remulate (version 2.1.0)

remulateActor: Simulate Relational Event Data - Actor-Oriented model

Description

A function to simulate relational event data by sampling from an actor-oriented relational event model.

Usage

remulateActor(
  rateEffects,
  choiceEffects,
  actors,
  endTime,
  events = NULL,
  startTime = 0,
  initial = 0,
  riskset = NULL,
  memory = c("full", "window", "window_m", "decay"),
  memoryParam = NULL
)

Value

An object of class "remulateActor". A data.frame containing the simulated event sequence with columns (time, sender, receiver). The "remulateActor" object has the following attributes::

evls

A matrix containing the event list with columns (dyad, time), where dyad represents the index of the (sender, receiver) pair in the risk set.

rateStatistics

An array of dimensions M x N x P, where: - M is the number of events, - N is the number of actors, - P is the number of sender rate statistics.

choiceStatistics

An array of dimensions M x D x Q, where: - M is the number of events, - D is the number of dyads in the risk set, - Q is the number of receiver choice statistics.

rateParams

A named list of rate model parameters corresponding to the specified rate statistics.

choiceParams

A named list of choice model parameters corresponding to the specified choice statistics.

riskset

A matrix with columns (sender, receiver) representing the risk set used in the simulation.

actors

A data.frame mapping the actor names provided by the user to the integer IDs used in internal computations.

#'

initial

A A numeric or data.frame object representing the network initialization, which can be a number of random initialization events or a data.frame specifying pre-existing ties.

rateEffects

A formula object specifying the effects included in the rate sub-model.

choiceEffects

A formula object specifying the effects included in the choice sub-model.

density

A numeric value indicating the density of the generated network, defined as the number of observed ties divided by N*(N-1), where N is the number of actors.

Arguments

rateEffects

A formula object specifying the statistics used to simulate the network under the actor rate sub-model.

choiceEffects

A formula object specifying the statistics used to simulate the network under the receiver choice sub-model.

actors

A numeric or character vector representing the actor names.

endTime

A numeric value specifying the end time up to which the network should be simulated.

events

[Optional] An integer specifying the maximum number of events to simulate.

startTime

[Optional] A numeric value (default = 0) indicating the time at which the simulation should start.

initial

[Optional] A numeric or data.frame object (default = 0) specifying how to initialize the network. - If an integer is provided, it represents the number of random events to sample before beginning data generation. - If a data.frame is provided with columns (time, sender, receiver), it serves as an edgelist of initial events, after which subsequent events are predicted.

riskset

[Optional] A matrix with columns (sender, receiver) defining a custom risk set.

memory

[Optional] A string (default = "full") specifying the memory type used for computing statistics. - `"full"`: Uses the entire event history. - `"window"`: Considers only events occurring within a specified time window. - `"window_m"`: Considers only a specified number of most recent events. - `"decay"`: Applies an exponential decay, where older events contribute less based on elapsed time.

memoryParam

[Optional] A numeric value (> 0) defining the memory parameter based on the selected memory type: - `"window"`: Length of the time window. - `"window_m"`: Number of past events to consider. - `"decay"`: Half-life (i.e., time until an event's weight is reduced to half).

Details

#' If time is irrelevant and only a specific number of events are desired, set time to Inf. If both time and events are supplied then the function stops simulating whenever the first stop condition is met

A list of available statistics for actor rate model. See remulateActorEffects for details on effects:

• baseline()

• indegreeSender()

• outdegreeSender()

• totaldegreeSender()

• ospSender()

• otpSender()

• send()

• interact()

A list of available statistics for receiver choice model. See remulateActorEffects for details on effects: :

• inertia()

• reciprocity()

• indegreeReceiver()

• outdegreeReceiver()

• totaldegreeReceiver()

• otp()

• itp()

• osp()

• isp()

• psABBA()

• psABBY()

• psABXA()

• psABXB()

• psABXY()

• psABAY()

• recencyContinue()

• recencySendReceiver()

• recencyReceiveReceiver()

• rrankReceive()

• receive()

• dyad()

• same()

• average()

• difference()

• minimum()

• maximum()

• interact()

References

Lakdawala, R., Mulder, J., & Leenders, R. (2025). *Simulating Relational Event Histories: Why and How*. arXiv:2403.19329.

Examples

 # To generate events up to time '50' in a network of 25 actors with 
 # 200 random initial events
 
 # Exogenous attributes data.frame
 cov <- data.frame(
   id   = 1:25, 
   time = rep(0, 25), 
   sex  = sample(c(0,1), 25, replace = TRUE, prob = c(0.4, 0.6)), 
   age  = sample(20:30, 25, replace = TRUE) 
 )
 
 # Effects specification
 rateform <- ~ remulate::baseline(-6) + 
              remulate::indegreeSender(0.01) + 
              remulate::send(0.02, variable = "age", attr_actors = cov) + 
              remulate::interact(0.01, indices = c(2, 3))
 
 choiceform <- ~ remulate::inertia(0.01) + 
               remulate::reciprocity(-0.03) + 
               remulate::interact(0.01, indices = c(2, 1))
 
 # Calling remulateActor
 remulate::remulateActor(
   rateform, 
   choiceform, 
   actors  = 1:25, 
   endTime = 100, 
   initial = 200, 
   events  = 500, 
 )
  
 # To predict events, given an edgelist of initial events
 initialREH <- data.frame(
   time = seq(0.5, 100, 0.5), 
   sender = sample(1:25, 200, TRUE), 
   receiver = sample(1:25, 200, TRUE)
 )
 
 remulate::remulateActor(
   rateform, 
   choiceform, 
   actors  = 1:25, 
   endTime = 200, 
   initial = initialREH, 
   events  = 500
 )