Calculate the f2, f3, f4, and f4-ratio statistics

ts_f2(
  ts,
  A,
  B,
  mode = c("site", "branch", "node"),
  span_normalise = TRUE,
  windows = NULL
)

ts_f3(
  ts,
  A,
  B,
  C,
  mode = c("site", "branch", "node"),
  span_normalise = TRUE,
  windows = NULL
)

ts_f4(
  ts,
  W,
  X,
  Y,
  Z,
  mode = c("site", "branch", "node"),
  span_normalise = TRUE,
  windows = NULL
)

ts_f4ratio(
  ts,
  X,
  A,
  B,
  C,
  O,
  mode = c("site", "branch"),
  span_normalise = TRUE
)

Arguments

ts: Tree sequence object of the class slendr_ts
mode: The mode for the calculation ("sites" or "branch")
span_normalise: Divide the result by the span of the window? Default TRUE, see the tskit documentation for more detail.
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.
W, X, Y, Z, A, B, C, O: Character vectors of individual names (following the nomenclature of Patterson et al. 2021)

Value

Data frame with statistics calculated for the given sets of individuals

Examples

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)

# calculate f2 for two individuals in a previously loaded tree sequence
ts_f2(ts, A = "AFR_1", B = "EUR_1")
#> # A tibble: 1 × 3
#>   A     B           f2
#>   <chr> <chr>    <dbl>
#> 1 AFR_1 EUR_1 0.000042

# calculate f2 for two sets of individuals
ts_f2(ts, A = c("AFR_1", "AFR_2"), B = c("EUR_1", "EUR_3"))
#> # A tibble: 1 × 3
#>   A           B                  f2
#>   <chr>       <chr>           <dbl>
#> 1 AFR_1+AFR_2 EUR_1+EUR_3 0.0000427

# calculate f3 for two individuals in a previously loaded tree sequence
ts_f3(ts, A = "EUR_1", B = "AFR_1", C = "NEA_1")
#> # A tibble: 1 × 4
#>   A     B     C           f3
#>   <chr> <chr> <chr>    <dbl>
#> 1 EUR_1 AFR_1 NEA_1 0.000022

# calculate f3 for two sets of individuals
ts_f3(ts, A = c("AFR_1", "AFR_2", "EUR_1", "EUR_2"),
          B = c("NEA_1", "NEA_2"),
          C = "CH_1")
#> # A tibble: 1 × 4
#>   A                       B           C           f3
#>   <chr>                   <chr>       <chr>    <dbl>
#> 1 AFR_1+AFR_2+EUR_1+EUR_2 NEA_1+NEA_2 CH_1  0.000199

# calculate f4 for single individuals
ts_f4(ts, W = "EUR_1", X = "AFR_1", Y = "NEA_1", Z = "CH_1")
#> # A tibble: 1 × 5
#>   W     X     Y     Z        f4
#>   <chr> <chr> <chr> <chr> <dbl>
#> 1 EUR_1 AFR_1 NEA_1 CH_1      0

# calculate f4 for sets of individuals
ts_f4(ts, W = c("EUR_1", "EUR_2"),
          X = c("AFR_1", "AFR_2"),
          Y = "NEA_1",
          Z = "CH_1")
#> # A tibble: 1 × 5
#>   W           X           Y     Z        f4
#>   <chr>       <chr>       <chr> <chr> <dbl>
#> 1 EUR_1+EUR_2 AFR_1+AFR_2 NEA_1 CH_1      0

# calculate f4-ratio for a given set of target individuals X
ts_f4ratio(ts, X = c("EUR_1", "EUR_2", "EUR_4", "EUR_5"),
               A = "NEA_1", B = "NEA_2", C = "AFR_1", O = "CH_1")
#> # A tibble: 4 × 6
#>   X     A     B     C     O     alpha
#>   <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 EUR_1 NEA_1 NEA_2 AFR_1 CH_1      0
#> 2 EUR_2 NEA_1 NEA_2 AFR_1 CH_1      0
#> 3 EUR_4 NEA_1 NEA_2 AFR_1 CH_1      0
#> 4 EUR_5 NEA_1 NEA_2 AFR_1 CH_1      0