EDISON.run {EDISON}

Wrapper function for starting an MCMC simulation

Description

This function provides a wrapper for starting an MCMC simulation, using only the data and some basic options as input.

Usage

EDISON.run(input, output.file = "EDISON.output",
  information.sharing = "poisson", num.iter = 10000, prior.params = NULL,
  options = NULL, fixed.edges = NULL)

Arguments

input

Input data. Either a filename pointing to an R data file containing the results of simulateNetwork, or a NumTimePoints by NumNodes matrix.

output.file

Where to save the output of the MCMC simulation.

information.sharing

Which information sharing prior to use: 'poisson' for the Poisson prior (no information sharing), 'exp_hard' or 'exp_soft' for the exponential prior with hard or soft coupling among nodes, respectively, and 'bino_hard' or 'bino_soft' for the binomial prior with hard or soft coupling among nodes.

num.iter

Number of iterations of the MCMC simulation.

prior.params

Initial values of the hyperparameters of the information sharing priors.

options

Settings for the MCMC simulation, as generated by defaultOptions.

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

Returns the results of the MCMC simulation, similar to runDBN.

Author(s)

Sophie Lebre Frank Dondelinger

References

For details on the model and MCMC simulation, 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

runDBN

Examples

# Generate random gene network and simulate data from it
dataset = simulateNetwork(l=25)

# Run MCMC simulation to infer networks and changepoint locations
# Uses default settings: Poisson prior and 1500 iterations
result.poisson = EDISON.run(dataset$sim_data, num.iter=500)

# Use the binomial information sharing prior with hard node coupling, and
# run for 5000 iterations

# NOT EXECUTED
#result.bino = EDISON.run(dataset$sim_data, 
#                information.sharing='bino_hard', num.iter=5000)
                        
# Set options to allow saving network and changepoint samples to file
options = defaultOptions()
options$save.file = TRUE

# NOT EXECUTED
# result.bino2 = EDISON.run(dataset$sim_data, 
#                  information.sharing='bino_hard',
#                  num.iter=5000, output.file='bino2.results',
#                  options=options)