Specifies the statistic for an `outdegreeSender` effect in the tie-oriented model or the sender activity rate step of the actor-oriented model.
Usage
outdegreeSender(scaling = c("none", "prop", "std"), consider_type = TRUE)Arguments
- scaling
the method for scaling the degree statistic. Default is to not scale the statistic (scaling = "none"). Alternatively, scaling of the raw degree counts by the number of past events at time t can be requested with 'prop' or standardization of the raw degree counts per time point can be requested with 'std'.
- consider_type
logical, indicates whether to count the degrees separately for each event type (TRUE, default) or sum degrees across different event types (FALSE).
Details
An outdegree of the sender effect refers to the tendency for actors to send events if they have send more past events. The statistic at timepoint t for dyad (i,j) (tie-oriented model) or sender i (actor-oriented model) is equal to the number of events send by actor i before timepoint t. Note that the 'outdegreeSender' effect is only defined for directed events.
Optionally, a scaling method can be set with scaling. By scaling the
degree count by the total number of past events, the statistic refers to the
fraction of past events that were send by actor i. At the first time
point, when no events did previously occur, it is assumed that every actor
is equally likely to send a message and the statistic is set equal to 1/n,
where n refers to the number of actors.
See also
indegreeSender, indegreeReceiver,
outdegreeReceiver, totaldegreeSender, or
totaldegreeReceiver for other types of degree effects.
Examples
reh_tie <- remify::remify(history, model = "tie")
effects <- ~ outdegreeSender()
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: outdegreeSender
reh_actor <- remify::remify(history, model = "actor")
remstats(reh = reh_actor, sender_effects = effects)
#> Relational Event Network Statistics
#> > Model: actor-oriented
#> > Computation method: per time point
#> > Sender model:
#> >> Dimensions: 115 time points x 10 actors x 2 statistics
#> >> Statistics:
#> >>> 1: baseline
#> >>> 2: outdegreeSender