Calculate the density of segregating sites for the given sets of individuals

Usage

ts_segregating(
  ts,
  sample_sets,
  mode = c("site", "branch", "node"),
  windows = NULL,
  span_normalise = FALSE
)

Arguments

ts: Tree sequence object of the class slendr_ts
sample_sets: 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.
mode: The mode for the calculation ("sites" or "branch")
windows: Coordinates of breakpoints between windows. The first coordinate (0) and the last coordinate (equal to ts$sequence_length) are added automatically)
span_normalise: Divide the result by the span of the window? Default TRUE, see the tskit documentation for more detail.

Value

For each set of individuals either a single diversity value or a vector of diversity values (one for each window)

Examples

check_dependencies(python = TRUE, quit = TRUE) # dependencies must be 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_slim.trees", package = "slendr")
model <- read_model(path = system.file("extdata/models/introgression", package = "slendr"))

# load the tree-sequence object from disk
ts <- ts_read(slendr_ts, model) %>% ts_mutate(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          1
#> 2 CH           0
#> 3 EUR        138
#> 4 NEA          6