remulateActorEffects: Remulate Actor Effects
In remulate: Simulate Dynamic Networks from Relational Event Models

remulateActorEffects

R Documentation

Remulate Actor Effects

Description

This page lists the effects that are available in the remulate package for the actor-oriented relational event model.

Usage

remulateActorEffects(rateEffects = TRUE, choiceEffects = TRUE)

Arguments

`rateEffects`	Logical. If TRUE, includes rate effects (i.e sender-rate effects) of the actor-oriented relational event model. If FALSE, rate effects are excluded.
`choiceEffects`	Logical. If TRUE, includes choice effects (i.e receiver-choice effects) of the actor-oriented relational event model. If FALSE, choice effects are excluded.

Details

The attr_actors object contains at least three columns (actor,time,attribute). It should be constructed as follows: Each row refers to the attribute value of actor i at timepoint t. The first column contains the actor names (corresponding to the vector of names in the actors argument of remulateActor). The second column contains the time when attributes change (set to zero if the attributes do not vary over time). Subsequent columns contain the attributes that are called in the specifications of exogenous statistics. The same attr_actors object can be used with multiple exogenous statistics.

Value

Returns a character vector of available effects for the rateEffects or choiceEffects argument for the function remulateActor.

remulateActor Rate Effects

Endogenous effects:

baseline: Baseline tendency for actors to create events. The statistic equals to 1 for all actors in the riskset. The parameter for baseline controls the average number of events per unit time.
indegreeSender: In degree effect of the sender is the tendency for actor i to create an event when i has received more events in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of events received by actor i before timepoint t. Note: if scaling is "prop" for indegreeSender, the statistic for dyad (i,j) at time t is divided by the total degree of the sender i at time t.
outdegreeSender: Out degree effect of sender is the tendency for actor i to create an event when i has sent more events in the past. Note: if scaling is "prop" for outdegreeSender, the statistic for dyad (i,j) at time t is divided by the total degree of the sender i at time t.
totaldegreeSender: Total degree effect of sender is the tendency for actor i to create an event when i has sent and received more events in the past.
ospSender: Outgoing Shared Partners actor effect is the tendency for actor i to create an event if actor i is the source in a transitive structure (i->h<-j<-i).
otpSender: Outgoing Two Path actor effect is the tendency for sender i to create an event if actor i is the source in a transitive structure (i->h->j<-i).

Exogenous effects:

send: The tendency for actor i to create an event when i has a high attribute value.

remulateActor Choice Effects

Endogenous effects (Dyad statistics):

inertia: Inertia is the tendency to create an event i->j if the event i->j occurred in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of (i,j) events before timepoint t. Note: if scaling is "prop" for inertia, the statistic for dyad (i,j) at time t is divided by the out degree of the sender i at time t.
reciprocity: Reciprocity is the tendency to create an event i->j if j->i occurred in the past.The statistic at timepoint t for dyad (i,j) is equal to the number of (j,i) events before timepoint t. Note: if scaling is "prop" for inertia, the statistic for dyad (i,j) at time t is divided by the in degree of the sender i at time t.
tie: Tie effect is the tendency to create an event i->j if the event i->j occurred at least once in the past. The statistic at timepoint t for dyad (i,j) is equal to 1 if a an event i->j occurred before timepoint t

Endogenous effects (Triadic statistics):

otp: Outgoing Two Path effect is the tendency to create an event i->j if they have past outgoing two-paths between them (i->h->j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (i,h), (h,j) events, summed over all h.
itp: Incoming Two Path effect is the tendency to create an event i->j if they have past incoming two-paths between them (i<-h<-j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (j,h), (h,i) events, summed over all h.
osp: Outgoing Shared Partners effect is the tendency to create an event i->j if they have past outgoing shared partners between them (i->h<-j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (i,h), (j,h) events, summed over all h.
isp: Incoming Shared Partners effect is the tendency to create an event i->j if they have past incoming shared partners between them (i<-h->j). The statistic for dyad (i,j) at timepoint t is equal to the minimum of past (h,i), (h,j) events, summed over all h.

Endogenous effects (Node statistics):

indegreeReceiver: In degree effect of receiver is the tendency to create an event i->j if j has received more events in the past. The statistic at timepoint t for dyad (i,j) is equal to the number of events received by actor j before timepoint t. Note: if scaling is "prop" for indegreeReceiver, the statistic for dyad (i,j) at time t is divided by the total degree of the receiver j at time t.
outdegreeReceiver: Out degree effect of receiver is the tendency to create an event i->j if j has sent more events in the past. Note: if scaling is "prop" for outdegreeReceiver, the statistic for dyad (i,j) at time t is divided by the total degree of the receiver j at time t.
totaldegreeReceiver: Total degree effect of receiver is the tendency to create an event i->j if j has sent and received more events in the past.

Exogenous effects:

dyad: Dyadic attribute value is tendency to create an event i -> j when (i,j) has a high attribute value.
receive: Receiver attribute value is the tendency to create an event i->j when j has a high attribute value.
same: Same attribute value (Homophily) is the tendency to create an event i->j if actors i and j have the same attribute values
Difference: difference attribute value (Heterophily) is the tendency to create an event i->j if actors i and j have a high absolute difference in attribute values

Examples

#To specify an exogenous effect (example: same)

cov <- data.frame(
  actor = 1:10,
  time = rep(0, 10),
  gender = sample(c(0, 1), replace = TRUE, 10),
  age = sample(20:30, 10, replace = TRUE)
)

effects <- ~ same(0.2, variable = "gender", attr_actors = cov)

#Rate Effects:

#If parameter is constant

rateEffects <- ~ outdegreeSender(0.3) + 
  send(0.1, variable = "age", attr_actors = cov)

#If parameter varies with time

rateEffects <- ~ outdegreeSender(param = function(t) exp(-t)) + 
  send(0.1, variable = "age", attr_actors = cov)

#Choice Effects:

#If parameter is constant

choiceEffects <- ~ inertia(0.4) + 
  reciprocity(-0.1) + 
  same(0.2, variable = "gender", attr_actors = cov) + 
  receive(0.1, variable = "age", attr_actors = cov)

#If parameter varies with time

choiceEffects <- ~ inertia(param = function(t) exp(-t)) +  
  reciprocity(-0.1) + 
  same(0.2, variable = "gender", attr_actors = cov) + 
  receive(0.1, variable = "age", attr_actors = cov)

remulate documentation built on April 16, 2025, 5:09 p.m.