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