A randomly generated sequence of relational events with 5 actors and 100 events. The event sequence is generated by following an actor-oriented modeling approach (for more information on the algorithm used for the generation, refer to help(topic = remulateActor, package = "remulate") or ?remulate::remulateActor).
Usage
data(ao_data)Format
ao_data is a list object containing the following objects:
edgelista
data.framewith the raw simulated edgelist. The columns of thedata.frameare:timethe timestamp indicating the time at which each event occurred
actor1the actor that generated the relational event
actor2the actor that received the relational event
seedthe seed value used in
remulate::remulateActor()for generating the event sequencetrue.parsa list of two vectors named
"rate_model"and"choice_model", each containing the values of the parameters used in the generation of the event sequence
Examples
# (1) load the data into the workspace
data(ao_data)
# (2) process event sequence with \code{remify}
ao_reh <- remify::remify(edgelist = ao_data$edgelist, model = "actor")
# (3) define linear predictor and claculate stastistcs with \code{remstats} package
## linear predictor for the rate model
rate_model <- ~ 1 + remstats::indegreeSender()
## linear predictror for the choice model
choice_model <- ~ remstats::inertia() + remstats::reciprocity()
## calculate statistics
ao_reh_stats <- remstats::remstats(reh = ao_reh, sender_effects = rate_model,
receiver_effects = choice_model)
# (4) estimate model using method = "MLE" and print out summary
## estimate model
mle_ao <- remstimate::remstimate(reh = ao_reh, stats = ao_reh_stats, method = "MLE")
## print out a summary of the estimation
summary(mle_ao)
#> Relational Event Model (actor oriented)
#>
#> Call rate model **for sender**:
#>
#> ~1 + remstats::indegreeSender()
#>
#>
#> Coefficients rate model (MLE with interval likelihood):
#>
#> Estimate Std. Err z value Pr(>|z|) Pr(=0)
#> baseline -4.8062015 0.1832563 -26.2266709 0.0 <2e-16
#> indegreeSender -0.0083633 0.0159467 -0.5244534 0.6 0.8971
#> Null deviance: 1177.625 on 100 degrees of freedom
#> Residual deviance: 1177.348 on 98 degrees of freedom
#> Chi-square: 0.2770484 on 2 degrees of freedom, asymptotic p-value 0.8706422
#> AIC: 1181.348 AICC: 1181.472 BIC: 1186.558
#> --------------------------------------------------------------------------------
#>
#> Call choice model **for receiver**:
#>
#> ~remstats::inertia() + remstats::reciprocity()
#>
#>
#> Coefficients choice model (MLE with interval likelihood):
#>
#> Estimate Std. Err z value Pr(>|z|) Pr(=0)
#> inertia -0.032508 0.077037 -0.421983 0.6730 0.9015
#> reciprocity 0.018287 0.071630 0.255305 0.7985 0.9064
#> Null deviance: 2.772589 on 100 degrees of freedom
#> Residual deviance: 277.0551 on 98 degrees of freedom
#> Chi-square: -274.2826 on 2 degrees of freedom, asymptotic p-value 1
#> AIC: 281.0551 AICC: 281.1789 BIC: 286.2655