simulate_cascades: Simulate cascades from a diffusion network
In desmarais-lab/NetworkInference: Inferring Latent Diffusion Networks

Description Usage Arguments Value Examples

Simulate diffusion cascades based on the generative model underlying netinf and a diffusion network.

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simulate_cascades(diffnet, nsim = 1, max_time = Inf,
  start_probabilities = NULL, partial_cascade = NULL, params = NULL,
  model = NULL, nodes = NULL)

`diffnet`	object of class `diffnet`.
`nsim`	integer, number of cascades to simulate.
`max_time`	numeric, the maximum time after which observations are censored
`start_probabilities`	a vector of probabilities for each node in diffnet, to be the node with the first event. If `NULL` a node is drawn from a uniform distribution over all nodes.
`partial_cascade`	object of type cascade, containing one partial cascades for which further development should be simulated.
`params`	numeric, (optional) parameters for diffusion time distribution. See the details section of `netinf` for specification details. Only use this argument if parameters different from those contained in the `diffnet` object should be used or the network is not an object of class `diffnet`.
`model`	character, diffusion model to use. One of `c("exponential", "rayleigh", "log-normal")`. Only use this argument if parameters different from those contained in the `diffnet` object should be used or the network is not an object of class `diffnet`.
`nodes`	vector of node ids if different from nodes included in `diffnet`

A data frame with three columns. Containing 1) The names of the nodes ("node_name") that experience an event in each cascade, 2) the event time ("event_time") of the corresponding node, 3) the cascade identifier "cascade_id".

data(cascades) 
out <- netinf(cascades, trans_mod = "exponential", n_edges = 5, params = 1)
simulated_cascades <- simulate_cascades(out, nsim = 10)
 
# Simulation from partial cascade