Simulate an FDA Benchmark Scenario — simulate_fda

Generates raw functional predictors, scalar covariates, a response, and the mapped ground truth for the transformed design matrix.

Usage

simulate_fda_scenario(
  n = 80L,
  grid_length = 60L,
  family = c("gaussian", "binomial"),
  representation = c("grid", "basis", "fpca"),
  transforms = NULL,
  basis_df = 7L,
  n_components = 5L,
  scenario = c("localized_dense", "distributed_smooth", "confounded_blocks"),
  confounding_strength = NULL,
  active_region_scale = 1,
  local_correlation = 0,
  include_scalar = TRUE,
  noise_sd = 0.4,
  seed = NULL
)

Arguments

n: Number of observations.
grid_length: Number of grid points per functional predictor.
family: Model family used to generate the response.
representation: Representation used when building the returned fda_design(): "grid" keeps the raw curves, "basis" applies a spline-basis transform, and "fpca" applies FPCA scores.
transforms: Optional transform list passed to fda_design(). When omitted, a sensible default is chosen from representation.
basis_df: Degrees of freedom used when representation = "basis".
n_components: Number of FPCA components used when representation = "fpca".
scenario: Benchmark scenario. "localized_dense" emphasizes narrow active regions under strong local correlation, "distributed_smooth" spreads the effect over broader smooth regions, and "confounded_blocks" adds stronger nuisance structure near the active block.
confounding_strength: Strength of cross-block confounding injected into the nuisance curve. Higher values make plain SelectBoost less able to separate true local signals from correlated nuisance structure.
active_region_scale: Positive multiplier applied to the width of the active regions. Values below 1 create narrower active regions.
local_correlation: Non-negative smoothing parameter applied to the simulated curves. Larger values increase local correlation along the grid.
include_scalar: Should scalar covariates be included in the design and truth object?
noise_sd: Observation noise level.
seed: Optional random seed.

Value

An object of class fda_simulation_data.

Examples

sim <- simulate_fda_scenario(n = 24, grid_length = 16, seed = 1)
sim
#> FDA simulation data
#>   observations: 24 
#>   features: 34 
#>   active features: 8 
#>   scenario: localized_dense 
#>   confounding strength: 0 
#>   active region scale: 1 
#>   local correlation: 0 
#>   active predictors: signal, age, treatment 
head(sim$truth$active_features)
#> [1] "signal_2"  "signal_3"  "signal_4"  "signal_8"  "signal_9"  "signal_10"