simGaussianNet: Simulate a fully specified Gaussian Bayesian network

In robertness/gmsim: Simulations of Graphical Models.

Description

Simulates a Gaussian Bayesian network by simulating a network structure and a random covariance matrix consistent with the I-map of the network structure, and then calculating network parameters based on the covariance matrix.

Usage

simGaussianNet(num.vars, rng = c(0.5, 1.5), method = "melancon")

Arguments

num.vars	Number of variables/nodes
rng	The desired range of the eigen values in the covariance matrix.
method	A character string, possible values are ordered, ic-dag, and melancon. See ?random.graph in bnlearn

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.