remstats_recency: Compute Butts' (2008) Recency Network Statistic for Event...
In dream: Dynamic Relational Event Analysis and Modeling
View source: R/rem_stat_recency.R
remstats_recency R Documentation
Compute Butts' (2008) Recency Network Statistic for Event Dyads in a Relational Event Sequence
Description
![[Stable]](../help/figures/lifecycle-stable.svg)
This function computes the recency network sufficient statistic for a relational
event sequence (see Butts 2008; Vu et al. 2015; Meijerink-Bosman et al. 2022). The
recency statistic captures the tendency for more recent events (i.e., an exchange
between two medical doctors) are more likely to re-occur in comparison to events
that happened in the more distant past (see Butts 2008 for a discussion). This
measure allows for recency scores to be only computed for the sampled events,
while computing the statistics based on the full event sequence.
Usage
remstats_recency(
time,
sender,
receiver,
sampled,
observed,
type = c("raw.diff", "inv.diff.plus1", "rank.ordered.count"),
i_neighborhood = TRUE,
dependency = FALSE,
relationalTimeSpan = NULL,
nopastEvents = NA
)
Arguments
time
The vector of event times from the post-processing event sequence.
sender
The vector of event senders from the post-processing event sequence.
receiver
The vector of event receivers from the post-processing event sequence
sampled
A vector for the post-processing event sequence where i is equal to 1 if the observed dyadic event is sampled and 0 if not.
observed
A vector for the post-processing event sequence where i is equal to 1 if the dyadic event is observed and 0 if not.
type
A string value that specifies which recency formula will be used to compute the statistics. The options are "raw.diff", "inv.diff.plus1", "rank.ordered.count" (see details section).
i_neighborhood
TRUE/FALSE. TRUE indicates that the recency statistic will be computed in reference to the sender’s past relational history (see details section). FALSE indicates that the recency statistic will be computed in reference to the target’s past relational history (see details section). Set to TRUE by default.
dependency
TRUE/FALSE. TRUE indicates that temporal relevancy will be modeled (see details section). FALSE indicates that temporal relevancy will not be modeled, that is, all past events are relevant (see details section). Set to FALSE by default.
relationalTimeSpan
If dependency = TRUE, a numerical value that corresponds to the temporal span for relational relevancy, which must be the same measurement unit as the observed_time and processed_time objects. When dependency = TRUE, the relevant events are events that have occurred between current event time, t, and t - relationalTimeSpan. For example, if the time measurement is the number of days since the first event and the value for relationalTimeSpan is set to 10, then only those events which occurred in the past 10 days are included in the computation of the statistic.
nopastEvents
The numerical value that specifies what value should be given to events in which the sender was not active as a sender in the past (i’s neighborhood when i_neighborhood = TRUE) or was not the recipient of a past event (j’s neighborhood when i_neighborhood = FALSE). Set to NA by default.
Details
This function calculates the recency network sufficient statistic for a relational event based on
Butts (2008), Vu et al. (2015), or Meijerink-Bosman et al. (2022).
Depending on the type and neighborhood requested, different formulas will be used.
