Description Usage Arguments Value Examples
This function allows you to learn the DAG structure from observational data
1 2 |
X |
The n by p data matrix |
lambda |
tuning parameter for the first penalty of the adjacency matrix |
tau |
tuning parameter of the TLP function |
rho |
the ADMM penalty parameter, default is 1 |
A_NZ0 |
An p by p matrix indicating nonzero elements as initial values |
A0 |
An p by p matrix as initial values for A |
opts.tol |
Tolerance for convergence |
maxIter |
maximum number of iterations in ADMM loop |
Estimated adjacency matrix
1 2 3 4 5 6 | p <- 10
amat <- matrix(0, p, p)
amat[2:p, 1] <- 1
Sig <- seq(1, 0.5, length.out=p)
X <- rmvDAG_obs(100, amat, Sig)
out <- obsDAG(X, 5, 0.01)
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