Schedule sampling events at specified times and, optionally, a given set of locations on a landscape
Arguments
- model
Object of the class
slendr_model- times
Integer vector of times (in model time units) at which to schedule remembering of individuals in the tree-sequence
- ...
Lists of two elements (
slendr_poppopulation object-<number of individuals to sample), representing from which populations should how many individuals be remembered at times given bytimes- locations
List of vector pairs, defining two-dimensional coordinates of locations at which the closest number of individuals from given populations should be sampled. If
NULL(the default), individuals will be sampled randomly throughout their spatial boundary.- strict
Should any occurence of a population not being present at a given time result in an error? Default is
FALSE, meaning that invalid sampling times for any populations will be quietly ignored.
Value
Data frame with three columns: time of sampling, population to sample from, how many individuals to sample
Details
If both times and locations are given, the the sampling will be scheduled on each specified location in each given time-point. Note that for the time-being, in the interest of simplicity, no sanity checks are performed on the locations given except the restriction that the sampling points must fall within the bounding box around the simulated world map. Other than that, slendr will simply instruct its SLiM backend script to sample individuals as close to the sampling points given as possible, regardless of whether those points lie within a population spatial boundary at that particular moment of time.
Optionally, a name of a single sample from a population can be given, which
will then replace the generic format of "\{population\}_\{number\}" name.
See example for more detail.
Examples
init_env()
#> The interface to all required Python modules has been activated.
# load an example model with an already simulated tree sequence
path <- system.file("extdata/models/introgression", package = "slendr")
model <- read_model(path)
# afr, eur, and nea 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"]]
nea <- model$populations[["NEA"]]
# schedule the recording of 10 African and 100 European individuals from a
# given model at 20 ky, 10 ky, 5ky ago and at present-day (time 0)
schedule_amh <- schedule_sampling(
model, times = c(20000, 10000, 5000, 0),
list(afr, 10), list(eur, 100)
)
# schedule the recording of the Vindija Neanderthal genome
schedule_nea <- schedule_sampling(model, times = 40000, list(nea, 1, "Vindija"))
# the result of `schedule_sampling` is a simple data frame (note that the locations
# of sampling locations have `NA` values because the model is non-spatial)
schedule <- rbind(schedule_amh, schedule_nea)
schedule
#> # A tibble: 9 × 8
#> time pop n name y_orig x_orig y x
#> <int> <chr> <int> <chr> <lgl> <lgl> <lgl> <lgl>
#> 1 0 AFR 10 NA NA NA NA NA
#> 2 0 EUR 100 NA NA NA NA NA
#> 3 5000 AFR 10 NA NA NA NA NA
#> 4 5000 EUR 100 NA NA NA NA NA
#> 5 10000 AFR 10 NA NA NA NA NA
#> 6 10000 EUR 100 NA NA NA NA NA
#> 7 20000 AFR 10 NA NA NA NA NA
#> 8 20000 EUR 100 NA NA NA NA NA
#> 9 40000 NEA 1 Vindija NA NA NA NA
# simulate a tree sequence
ts <- msprime(model, sequence_length = 1e6, recombination_rate = 1e-8, samples = schedule)
# inspect the recorded table of samples
ts_samples(ts)
#> # A tibble: 441 × 3
#> name time pop
#> <chr> <dbl> <chr>
#> 1 Vindija 40000 NEA
#> 2 AFR_1 20000 AFR
#> 3 AFR_2 20000 AFR
#> 4 AFR_3 20000 AFR
#> 5 AFR_4 20000 AFR
#> 6 AFR_5 20000 AFR
#> 7 AFR_6 20000 AFR
#> 8 AFR_7 20000 AFR
#> 9 AFR_8 20000 AFR
#> 10 AFR_9 20000 AFR
#> # ℹ 431 more rows