runDBN {EDISON}  R Documentation 
Setup and run the MCMC simulation.
Description
This function initialises the variabes for the MCMC simulation, runs the simulation and returns the output.
Usage
runDBN(targetdata, preddata = NULL, q, n, multipleVar = TRUE,
minPhase = 2, niter = 20000, scaling = TRUE, method = "poisson",
prior.params = NULL, self.loops = TRUE, k = 15, options = NULL,
outputFile = ".", fixed.edges = NULL)
Arguments
targetdata 
Target input data: A matrix of dimensions NumNodes by NumTimePoints. 
preddata 
Optional: Input response data, if different from the target data. 
q 
Number of nodes. 
n 
Number of timepoints. 
multipleVar 

minPhase 
Minimal segment length. 
niter 
Number of MCMC iterations. 
scaling 
If 
method 
Network structure prior to use: 
prior.params 
Initial hyperparameters for the information sharing prior. 
self.loops 
If 
k 
Initial value for the level2 hyperparameter of the exponential information sharing prior. 
options 
MCMC options as obtained e.g. by the function

outputFile 
File where the output of the MCMC simulation should be saved. 
fixed.edges 
Matrix of size NumNodes by NumNodes, with

Value
A list containing the results of the MCMC simulation: network
samples, changepoint samples and hyperparameter samples. For details, see
output
.
Author(s)
Sophie Lebre
Frank Dondelinger
References
For more information about the MCMC simulations, see:
Dondelinger et al. (2012), "Nonhomogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually timevarying structure", Machine Learning.