remstats: remstats

In remstats: Computes Statistics for Relational Event History Data

remstats

Description

Computes statistics for modeling relational events with a tie-oriented or actor-oriented approach.

Usage

remstats(
  reh,
  tie_effects = NULL,
  sender_effects = NULL,
  receiver_effects = NULL,
  start_effects = NULL,
  end_effects = NULL,
  memory = c("full", "window", "decay", "interval"),
  memory_value = NA,
  psi_start = 1,
  psi_end = 1,
  first = 2,
  last = Inf,
  display_progress = FALSE,
  sampling = FALSE,
  samp_num = 10L,
  seed = NULL,
  attr_actors = NULL,
  attr_dyads = NULL
)

Arguments

reh	an object of class "remify" characterizing the relational event history. May also be a remify_durem object for duration relational event models.
tie_effects	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the effects in the tie-oriented model for which statistics are computed, see 'Details' for the available effects and their corresponding statistics
sender_effects	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the effects in the sender activity rate step of the actor-oriented model for which statistics are computed, see ‘Details’
receiver_effects	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the effects in the receiver choice step of model for which statistics are computed, see ‘Details’
start_effects	Formula for the start sub-model statistics. Only used when reh is a remify_durem object (i.e. when remify(..., duration = TRUE) was called). Equivalent to tie_effects but applied to the start process. Only supported for the tie-oriented model.
end_effects	Formula for the end sub-model statistics. Only used when reh is a remify_durem object. Only supported for the tie-oriented model.
memory	The memory to be used. See ‘Details’.
memory_value	Numeric value indicating the memory parameter. Default is NA, which is only valid for memory = "full" (no memory parameter required). See ‘Details’.
psi_start	Numeric. Duration exponent for start-model history weighting. The weight of each past event in the start statistics is event_weight * (end - time + 1)^psi_start. Default 1. Only used when reh is a remify_durem object.
psi_end	Numeric. Duration exponent for end-model history weighting. The weight of each past event in the end statistics is event_weight * (end - time + 1)^psi_end. Default 1. Only used when reh is a remify_durem object.
first	an optional integer value, specifying the index of the first unique time point event in the relational event history for which statistics must be computed (see 'Details'). Default is 2: the first event has no history and is used only to initialize statistics, not to fit the model.
last	an optional integer value, specifying the index of the last unique time point in the relational event history for which statistics must be computed (see 'Details')
display_progress	should a progress bar for the computation of the endogenous statistics be shown (TRUE) or not (FALSE)?
sampling	Logical. If TRUE, statistics are computed using case–control (dyad) sampling rather than the full risk set. Default FALSE. Only supported for a tie model.
samp_num	Integer. Number of dyads to include per event when sampling = TRUE. Must be smaller than or equal to the size of the active risk set. Ignored when sampling = FALSE. Only supported for a tie model.
seed	Optional integer. Random seed used for dyad sampling. Setting this ensures reproducible sampling across calls. If NULL, the current RNG state is used.
attr_actors	optionally, an object of class "data.frame" that contains exogenous attributes for actors (see Details).
attr_dyads	optionally, an object of class data.frame or matrix containing attribute information for dyads (see Details).

Value

An object of class 'remstats'. In case of the tie-oriented model, an array with the computed statistics, where rows refer to time points, columns refer to potential relational event (i.e., potential edges) in the risk set and slices refer to statistics. In case of the actor-oriented model, list with in the first element the statistics for the sender activity rate step and in the second element the statistics for the receiver choice step, where rows refer to time points, columns refer to potential senders or receivers, respectively. Statistics are computed once per unique time point (per-timepoint "pt" method), so the number of rows in the output equals reh$M (the number of unique time points), which may be less than the total number of observed events when simultaneous events are present. The 'remstats' object has the following attributes:

model

Type of model that is estimated, obtained from the remify object inputted to 'reh'.

formula

Model formula, obtained from the formula inputted to 'tie_effects', 'sender_effects' and/or 'receiver_effects', depending on the model.

riskset

For the tie-oriented model, the risk set used to construct the statistics.

actors

For the actor-oriented model, the set of actors used to construct the statistics, obtained from the remify object inputted to 'reh'.

