Simulate indifference points from the linearized Mazur random-effects model

Draws theta_ic ~ N(mu_c, g sigma2 / T), y_ijc ~ N(theta_ic, sigma2) and returns D = 1 / (1 + exp(y) t) (Hinds et al., 2026, Sec. 4.1), which is always strictly inside (0, 1).

Usage

simulate_dd_linear(
  n_subjects,
  delays,
  mu,
  sigma2,
  g,
  seed = NULL,
  attach_truth = FALSE
)

Arguments

n_subjects: Integer, scalar (recycled) or one per condition.
delays: Positive numeric vector of delays (T = length(delays)).
mu: Numeric vector of population mean ln k per condition; names become condition labels.
sigma2: Measurement-error variance on the transformed scale.
g: Random-effect variance multiplier: Var(theta) = g * sigma2 / T.
seed: Optional seed; the global RNG state is restored afterwards.
attach_truth: If TRUE, attach attr(, "truth").

Value

Long data.frame(id, condition, x, y).

Examples

s <- simulate_dd_linear(30, c(7, 30, 180, 365), mu = c(A = -6, B = -5),
                        sigma2 = 2, g = 10, seed = 1)
anova(fit_dd_linear(s, factors = "condition"))
#> # A tibble: 1 × 6
#>   hypothesis     F   df1   df2 p_value cohens_d
#>   <chr>      <dbl> <int> <dbl>   <dbl>    <dbl>
#> 1 A = B       4.59     1    58  0.0363   -0.563