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

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.

Author(s)

Sophie Lebre

Frank Dondelinger

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


[Package EDISON version 1.1.1 Index]