Simulate bayesRCM data with underlying G-Wishart data generating mechanism — sim_data • bayesRCM

Simulate bayesRCM data with underlying G-Wishart data generating mechanism

Usage

sim_data(
  subjects = 20,
  volumes = 200,
  rois = 10,
  prop_true_con = 1/5,
  trunc = c(0, 100),
  alpha_tau = 50,
  lambda_2 = 2/5,
  n_flip = 1,
  seed_0 = 4,
  seed_k = 4,
  write = FALSE
)

Arguments

subjects: K number of subjects
volumes: V number of samples/fMRI observations (length of time-series)
rois: P network dimension
prop_true_con: Proportion of true connections
seed_0: Reproducible seed for the simulation of G_0/Omega_0
seed_k: Reproducible seed for the K simulations of subject-specific G_k/Omega_k
save: Logical, if TRUE write save

Value

data_list