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

Source:`R/tree-sequences.R`

`ts_segregating.Rd`

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_load(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
```