| simulateData {GGMridge} | R Documentation |
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")
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". |
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. |
Author(s)
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.
Examples
simulation <- simulateData(G = 100, etaA = 0.02, n = 50, r = 10)