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EDISON (version 1.1.1)

runDBN: 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
TRUE when a specific variance is estimated for each segment, FALSE otherwise.
minPhase
Minimal segment length.
niter
Number of MCMC iterations.
scaling
If TRUE, scale the input data to mean 0 and standard deviation 1, else leave it unchanged.
method
Network structure prior to use: 'poisson' for a sparse Poisson prior (no information sharing), 'exp_hard' or 'exp_soft' for the exponential information sharing prior with hard or soft node coupling, 'bino_hard' or 'bino_soft' with hard or soft node coupling.
prior.params
Initial hyperparameters for the information sharing prior.
self.loops
If TRUE, allow self-loops in the network, if FALSE, disallow self-loops.
k
Initial value for the level-2 hyperparameter of the exponential information sharing prior.
options
MCMC options as obtained e.g. by the function defaultOptions.
outputFile
File where the output of the MCMC simulation should be saved.
fixed.edges
Matrix of size NumNodes by NumNodes, with fixed.edges[i,j]==1|0 if the edge between nodes i and j is fixed, and -1 otherwise. Defaults to NULL (no edges fixed).

Value

A list containing the results of the MCMC simulation: network samples, changepoint samples and hyperparameter samples. For details, see output.

References

For more information about the MCMC simulations, see:

Dondelinger et al. (2012), "Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure", Machine Learning.

See Also

output