Control parameters for datagen()

Control parameters for datagen()

Usage

datagenControl(
  method = c("mc", "fo", "gh"),
  n_sim = 5000L,
  n_nodes = 5L,
  sampling = c("sobol", "halton", "torus", "lhs", "rnorm"),
  seed = 12345L,
  cores = 1L,
  return_samples = FALSE
)

Arguments

method

Moment approximation used to generate E and V: "mc" (default) draws Monte Carlo samples over the IIV distribution, as in est = "admc"; "fo" uses the deterministic First-Order expansion (mu = f(theta, 0), V = J Omega J' + Sigma), matching est = "adfo"; "gh" uses deterministic Gauss-Hermite quadrature over the random-effects prior, matching est = "adgh" – unbiased at any IIV magnitude and noise-free. Use "fo" or "gh" for design evaluation where the data-generating and data-analytic models must coincide.

n_sim

Number of Monte Carlo samples used to approximate population moments. Ignored when method = "fo" or "gh".

n_nodes

Number of Gauss-Hermite nodes per eta dimension for method = "gh" (default 5). Total nodes = n_nodes^n_eta. Ignored for "mc" and "fo".

sampling

Quasi-random sampling method: "sobol" (default), "halton", "torus", "lhs", or "rnorm". Ignored when method = "fo" or "gh".

seed

Integer seed. Applied before stochastic methods ("rnorm", "lhs"). Ignored when method = "fo" or "gh".

cores

Number of rxSolve threads.

return_samples

Include the raw n_sim x length(times) prediction matrix as $samples in each study's output. No effect when method = "fo" or "gh" (those methods draw no samples).

Value

A list of class "datagenControl".

See also

datagen()

Examples

ctrl <- datagenControl(n_sim = 2000L)
ctrl$sampling  # "sobol"
#> [1] "sobol"

# Deterministic FO moments for design evaluation:
datagenControl(method = "fo")$method  # "fo"
#> [1] "fo"

# GH quadrature moments (unbiased, noise-free):
datagenControl(method = "gh", n_nodes = 5L)$n_nodes
#> [1] 5