Specifies the statistic for a 'totaldegreeDyad' effect.
Usage
totaldegreeDyad(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 two times 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
The 'totaldegreeDyad' effect refers to the tendency of pairs of actors (dyads) to increase their interaction rate as the total degree (number of interactions) of both actors in the pair goes up. To calculate this effect for a specific pair (i,j) at a given timepoint (t), we sum the degrees of the two actors in the dyad (i,j).
Additionally, there is an optional scaling method, which can be chosen using the 'scaling' method. When the 'prop' scaling method is applied, the degree count is divided by two times the total number of past events. This scaling converts the statistic into a fraction, representing the proportion of past events in which at least one actor in the dyad was involved. For the first timepoint, where no events have previously occurred, it is assumed that each actor is equally likely to be involved in an event. In this case, the statistic is set to 1 divided by the total number of actors (N).
The totaldegreeDyad effect is defined for the tie-oriented model and is applicable to both directed and undirected events.
Examples
reh_tie <- remify::remify(history, model = "tie")
effects <- ~ totaldegreeDyad()
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: totaldegreeDyad