simulateData: Generate Simulation Data from a Random Network.
Description
Generate a random network where both the network structure and the partial
correlation coefficients are random. The data matrices are generated from
multivariate normal distribution with the covariance matrix corresponding
to the network.
Usage
simulateData(G, etaA, n, r, dist = "mvnorm")
Value
A list containing
data
a list, each element containing an n X G matrix of
simulated data.
true.partialcor
The partial correlation matrix which the
datasets are generated from.
truecor.scaled
The covariance matrix calculted from the
partial correlation matrix.
sig.node
The indices of nonzero upper triangle
elements of partial correlation matrix.
Arguments
G
The number of variables (vertices).
etaA
The proportion of non-null edges among all the G(G-1)/2 edges.
n
The sample size.
r
The number of replicated G by N data matrices.
dist
A function which indicates the distribution of sample.
"mvnorm" is multivariate normal distribution and
"mvt" is multivariate t distribution with df=2.
The default is set by "mvnorm".
Author
Min Jin Ha
References
Schafer, J. and Strimmer, K.
(2005).
An empirical Bayes approach to inferring large-scale gene
association networks.
Bioinformatics, 21, 754--764.