Calculate diversity in given sets of individuals

Usage

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

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) # 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)

# compute diversity in each population based on sample sets extracted
# in the previous step
ts_diversity(ts, sample_sets) %>% .[order(.$diversity), ]
#> # A tibble: 4 × 2
#>   set    diversity
#>   <chr>      <dbl>
#> 1 AFR   0         
#> 2 CH    0         
#> 3 NEA   0.000004  
#> 4 EUR   0.00000484