simulateNetwork: Generate network and simulate data.
In EDISON: Network Reconstruction and Changepoint Detection

Description Usage Arguments Value Author(s) See Also Examples

This function generates a random network with structure changepoints (or takes one as input) and simulated data from it using a regression model.

simulateNetwork(l = 100, min_phase_length = 10, k_bar = 10, q = 10,
  lambda_2 = 0.45, noise = 0.25, net = NULL, lambda_3 = 2,
  spacing = 0, gauss_weights = FALSE, same = FALSE,
  changes = "sequential", fixed = FALSE, cps = NULL, saveFile = NULL)

`l`	Length of the time series.
`min_phase_length`	Minimum segment length.
`k_bar`	Maximum number of changepoints.
`q`	Number of nodes.
`lambda_2`	Average number of parents for each node in the network (parameter for a Poisson distribution).
`noise`	Standard deviation of the Gaussian observation noise. Can be constant, or segment specific (in which case the number of changepoints needs to be fixed and the noise needs to be a vector of the same length).
`net`	Input network, can be `NULL` if a new network should be generated.
`lambda_3`	Average number of structure changes between two segments (parameter for a Poisson distribution).
`spacing`	`1` if segments are equally spaced, `0` if they are spaced randomly (subject to the constraints of min_phase_length).
`gauss_weights`	`1` if edge weights in the network are drawn from N(0, 1), `0` if they are fixed to be 1.
`same`	`1` if the networks should all be the same (no changes), `0` otherwise.
`changes`	`'sequential'` if the changes happen sequentially (i.e. changes at segment i are applied to segment i-1), `'hierarchical'` if the changes happen with respect to a hypernetwork (i.e. changes at segment i are applied to segment 0).
`fixed`	`T` if the changepoint locations are fixed, `F` if they should be sampled.
`cps`	Changepoint locations (if they are fixed).
`saveFile`	If not `NULL`, indicates the filename for saving the output in R data format.

A list with elements:

`sim_data`	A matrix of length NumNodes by NumTimepoints containing the simulated data from the regression model.
`epsilon`	Changepoint vector.
`k`	Number of changepoints.
`network`	The network, a list of length NumSegs, where each element is a NumNodes by NumNodes matrix.
`noise`	The standard deviation of the applied Gaussian noise.

Frank Dondelinger

generateNetwork

# Generate random network and simulate data with default parameters
dataset = simulateNetwork()

# Generate random network and simulate data with an average of 
# 1 change per node among network segments
dataset = simulateNetwork(lambda_3=1)

# Generate random network and simulate data with an average of 
# 1 change per node among network segments and standard deviation 
# of the Gaussian observation noise 0.5
dataset = simulateNetwork(lambda_3=1, noise=0.5)

# Generate random network with default parameters
network = generateNetwork()

# Simulate data using generated network
dataset = simulateNetwork(net=network)

# Generate random network with 4 changepoints and 15 nodes, 
# with changepoints distributed over a timeseries of length 50
network = generateNetwork(l=50, q=15, fixed=TRUE, k_bar=4)

# Simulate data of length 50 using generated network
dataset = simulateNetwork(net=network)