`R/tree-sequences.R`

`ts_divergence.Rd`

Calculate pairwise divergence between sets of individuals

```
ts_divergence(
ts,
sample_sets,
mode = c("site", "branch", "node"),
windows = NULL,
span_normalise = TRUE
)
```

- 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)

- 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`

) do not have to be specified as they are added automatically.- span_normalise
Divide the result by the span of the window? Default TRUE, see the tskit documentation for more detail.

For each pairwise calculation, either a single divergence value or a vector of divergence values (one for each window)

```
check_dependencies(python = TRUE) # make sure dependencies are present
# 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)
# compute the divergence between individuals from each sample set (list of
# individual names generated in the previous step)
ts_divergence(ts, sample_sets) %>% .[order(.$divergence), ]
#> # A tibble: 6 × 3
#> x y divergence
#> <chr> <chr> <dbl>
#> 1 AFR EUR 0.0000442
#> 2 AFR NEA 0.000383
#> 3 EUR NEA 0.000387
#> 4 CH NEA 0.00412
#> 5 AFR CH 0.00415
#> 6 CH EUR 0.00416
```