slim | R Documentation |
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,
ts = TRUE,
path = NULL,
random_seed = NULL,
method = c("batch", "gui"),
verbose = FALSE,
run = TRUE,
slim_path = NULL,
burnin = 0,
max_attempts = 1,
spatial = !is.null(model$world),
coalescent_only = TRUE,
locations = NULL
)
model |
Model object created by the |
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 |
ts |
Should a tree sequence be simulated from the model? |
path |
Path to the directory where simulation result files will be saved.
If |
random_seed |
Random seed (if |
method |
How to run the script? ("gui" - open in SLiMgui, "batch" - run on the command line) |
verbose |
Write the log information from the SLiM run to the console
(default |
run |
Should the SLiM engine be run? If |
slim_path |
Path to the appropriate SLiM binary (this is useful if the
|
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 |
locations |
If |
The arguments sequence_length
and recombination_rate
can be
omitted for slendr models utilizing customized initialization of genomic
architecture. In such cases, users may either provide hard-coded values
directly through SLiM's initializeGenomicElement()
and
initializeRecombinationRate()
functions or utilize slendr's
templating functionality provided by its substitute()
function.
When ts = TRUE
, the returning value of this function depends on whether
or not the path
argument was set. If the user did provide the path
where output files should be saved, the path is returned (invisibly). This is
mostly intended to support simulations of customized user models. If path
is not set by the user, it is assumed that a tree-sequence object is desired as
a sole return value of the function (when ts = TRUE
) and so it is automatically
loaded when simulation finishes, or (when ts = FALSE
) that only customized
files are to be produced by the simulation, in which the user will be loading such
files by themselves (and only the path is needed).
A tree-sequence object loaded via Python-R reticulate interface function ts_read
(internally represented by the Python object tskit.trees.TreeSequence
). If the
path
argument was set, specifying the directory where results should be saved,
the function will return this path as a single-element character vector.
init_env()
# 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 object as a result
ts <- slim(model, sequence_length = 1e5, recombination_rate = 0, samples = samples)
# simulated tree-sequence object can be saved to a file using ts_write()...
ts_file <- normalizePath(tempfile(fileext = ".trees"), winslash = "/", mustWork = FALSE)
ts_write(ts, ts_file)
# ... and, at a later point, loaded by ts_read()
ts <- ts_read(ts_file, model)
ts
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