Description Usage Arguments Details Value Author(s) References Examples
Simulate a directed acyclic graph with mixed data (gaussian and binary).
1 |
n |
Number of observations. |
p |
Number of variables. Not applicable to the graph of |
sparsity |
Sparsity of the graph in the |
p.binary |
Number of binary variables. Not applicable to the graph of |
type |
The graph structure with 3 options: |
verbose |
If |
Given the type of graph, the patterns are generated as below:
(I) "random"
: Each pair of off-diagonal elements are randomly set edgematrix[i,j]=1
for i < j
with probability sparsity
, and 0
otherwise. It results in about p*(p-1)*sparsity/2
edges in the graph.
(II)"AR(2)"
: The off-diagonal elements are set to be theta[i,j]=1
if i<j
and |i-j|<=2
and 0
otherwise.
(III) "alarm"
: The graph structure is directly borrowed from package 'bnlearn', which has 37 variables with 46 edges. See 'bnlearn' for more detail.
A list of five objects.
edgematrix |
A pxp matrix which indicates the true structure of directed acyclic graph. If the (i,j)th element is equal to 1, there exists a directed edge from X_i to X_j. |
data |
The simulated dataset in a nxp matrix. |
moral.matrix |
The simulated adjacency matrix of the moral graph, which is the undircted version of Bayesian network. |
gaussian.index |
The index of Gaussian variables. |
binary.index |
The index of binary variables. |
Suwa Xu, Bochao Jia and Faming Liang
Kalisch, M., and Buhlmann, P. (2007). Estimating high-dimensional directed acyclic graphs with the PC-algorithm. Journal of Machine Learning Research, 8(Mar), 613-636.
Xu, S., Jia, B., and Liang, F. (2018). Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data. Submitted.
I. A. Beinlich, H. J. Suermondt, R. M. Chavez, and G. F. Cooper. The ALARM Monitoring System: A Case Study with Two Probabilistic Inference Techniques for Belief Networks. In Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine, pages 247-256. Springer-Verlag, 1989.
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