tomstats — tomstats • remstats

Computes statistics for modeling relational event history data with the tie-oriented relational event model.

Usage

tomstats(
  effects,
  reh,
  attr_actors = NULL,
  attr_dyads = NULL,
  method = c("pt", "pe"),
  memory = c("full", "window", "decay", "interval"),
  memory_value = NA,
  start = 1,
  stop = Inf,
  display_progress = FALSE,
  adjmat = NULL,
  get_adjmat = FALSE,
  attr_data,
  attributes,
  edgelist
)

Arguments

effects: an object of class "formula" (or one that can be coerced to that class): a symbolic description of the effects in the model for which statistics are computed, see 'Details' for the available effects and their corresponding statistics
reh: an object of class "remify" characterizing the relational event history.
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).
method: Specifies the method for managing simultaneous events, i.e., events occurring at the same time. The default 'method' is 'pt' (per timepoint), where statistics are computed once for each unique timepoint in the edgelist. Alternatively, you can choose 'pe' (per event), where statistics are computed once for each unique event observed in the edgelist.
memory: The memory to be used. See `Details'.
memory_value: Numeric value indicating the memory parameter. See `Details'.
start: an optional integer value, specifying the index of the first time or event in the relational event history for which statistics must be computed (see 'Details')
stop: an optional integer value, specifying the index of the last time or event 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)?
adjmat: optionally, a previously computed adjacency matrix with on the rows the time points and on the columns the risk set entries
get_adjmat: whether the adjmat computed by tomstats should be outputted as an attribute of the statistics.
attr_data: deprecated, please use "attr_actors" instead
attributes: deprecated, please use "attr_data" instead
edgelist: deprecated, please use "reh" instead

Value

An object of class 'tomstats'. 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. The 'tomstats' object has the following attributes:

model: Type of model that is estimated.
formula: Model formula, obtained from the formula inputted to 'tie_effects'.
riskset: The risk set used to construct the statistics.
adjmat: [Optional], if "get_adjmat = TRUE", the matrix with the accumulated event weights for each time point (on the rows) and each dyad (in the columns).

Effects

The statistics to be computed are defined symbolically and should be supplied to the effects argument in the form ~ effects. The terms are separated by + operators. For example: effects = ~ inertia() + otp(). Interactions between two effects can be included with * operators. For example: effects = ~ inertia()*otp(). A list of available effects can be obtained with tie_effects().

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 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.

attr_actors

For the computation of the exogenous statistics an attributes object with the exogenous covariate information has to be supplied to the attr_actors argument in either remstats() or in the separate effect functions supplied to the ..._effects arguments (e.g., see send). This attr_actors object should be constructed as follows: A dataframe with rows referring to the attribute value of actor i at timepoint t. A `name` column is required that contains the actor name (corresponding to the actor names in the relational event history). A `time` column is required that contains the time when attributes change (set to zero if none of the attributes vary over time). Subsequent columns contain the attributes that are called in the specifications of exogenous statistics (column name corresponding to the string supplied to the variable argument in the effect function). Note that the procedure for the exogenous effects `tie' and `event' deviates from this, here the exogenous covariate information has to be specified in a different way, see tie and event.

attr_dyads

For the computation of the dyad exogenous statistics with tie(), an attributes object with the exogenous covariates information per dyad has to be supplied. This is a data.frame or matrix containing attribute information for dyads. If attr_dyads is a data.frame, the first two columns should represent "actor1" and "actor2" (for directed events, "actor1" corresponds to the sender, and "actor2" corresponds to the receiver). Additional columns can represent dyads' exogenous attributes. If attributes vary over time, include a column named "time". If attr_dyads is a matrix, the rows correspond to "actor1", columns to "actor2", and cells contain dyads' exogenous attributes.

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 = "window"`, 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 'start' and 'stop'

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 'start' and 'stop' values as the indices for the first and last event times for which statistics are needed. For instance, setting 'start = 5' and 'stop = 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 with the 'method' set to 'pt' (per timepoint), 'start' and 'stop' should correspond to the indices of the first and last unique event timepoints for which statistics are needed. For example, if 'start = 5' and 'stop = 5', statistics are computed for the 5th unique timepoint in the relational event sequence, considering all events occurring at unique timepoints 1-4.

Adjacency matrix

Optionally, a previously computed adjacency matrix can be supplied. Note that the endogenous statistics will be computed based on this adjacency matrix. Hence, supplying a previously computed adjacency matrix can reduce computation time but the user should be absolutely sure the adjacency matrix is accurate.

References

Butts, C. T. (2008). A relational event framework for social action. Sociological Methodology, 38(1), 155–200. doi:10.1111/j.1467-9531.2008.00203.x

Examples

library(remstats)

# Load data
data(history)
data(info)

# Prepare data
reh <- remify::remify(edgelist = history, model = "tie")

# Compute effects
effects <- ~ inertia():send("extraversion") + otp()
tomstats(effects, reh = reh, attr_actors = info)
#> Relational Event Network Statistics
#> > Model: tie-oriented
#> > Computation method: per time point
#> > Dimensions: 115 time points x 90 dyads x 5 statistics
#> > Statistics:
#> 	 >> 1: baseline
#> 	 >> 2: inertia
#> 	 >> 3: send_extraversion
#> 	 >> 4: otp
#> 	 >> 5: inertia:send_extraversion