simBNFromCovAndDAG: Simulate a Bayesian network from a DAG and covariance matrix
In robertness/gmsim: Simulations of Graphical Models.
Description
Usage
Arguments
Value
See Also
Description
Given a covariance matrix and a directed acyclic graph structure,
simulates a fully parameterized Guassian Bayesian network consistent
with the covariance matrix.
Usage
1
simBNFromCovAndDAG(dag, cov.mat)
Arguments
dag
A object of class bn, with no undirected edges.
cov.mat
A positive definate matrix.
Value
An object of class bn.fit
See Also
simGaussianNet, simBNFromCovMat,
simDAGFromCovMat, simNetCovariance, simMVNData,
randomNet, simSparsePrecision, simDAGFromCovMat
robertness/gmsim documentation built on May 27, 2019, 10:32 a.m.
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Description Usage Arguments Value See Also
Description
Given a covariance matrix and a directed acyclic graph structure, simulates a fully parameterized Guassian Bayesian network consistent with the covariance matrix.
Usage
1 | simBNFromCovAndDAG(dag, cov.mat)
|
Arguments
dag |
A object of class bn, with no undirected edges. |
cov.mat |
A positive definate matrix. |
Value
An object of class bn.fit
See Also
simGaussianNet, simBNFromCovMat,
simDAGFromCovMat, simNetCovariance, simMVNData,
randomNet, simSparsePrecision, simDAGFromCovMat
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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