Transforms a DAG into an essential graph.

1 |

`dag` |
The graph matrix as produced from |

`type` |
This can be either NULL or 1 or 2. type = 1 means that the matrix contains 0, 1, 2, 3 where G[i, j] = g[j, i] = 0, means there is no edge between nodes i and j, G[i, j] = g[j, i] = 1, there is an edge between nodes i and j and G[i, j] = 2 and G[j, i] = 3 means that there is an arrow from node i to node j. If type 2, the matrix contains 0 for no edge and 1 for a directed edge. In this case, G[i,j]=1 and G[j,i]=0 means that there is an arrow from node i to node j. If you are not sure of what you have, just leave it NULL, the function will check to which case your matrix belongs. |

The function is an R translation from an old matlab code.

The matrix of the essential graph.

Ioannis Tsamardinos and Michail Tsagris

R implementation and documentation: Giorgos Athineou <athineou@csd.uoc.gr>, Ioannis Tsamardinos <tsamard@csd.uoc.gr> and and Michail Tsagris <mtsagris@csd.uoc.gr>

Chickering, D.M. (1995). A transformational characterization of equivalent Bayesian network structures. Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Montreal, Canada, 87-98.

1 2 3 4 5 6 7 8 | ```
# simulate a dataset with continuous data
# simulate a dataset with continuous data
y = rdag(1000, 10, 0.3)
tru = y$G
eg = dag2eg(tru)
par( mfrow = c(1, 2) )
plotnetwork(tru)
plotnetwork(eg)
``` |

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