In the below equations, when i_neighborhood is TRUE:
t^{*} = max(t \in \left\{(s',r',t') \in E : s'= s \land r'= r \land t'<t \right\})
When i_neighborhood is FALSE, the following formula is used:
t^{*} = max(t \in \left\{(s',r',t') \in E : s'= r \land r'= s \land t'<t \right\})
The formula for recency for event e_i with type set to "raw.diff" and i_neighborhood is TRUE (Vu et al. 2015):
recency_{e_i} = t_{e_i} - t^{*}
where t^{*}, is the most recent time in
which the past event has the same receiver and sender as the current event. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "raw.diff" and i_neighborhood is FALSE (Vu et al. 2015):
recency_{e_i} = t_{e_i} - t^{*}
where t^{*}, is the most recent time in
which the past event's sender is the current event receiver and the past event receiver is the current event sender. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "inv.diff.plus1" and i_neighborhood is TRUE (Meijerink-Bosman et al. 2022):
recency_{e_i} =\frac{1}{t_{e_i} - t^{*} + 1}
where t^{*}, is the most recent time in
which the past event has the same receiver and sender as the current event. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "inv.diff.plus1" and i_neighborhood is FALSE (Meijerink-Bosman et al. 2022):
recency_{e_i} = \frac{1}{t_{e_i} - t^{*} + 1}
where t^{*}, is the most recent time in
which the past event's sender is the current event receiver and the past event receiver is the current event sender. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "rank.ordered.count" and i_neighborhood is TRUE (Butts 2008):
recency_{e_i} = \rho(s(e_i), r(e_i), A_t)^{-1}
where \rho(s(e_i), r(e_i), A_t) , is the current event receiver's rank amongst the current sender's recent relational events. That is, as Butts (2008: 174) argues,
"\rho(s(e_i), r(e_i), A_t) is j’s recency rank among i’s in-neighborhood. Thus, if j is the last person to have called i, then \rho(s(e_i), r(e_i), A_t)^{-1} = 1. This falls to 1/2 if j is the second
most recent person to call i, 1/3 if j is the third most recent person, etc." Moreover, if j is not in i's neighborhood, the value defaults to infinity. If there are no past events with the current sender, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "rank.ordered.count" and i_neighborhood is FALSE (Butts 2008):
recency_{e_i} = \rho(r(e_i), s(e_i), A_t)^{-1}
where \rho(r(e_i), s(e_i), A_t) , is the current event sender's rank amongst the current receiver's recent relational events. That is, this measure is the same as above
where the dyadic pair is flipped for the past relational events. Moreover, if j is not in i's neighborhood, the value defaults to infinity. If there are no past events with the current sender, then
the value defaults to the nopastEvents argument.
Finally, researchers interested in modeling temporal relevancy (see Quintane, Mood, Dunn, and Falzone 2022) can specify the relational time span, that is, length of time for which events are considered
relationally relevant. This should be specified via the option relationalTimeSpan with dependency set to TRUE.
Value
The vector of recency network statistics for the relational event sequence.
Author(s)
Kevin A. Carson kacarson@arizona.edu, Diego F. Leal dflc@arizona.edu
References
Butts, Carter T. 2008. "A relational event framework for social action." Sociological Methodology 38(1): 155-200.
Meijerink-Bosman, Marlyne, Roger Leenders, and Joris Mulder. 2022. "Dynamic relational event modeling: Testing, exploring,
and applying." PLOS One 17(8): e0272309.
Quintane, Eric, Martin Wood, John Dunn, and Lucia Falzon. 2022. “Temporal
Brokering: A Measure of Brokerage as a Behavioral Process.” Organizational Research Methods
25(3): 459-489.
Vu, Duy, Philippa Pattison, and Garry Robbins. 2015. "Relational event models for social learning in MOOCs." Social Networks 43: 121-135.
Examples
# A Dummy One-Mode Event Dataset
events <- data.frame(time = 1:18,
eventID = 1:18,
sender = c("A", "B", "C",
"A", "D", "E",
"F", "B", "A",
"F", "D", "B",
"G", "B", "D",
"H", "A", "D"),
target = c("B", "C", "D",
"E", "A", "F",
"D", "A", "C",
"G", "B", "C",
"H", "J", "A",
"F", "C", "B"))
# Creating the Post-Processing Event Dataset with Null Events
eventSet <- create_riskset(type = "one-mode",
time = events$time,
eventID = events$eventID,
sender = events$sender,
receiver = events$target,
p_samplingobserved = 1.00,
n_controls = 6,
seed = 9999)
#Computing the recency statistics (with raw time difference) for the relational event sequence
eventSet$recency_rawdiff <- remstats_recency(
time = as.numeric(eventSet$time),
observed = eventSet$observed,
sampled = rep(1,nrow(eventSet)),
sender = eventSet$sender,
receiver = eventSet$receiver,
type = "raw.diff")
#Computing the recency statistics (with inverse of time difference) for the
#relational event sequence
eventSet$recency_rawdiff <- remstats_recency(
time = as.numeric(eventSet$time),
observed = eventSet$observed,
sampled = rep(1,nrow(eventSet)),
sender = eventSet$sender,
receiver = eventSet$receiver,
type = "inv.diff.plus1")
#Computing the rank-based recency statistics for the relational event sequence
eventSet$recency_rawdiff <- remstats_recency(
time = as.numeric(eventSet$time),
observed = eventSet$observed,
sampled = rep(1,nrow(eventSet)),
sender = eventSet$sender,
receiver = eventSet$receiver,
type = "rank.ordered.count")
dream documentation built on Jan. 21, 2026, 1:06 a.m.
