osp

Specifies the statistic for an outgoing shared partners effect.

Usage

osp(unique = FALSE, scaling = c("none", "std"), consider_type = TRUE)

Arguments

unique

A logical value indicating whether to sum the minimum of events with third actors (FALSE, default) or the number of third actors that create a new, unique shared partner (TRUE). See details for more information.

scaling

the method for scaling the triad statistic. Default is to not scale the statistic but keep the raw 'counts'. Alternatively, standardization of the raw counts per time point can be requested with 'std'.

consider_type

logical, indicates whether to count the shared partners separately for each event type (TRUE, default) or sum across different event types (FALSE).

Value

List with all information required by `remstats::remstats()` to compute the statistic.

Details

The outgoing shared partners effect describes the propensity of dyads to interact based on the number of past outgoing shared partners between them. By default, the statistic at timepoint t for the dyad (i,j) is computed as the sum of the minimum occurrences of past (i,h) and (j,h) events across all actors h.

When the unique parameter is set to TRUE, a different approach is taken. In this case, the statistic counts the number of actors h that contribute to the creation of a new, distinct shared partner between actors i and j.

Additionally, it is possible to specify a scaling method using the scaling parameter.

Please note that the outgoing shared partners effect, 'osp', is exclusively defined for directed events.

References

Butts, C. (2008). A relational event framework for social action. Sociological Methodology.

See also

otp, itp, or isp for other types of triadic effects for directed relational events and sp for triadic effects for undirected relational events.

Examples

reh_tie <- remify::remify(history, model = "tie")
effects <- ~ osp()
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: osp

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: osp