Mutations │ 0│ 1.2 KiB│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Nodes │1917│ 71.8 KiB│ Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Populations│ 4│ 2.5 KiB│ Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Provenances│ 1│ 34.6 KiB│ No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Sites │ 0│ 16 Bytes│ No║
#> ╚═══════════╧════╧═════════╧════════════╝
#>