This function will execute a SLiM script generated by the compile function during the compilation of a slendr demographic model.

slim(
  model,
  sequence_length,
  recombination_rate,
  samples = NULL,
  output = NULL,
  burnin = 0,
  max_attempts = 1,
  spatial = !is.null(model$world),
  coalescent_only = TRUE,
  method = c("batch", "gui"),
  random_seed = NULL,
  verbose = FALSE,
  load = TRUE,
  locations = NULL,
  slim_path = NULL
)

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 to TRUE (the default) or FALSE? 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)

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 with animate_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)

Examples

check_dependencies(python = TRUE, slim = TRUE) # make sure dependencies are present

# 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     │320.6 KiB║
#> ╚═══════════════╧═════════╝
#> ╔═══════════╤════╤═════════╤════════════╗
#> ║Table      │Rows│Size     │Has Metadata║
#> ╠═══════════╪════╪═════════╪════════════╣
#> ║Edges      │1925│ 60.2 KiB│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Individuals│1348│133.4 KiB│         Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Migrations │   0│  8 Bytes│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Mutations  │   0│  1.2 KiB│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Nodes      │1926│ 72.1 KiB│         Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Populations│   4│  2.5 KiB│         Yes║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Provenances│   1│ 33.5 KiB│          No║
#> ╟───────────┼────┼─────────┼────────────╢
#> ║Sites      │   0│ 16 Bytes│          No║
#> ╚═══════════╧════╧═════════╧════════════╝
#>