simDAGFromCovMat: Simulate a Gaussian Bayesian network structure based on a...
In robertness/gmsim: Simulations of Graphical Models.
Description
Usage
Arguments
Value
See Also
Description
This function attempts to find a directed acyclic structure consistent with the
conditional independencies given by the covariance matrix under Gaussian assumptions.
If such a consistent extention cannot be found, then NULL is returned.
Usage
1
simDAGFromCovMat(cov.mat)
Arguments
cov.mat
A covariance matrix (positive definate matrix).
Value
An objective of class bn. If no consistent extention is found, NULL is returned.
See Also
simGaussianNet, simBNFromCovMat,
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
This function attempts to find a directed acyclic structure consistent with the conditional independencies given by the covariance matrix under Gaussian assumptions. If such a consistent extention cannot be found, then NULL is returned.
Usage
1 | simDAGFromCovMat(cov.mat)
|
Arguments
cov.mat |
A covariance matrix (positive definate matrix). |
Value
An objective of class bn. If no consistent extention is found, NULL is returned.
See Also
simGaussianNet, simBNFromCovMat,
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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