Calculate the density of segregating sites for the given sets of individualsSource:
Calculate the density of segregating sites for the given sets of individuals
ts_segregating( ts, sample_sets, mode = c("site", "branch", "node"), windows = NULL, span_normalise = FALSE )
Tree sequence object of the class
A list (optionally a named list) of character vectors with individual names (one vector per set). If a simple vector is provided, it will be interpreted as
as.list(sample_sets), meaning that a given statistic will be calculated for each individual separately.
The mode for the calculation ("sites" or "branch")
Coordinates of breakpoints between windows. The first coordinate (0) and the last coordinate (equal to
ts$sequence_length) are added automatically)
Divide the result by the span of the window? Default TRUE, see the tskit documentation for more detail.
For each set of individuals either a single diversity value or a vector of diversity values (one for each window)
check_dependencies(python = TRUE) # make sure dependencies are present init_env() #> The interface to all required Python modules has been activated. # load an example model with an already simulated tree sequence slendr_ts <- system.file("extdata/models/introgression.trees", package = "slendr") model <- read_model(path = system.file("extdata/models/introgression", package = "slendr")) # load the tree-sequence object from disk ts <- ts_load(slendr_ts, model, mutate = TRUE, mutation_rate = 1e-8, random_seed = 42) # collect sampled individuals from all populations in a list sample_sets <- ts_samples(ts) %>% split(., .$pop) %>% lapply(function(pop) pop$name) ts_segregating(ts, sample_sets) #> # A tibble: 4 × 2 #> set segsites #> <chr> <dbl> #> 1 AFR 0 #> 2 CH 0 #> 3 EUR 8 #> 4 NEA 3