We can start by defining sender and receiver sets and generating exogenous covariates. Static covariates return wide matrices, while time-varying covariates are emitted in tidy format.
senders <- paste0("s", 1:3)
receivers <- paste0("r", 1:2)
covs <- simulate_actor_covariates(
senders = senders,
receivers = receivers,
covariate_names = c("activity", "popularity"),
time_points = 0:5,
rho = 0.7,
sd = 0.2,
seed = 2024
)
head(covs$sender_covariates)
#> actor time covariate value
#> 1 s1 0 activity 0.275006442
#> 2 s1 1 activity 0.170910243
#> 3 s1 2 activity 0.351256861
#> 4 s1 3 activity 0.352809232
#> 5 s1 4 activity 0.001991379
#> 6 s1 5 activity -0.332991039Using the covariates, we simulate 20 relational events. Sender effects control how activity shapes outgoing intensity, and receiver effects do the same for popularity.
static_sender <- reshape(
covs$sender_covariates,
direction = "wide",
idvar = "actor",
timevar = "covariate"
)
#> Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
#> varying, : multiple rows match for covariate=activity: first taken
#> Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying =
#> varying, : multiple rows match for covariate=popularity: first taken
events <- simulate_relational_events(
n_events = 20,
senders = senders,
receivers = receivers,
baseline_rate = 2,
sender_covariates = static_sender[, c("value.activity", "value.popularity")],
sender_effects = c(0.8, -0.2),
allow_loops = FALSE
)
head(events)
#> sender receiver time
#> 1 s1 r2 0.1380645
#> 2 s2 r1 0.2342308
#> 3 s3 r2 0.2541056
#> 4 s1 r2 0.2999936
#> 5 s3 r2 0.5930391
#> 6 s3 r1 0.6676661
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