Specifies the statistic for a reciprocity effect in the tie-oriented model or the receiver choice step of the actor-oriented model.
Usage
reciprocity(scaling = c("none", "prop", "std"), consider_type = TRUE)Arguments
- scaling
the method for scaling the reciprocity statistic. Default is to not scale the statistic but keep the raw 'counts'. Alternatively, the statistics can be scaled by 'prop', in which raw counts are divided by the indegree of the sender at time t (see 'details') or standardization of the raw counts per time point can be requested with 'std'.
- consider_type
logical, indicates whether to count the number of past reciprocal events separately for each event type (TRUE, default) or sum across different event types (FALSE).
Details
A reciprocity effect refers to the tendency for actors to reciprocate past interactions. The statistic at timepoint t for dyad (i,j) (tie-oriented model) or receiver j (actor-oriented model) is equal to the number of (j,i) events before timepoint t. Note that a reciprocity effect is only defined for directed events.
Optionally, a scaling method can be set with scaling. By scaling the
reciprocity count by the indegree of the sender, the statistic refers to the
fraction of messages received by actor i that were received from actor j. If
actor i hasn't received any messages yet it can be assumed that actor i is
equally likely to receive a message from every actor and the statistic is
set equal to 1/(n-1), where n refers to the number of actors. The resulting
statistic is similar to the "FrRecSnd" statistic in the R package 'relevent'.
Examples
reh_tie <- remify::remify(history, model = "tie")
effects <- ~ reciprocity()
remstats(reh = reh_tie, tie_effects = effects)
#> Relational Event Network Statistics
#> > Model: tie-oriented
#> > Computation method: per time point
#> > Dimensions: 115 time points x 90 dyads x 2 statistics
#> > Statistics:
#> >> 1: baseline
#> >> 2: reciprocity
reh_actor <- remify::remify(history, model = "actor")
remstats(reh = reh_actor, receiver_effects = effects)
#> Relational Event Network Statistics
#> > Model: actor-oriented
#> > Computation method: per time point
#> > Sender model: empty
#> > Receiver model:
#> >> Dimensions: 115 events x 10 actors x 1 statistics
#> >> Statistics:
#> >>> 1: reciprocity