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


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



Model object created by the compile function


Total length of the simulated sequence (in base-pairs)


Recombination rate of the simulated sequence (in recombinations per basepair per generation)


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.


Path to the output tree-sequence file. If NULL (the default), tree sequence will be saved to a temporary file.


Length of the burnin (in model's time units, i.e. years)


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.


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.


Should initializeTreeSeq(retainCoalescentOnly = <...>) be set to TRUE (the default) or FALSE? See "retainCoalescentOnly" in the SLiM manual for more detail.


How to run the script? ("gui" - open in SLiMgui, "batch" - run on the command line)


Random seed (if missing, SLiM's own seed will be used)


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.


Write the SLiM output log to the console (default FALSE)?


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.


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).


Optional way to specify path to an appropriate SLiM binary (this is useful if the slim binary is not on the $PATH).


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.


check_dependencies(python = TRUE, slim = TRUE, quit = TRUE) # dependencies must be present

#> 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)

#> ╔═════════════════════════╗
#> ║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║
#> ╚═══════════╧════╧═════════╧════════════╝