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View source: R/rem_stat_recency.R
| remstats_recency | R Documentation |
Compute Butts' (2008) Recency Network Statistic for Event Dyads in a Relational Event Sequence
Description
This function computes the recency network sufficient statistic for a relational event sequence (see Butts 2008; Vu et al. 2015; Meijerink-Bosman et al. 2022). The recency statistic captures the tendency for more recent events (i.e., an exchange between two medical doctors) are more likely to re-occur in comparison to events that happened in the more distant past (see Butts 2008 for a discussion). This measure allows for recency scores to be only computed for the sampled events, while computing the statistics based on the full event sequence.
Usage
remstats_recency(
time,
sender,
receiver,
sampled,
observed,
type = c("raw.diff", "inv.diff.plus1", "rank.ordered.count"),
i_neighborhood = TRUE,
dependency = FALSE,
relationalTimeSpan = NULL,
nopastEvents = NA
)
Arguments
time |
The vector of event times from the post-processing event sequence. |
sender |
The vector of event senders from the post-processing event sequence. |
receiver |
The vector of event receivers from the post-processing event sequence |
sampled |
A vector for the post-processing event sequence where i is equal to 1 if the observed dyadic event is sampled and 0 if not. |
observed |
A vector for the post-processing event sequence where i is equal to 1 if the dyadic event is observed and 0 if not. |
type |
A string value that specifies which recency formula will be used to compute the statistics. The options are "raw.diff", "inv.diff.plus1", "rank.ordered.count" (see details section). |
i_neighborhood |
TRUE/FALSE. TRUE indicates that the recency statistic will be computed in reference to the sender’s past relational history (see details section). FALSE indicates that the recency statistic will be computed in reference to the target’s past relational history (see details section). Set to TRUE by default. |
dependency |
TRUE/FALSE. TRUE indicates that temporal relevancy will be modeled (see details section). FALSE indicates that temporal relevancy will not be modeled, that is, all past events are relevant (see details section). Set to FALSE by default. |
relationalTimeSpan |
If dependency = TRUE, a numerical value that corresponds to the temporal span for relational relevancy, which must be the same measurement unit as the observed_time and processed_time objects. When dependency = TRUE, the relevant events are events that have occurred between current event time, t, and t - relationalTimeSpan. For example, if the time measurement is the number of days since the first event and the value for relationalTimeSpan is set to 10, then only those events which occurred in the past 10 days are included in the computation of the statistic. |
nopastEvents |
The numerical value that specifies what value should be given to events in which the sender was not active as a sender in the past (i’s neighborhood when i_neighborhood = TRUE) or was not the recipient of a past event (j’s neighborhood when i_neighborhood = FALSE). Set to NA by default. |
Details
This function calculates the recency network sufficient statistic for a relational event based on Butts (2008), Vu et al. (2015), or Meijerink-Bosman et al. (2022). Depending on the type and neighborhood requested, different formulas will be used.
In the below equations, when i_neighborhood is TRUE:
t^{*} = max(t \in \left\{(s',r',t') \in E : s'= s \land r'= r \land t'<t \right\})
When i_neighborhood is FALSE, the following formula is used:
t^{*} = max(t \in \left\{(s',r',t') \in E : s'= r \land r'= s \land t'<t \right\})
The formula for recency for event e_i with type set to "raw.diff" and i_neighborhood is TRUE (Vu et al. 2015):
recency_{e_i} = t_{e_i} - t^{*}
where t^{*}, is the most recent time in
which the past event has the same receiver and sender as the current event. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "raw.diff" and i_neighborhood is FALSE (Vu et al. 2015):
recency_{e_i} = t_{e_i} - t^{*}
where t^{*}, is the most recent time in
which the past event's sender is the current event receiver and the past event receiver is the current event sender. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "inv.diff.plus1" and i_neighborhood is TRUE (Meijerink-Bosman et al. 2022):
recency_{e_i} =\frac{1}{t_{e_i} - t^{*} + 1}
where t^{*}, is the most recent time in
which the past event has the same receiver and sender as the current event. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "inv.diff.plus1" and i_neighborhood is FALSE (Meijerink-Bosman et al. 2022):
recency_{e_i} = \frac{1}{t_{e_i} - t^{*} + 1}
where t^{*}, is the most recent time in
which the past event's sender is the current event receiver and the past event receiver is the current event sender. If there are no past events within the current dyad, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "rank.ordered.count" and i_neighborhood is TRUE (Butts 2008):
recency_{e_i} = \rho(s(e_i), r(e_i), A_t)^{-1}
where \rho(s(e_i), r(e_i), A_t) , is the current event receiver's rank amongst the current sender's recent relational events. That is, as Butts (2008: 174) argues,
"\rho(s(e_i), r(e_i), A_t) is j’s recency rank among i’s in-neighborhood. Thus, if j is the last person to have called i, then \rho(s(e_i), r(e_i), A_t)^{-1} = 1. This falls to 1/2 if j is the second
most recent person to call i, 1/3 if j is the third most recent person, etc." Moreover, if j is not in i's neighborhood, the value defaults to infinity. If there are no past events with the current sender, then
the value defaults to the nopastEvents argument.
