Generates synthetic network data from a dynamic DBN with time-varying A and B influence matrices. Each period's influence structure evolves from the previous period's via a random walk (default) or AR(1) process.
Usage
simulate_dynamic_dbn(
n = 30,
n_col = n,
p = 2,
time = 50,
sigma2 = 0.5,
tauA2 = 0.05,
tauB2 = 0.05,
ar1 = FALSE,
rhoA = 0.9,
rhoB = 0.9,
K = 5,
return_truth = TRUE,
seed = NULL,
symmetric = FALSE
)Arguments
- n
Number of actors (senders)
- n_col
Number of receivers (default: same as
n)- p
Number of relation types (default: 2)
- time
Number of time periods to simulate
- sigma2
Process noise variance
- tauA2
Innovation variance for A (how fast sender influence changes). Larger values produce more volatile influence dynamics.
- tauB2
Innovation variance for B (how fast receiver influence changes)
- ar1
If TRUE, use AR(1) dynamics (smooth evolution). If FALSE (default), use random walk (less smooth).
- rhoA
AR(1) persistence parameter for A (only used if
ar1 = TRUE). Values near 1 produce slowly changing influence.- rhoB
AR(1) persistence parameter for B
- K
Number of ordinal categories for observed data (default: 5)
- return_truth
If TRUE (default), include true parameters in output
- seed
Random seed for reproducibility
- symmetric
If TRUE, set B = A at each time point.
Value
A list containing:
- Y
Observed ordinal data array
[n_row, n_col, p, time]- Z
Continuous latent values (use with
family = "gaussian")- Theta
True latent network state at each time point
- A
True time-varying sender influence
[n_row, n_row, time]- B
True time-varying receiver influence
[n_col, n_col, time]- M
True baseline mean array
- sigma2, tauA2, tauB2, rhoA, rhoB
True parameter values
See also
dbn() for model fitting, simulate_static_dbn() for
fixed-influence version