Simulate non-BM trait data for benchmarking — simulate_non

Generates species trait data under models that deviate from Brownian Motion. Supports OU (stabilising selection), regime shifts (clade-specific optima), and non-linear inter-trait correlations.

Usage

simulate_non_bm(
  tree,
  n_traits = 4,
  scenario = c("OU", "regime_shift", "nonlinear"),
  alpha = 2,
  theta = 0,
  sigma = 1,
  shift_magnitude = 2,
  seed = NULL
)

Arguments

tree: Object of class "phylo".
n_traits: Integer. Number of traits to simulate (default 4). Use at least 2 for scenario = "nonlinear": the current generator always returns trait1 and trait2, even if a smaller number is requested.
scenario: Character. One of "OU", "regime_shift", "nonlinear".
alpha: Numeric. OU pull strength (only for "OU").
theta: Numeric. Base OU optimum (only for "OU"). Trait j uses theta + 0.5 * (j - 1) for both its optimum and root value.
sigma: Numeric. BM diffusion rate.
shift_magnitude: Numeric. Raw additive regime offset (only for "regime_shift"). Trait j uses shift_magnitude * (1 + 0.2 * (j - 1)), added to one clade and subtracted from the other. It is not multiplied by a trait SD; sigma separately controls the underlying BM diffusion.
seed: Integer seed or NULL.

Value

A data.frame with species as rownames.

Examples

tree <- ape::rtree(12)
traits <- simulate_non_bm(tree, n_traits = 2, scenario = "OU", seed = 1)
head(traits)
#>          trait1     trait2
#> t4   0.80182996  0.3154335
#> t8  -0.48729571 -0.2226767
#> t6   0.37793160  0.6794849
#> t5  -0.04127435  0.3547815
#> t10  0.34377702  1.0922780
#> t7  -0.99083407  0.3082257