The formula for recency for event e_i with type set to "rank.ordered.count" and i_neighborhood is FALSE (Butts 2008):
recency_{e_i} = \rho(r(e_i), s(e_i), A_t)^{-1}
where \rho(r(e_i), s(e_i), A_t) , is the current event sender's rank amongst the current receiver's recent relational events. That is, this measure is the same as above
where the dyadic pair is flipped for the past relational events. Moreover, if j is not in i's neighborhood, the value defaults to infinity. If there are no past events with the current sender, then
the value defaults to the nopastEvents argument.
Finally, researchers interested in modeling temporal relevancy (see Quintane, Mood, Dunn, and Falzone 2022) can specify the relational time span, that is, length of time for which events are considered relationally relevant. This should be specified via the option relationalTimeSpan with dependency set to TRUE.
Value
The vector of recency network statistics for the relational event sequence.
Author(s)
Kevin A. Carson kacarson@arizona.edu, Diego F. Leal dflc@arizona.edu
References
Butts, Carter T. 2008. "A relational event framework for social action." Sociological Methodology 38(1): 155-200.
Meijerink-Bosman, Marlyne, Roger Leenders, and Joris Mulder. 2022. "Dynamic relational event modeling: Testing, exploring, and applying." PLOS One 17(8): e0272309.
Quintane, Eric, Martin Wood, John Dunn, and Lucia Falzon. 2022. “Temporal Brokering: A Measure of Brokerage as a Behavioral Process.” Organizational Research Methods 25(3): 459-489.
Vu, Duy, Philippa Pattison, and Garry Robbins. 2015. "Relational event models for social learning in MOOCs." Social Networks 43: 121-135.
Examples
# A Dummy One-Mode Event Dataset
events <- data.frame(time = 1:18,
eventID = 1:18,
sender = c("A", "B", "C",
"A", "D", "E",
"F", "B", "A",
"F", "D", "B",
"G", "B", "D",
"H", "A", "D"),
target = c("B", "C", "D",
"E", "A", "F",
"D", "A", "C",
"G", "B", "C",
"H", "J", "A",
"F", "C", "B"))
# Creating the Post-Processing Event Dataset with Null Events
eventSet <- create_riskset(type = "one-mode",
time = events$time,
eventID = events$eventID,
sender = events$sender,
receiver = events$target,
p_samplingobserved = 1.00,
n_controls = 6,
seed = 9999)
#Computing the recency statistics (with raw time difference) for the relational event sequence
eventSet$recency_rawdiff <- remstats_recency(
time = as.numeric(eventSet$time),
observed = eventSet$observed,
sampled = rep(1,nrow(eventSet)),
sender = eventSet$sender,
receiver = eventSet$receiver,
type = "raw.diff")
#Computing the recency statistics (with inverse of time difference) for the
#relational event sequence
eventSet$recency_rawdiff <- remstats_recency(
time = as.numeric(eventSet$time),
observed = eventSet$observed,
sampled = rep(1,nrow(eventSet)),
sender = eventSet$sender,
receiver = eventSet$receiver,
type = "inv.diff.plus1")
#Computing the rank-based recency statistics for the relational event sequence
eventSet$recency_rawdiff <- remstats_recency(
time = as.numeric(eventSet$time),
observed = eventSet$observed,
sampled = rep(1,nrow(eventSet)),
sender = eventSet$sender,
receiver = eventSet$receiver,
type = "rank.ordered.count")
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