Effects

The statistics to be computed are defined symbolically and should be supplied to the tie_effects (for the tie-oriented model), sender_effects and/or receiver_effects (for the actor-oriented model) argument in the form ~ effects. In case of events with a duration (where reh is a remify_durem object, created with remify(..., duration = TRUE)), statistics should instead be supplied to start_effects and end_effects; note that events with a duration are only supported for the tie-oriented model. The statistics terms are separated by + operators. For example: effects = ~ inertia() + otp(). Interactions between two effects can be included with * or : operators. For example: effects = ~ inertia():otp(). A list of the available effects can be obtained with tie_effects() (tie-oriented model), actor_effects() (actor-oriented model), and, for models of events with a duration, active_effects() (statistics that depend on which actors or dyads are currently active).

The majority of the statistics can be scaled in some way, see the documentation of the scaling argument in the separate effect functions for more information on this.

The majority of the statistics can account for the event type included as a dependent variable, see the documentation of the consider_type argument in the separate effect functions for more information on this. Note that this option is only available for the tie-oriented model.

Note that events in the relational event history can be directed or undirected. Some statistics are only defined for either directed or undirected events (see the documentation of the statistics). Note that undirected events are only available for the tie-oriented model.

Memory

The default 'memory' setting is '"full"', which implies that at each time point $t$ the entire event history before $t$ is included in the computation of the statistics. Alternatively, when 'memory' is set to '"window"', only the past event history within a given time window is considered (see Mulders & Leenders, 2019). This length of this time window is set by the 'memory_value' parameter. For example, when 'memory_value = 100' and 'memory = "window"', at time point $t$ only the past events that happened at most 100 time units ago are included in the computation of the statistics. A third option is to set 'memory' to '"interval"'. In this case, the past event history within a given time interval is considered. For example, when '"memory_value" = c(50, 100)' and 'memory = "interval"', at time point $t$ only the past events that happened between 50 and 100 time units ago are included in the computation of the statistics. Finally, the fourth option is to set 'memory' to '"decay"'. In this case, the weight of the past event in the computation of the statistics depend on the elapsed time between $t$ and the past event. This weight is determined based on an exponential decay function with half-life parameter 'memory_value' (see Brandes et al., 2009).

Event weights

Note that if the relational event history contains a column that is named “weight”, it is assumed that these affect the endogenous statistics. These affect the computation of all endogenous statistics with a few exceptions that follow logically from their definition (e.g., the recenyContinue statistic does depend on time since the event and not on event weights).

Subset the event history using 'first' and 'last'

It is possible to compute statistics for a segment of the relational event sequence, based on the entire event history. This is done by specifying the 'first' and 'last' values as the indices for the first and last event times for which statistics are needed. For instance, setting 'first = 5' and 'last = 5' calculates statistics for the 5th event in the relational event sequence, considering events 1-4 in the history. Note that in cases of simultaneous events 'first' and 'last' refer to indices of unique time points.

References

Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155–200. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1467-9531.2008.00203.x")}, Stadtfeld, C., & Block, P. (2017). Interactions, actors, and time: Dynamic network actor models for relational events. Sociological Science, 4, 318–352. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.15195/v4.a14")}

Examples

library(remstats)

# Tie-oriented model
eff <- ~ inertia():send("extraversion", attr_actors = info) + otp()
reh_tie <- remify::remify(edgelist = history, model = "tie")
remstats(reh = reh_tie, tie_effects = eff)

# Actor-oriented model
seff <- ~ send("extraversion", attr_actors = info)
reff <- ~ receive("agreeableness", attr_actors = info) + inertia() + otp()
reh_actor <- remify::remify(edgelist = history, model = "actor")
remstats(reh = reh_actor, sender_effects = seff, receiver_effects = reff)

# Model for events with a duration (tie-oriented only)
# (the baboons dataset is provided by the 'remdata' package)
if (requireNamespace("remdata", quietly = TRUE)) {
  data(baboons_obs, package = "remdata")
  reh_dur <- remify::remify(baboons_obs$edgelist[1:1000,], model = "tie",
  directed = FALSE, duration = TRUE)
  remstats(reh_dur,
    start_effects = ~ inertia(scaling = "std") +
      activeDegreeDyad(scaling = "std"),
    end_effects = ~ totaldegreeDyad(scaling = "std"),
    first = 50)
}

remstats documentation built on July 15, 2026, 5:07 p.m.