Simulate from the model X = X
If we pass in upper triangular then we take transpose
X ~ MVN(0, (I - W)^-T
Where eps ~ MVN(0, Sigma)
Usage
sim_linear_sem(W, n = 1, Sigma = diag(ncol(W)), check_lower = TRUE)
Arguments
- W
Adjacency matrix representing SEM (d x d)
- n
Number of samples
- Sigma
Covariance matrix of noise
Value
Matrix of samples (n x d)
Examples
B <- matrix(
c(0, 3, 0, 3,
0, 0, 0, 5,
0, 0, 0, 2,
0, 0, 0, 0),
nrow = 4, ncol = 4, byrow = TRUE)
d <- ncol(B)
X <- sim_linear_sem(B, n = 500, Sigma = 1 * diag(ncol(B)))
