Description Usage Arguments Details Author(s) See Also Examples
Calculate the endogenous network statistic inertia
for relational event models. inertia
measures the tendency for events to consist of the same sender and target (i.e. repeated events).
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data 
A data frame containing all the variables. 
time 
Numeric variable that represents the event sequence. The variable has to be sorted in ascending order. 
sender 
A string (or factor or numeric) variable that represents the sender of the event. 
target 
A string (or factor or numeric) variable that represents the target of the event. 
halflife 
A numeric value that is used in the decay function. The vector of past events is weighted by an exponential decay function using the specified halflife. The halflife parameter determins after how long a period the event weight should be halved. E.g. if 
weight 
An optional numeric variable that represents the weight of each event. If 
eventtypevar 
An optional variable that represents the type of the event. Use 
eventtypevalue 
An optional value (or set of values) used to specify how paste events should be filtered depending on their type.

eventfiltervar 
An optional numeric/character/or factor variable for each event. If 
eventfiltervalue 
An optional character string that represents the value for which past events should be filtered. To filter the current events, use 
eventvar 
An optional dummy variable with 0 values for nullevents and 1 values for true events. If the 
variablename 
An optional value (or values) with the name the inertia statistic variable should be given. To be used if 
returnData 

showprogressbar 

inParallel 

cluster 
An optional numeric or character value that defines the cluster. By specifying a single number, the cluster option uses the provided number of nodes to parallellize. By specifying a cluster using the 
The inertiaStat()
function calculates an endogenous statistic that measures whether events have a tendency to be repeated with the same sender and target over the entire event sequence.
The effect is calculated as follows.
G_t = G_t(E) = (A, B, w_t),
G_t represents the network of past events and includes all events E. These events consist each of a sender a in A and a target b in B and a weight function w_t:
w_t(i, j) = ∑_{e:a = i, b = j}  w_e  * exp^{(tt_e)* (ln(2)/T_{1/2})} * (ln(2)/T_{1/2}),
where w_e is the event weight (usually a constant set to 1 for each event), t is the current event time, t_e is the past event time and T_{1/2} is a halflife parameter.
For the inertia effect, the past events G_t are filtered to include only events where the senders and targets are identical to the current sender and target.
inertia(G_t , a , b) = w_t(a, b)
An exponential decay function is used to model the effect of time on the endogenous statistics. Each past event that contains the same sender and target and fulfills additional filtering options specivied via event type or event attributes is weighted with an exponential decay. The further apart the past event is from the present event, the less weight is given to this event. The halflife parameter in the inertiaStat()
function determins at which rate the weights of past events should be reduced.
The eventfiltervar
 and eventtypevar
options help filter the past events more specifically. How they are filtered depends on the eventfiltervalue
 and eventtypevalue
option.
Laurence Brandenberger [email protected]
rempackage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81  # create some data with 'sender', 'target' and a 'time'variable
# (Note: Data used here are random events from the Correlates of War Project)
sender < c('TUN', 'NIR', 'NIR', 'TUR', 'TUR', 'USA', 'URU',
'IRQ', 'MOR', 'BEL', 'EEC', 'USA', 'IRN', 'IRN',
'USA', 'AFG', 'ETH', 'USA', 'SAU', 'IRN', 'IRN',
'ROM', 'USA', 'USA', 'PAN', 'USA', 'USA', 'YEM',
'SYR', 'AFG', 'NAT', 'NAT', 'USA')
target < c('BNG', 'ZAM', 'JAM', 'SAU', 'MOM', 'CHN', 'IRQ',
'AFG', 'AFG', 'EEC', 'BEL', 'ITA', 'RUS', 'UNK',
'IRN', 'RUS', 'AFG', 'ISR', 'ARB', 'USA', 'USA',
'USA', 'AFG', 'IRN', 'IRN', 'IRN', 'AFG', 'PAL',
'ARB', 'USA', 'EEC', 'BEL', 'PAK')
time < c('800107', '800107', '800107', '800109', '800109',
'800109', '800111', '800111', '800111', '800113',
'800113', '800113', '800114', '800114', '800114',
'800116', '800116', '800116', '800119', '800119',
'800119', '800122', '800122', '800122', '800124',
'800125', '800125', '800127', '800127', '800127',
'800204', '800204', '800204')
type < sample(c('cooperation', 'conflict'), 33,
replace = TRUE)
# combine them into a data.frame
dt < data.frame(sender, target, time, type)
# create event sequence and order the data
dt < eventSequence(datevar = dt$time, dateformat = "%y%m%d",
data = dt, type = "continuous",
byTime = "daily", returnData = TRUE,
sortData = TRUE)
# create counting process data set (with nullevents)  conditional logit setting
dts < createRemDataset(dt, dt$sender, dt$target,
dt$event.seq.cont, eventAttribute = dt$type,
atEventTimesOnly = TRUE, untilEventOccurrs = TRUE,
returnInputData = TRUE)
## divide up the results: counting process data = 1, original data = 2
dtrem < dts[[1]]
dt < dts[[2]]
## merge all necessary event attribute variables back in
dtrem$type < dt$type[match(dtrem$eventID, dt$eventID)]
# manually sort the data set
dtrem < dtrem[order(dtrem$eventTime), ]
# manually sort the data set
dtrem < dtrem[order(dtrem$eventTime), ]
# calculate inertia statistics
dtrem$inertia < inertiaStat(data = dtrem, time = dtrem$eventTime,
sender = dtrem$sender, target = dtrem$target,
eventvar = dtrem$eventDummy,
halflife = 2, returnData = FALSE,
showprogressbar = FALSE)
# plot inertia over time
library("ggplot2")
ggplot(dtrem, aes ( eventTime, inertia,
group = factor(eventDummy), color = factor(eventDummy)) ) +
geom_point() + geom_smooth()
# inertia with typematch (e.g. for 'cooperation' events only count
# past 'cooperation' events)
dtrem$inertia.tm < inertiaStat(data = dtrem, time = dtrem$eventTime,
sender = dtrem$sender, target = dtrem$target,
eventvar = dtrem$eventDummy,
halflife = 2,
eventtypevar = dtrem$type,
eventtypevalue = "valuematch",
returnData = FALSE,
showprogressbar = FALSE)
# inertia with valuemix: for each combination of types
# in the eventtypevar, create a variable
dtrem < inertiaStat(data = dtrem, time = dtrem$eventTime,
sender = dtrem$sender, target = dtrem$target,
eventvar = dtrem$eventDummy,
halflife = 2,
eventtypevar = dtrem$type,
eventtypevalue = "valuemix",
returnData = TRUE,
showprogressbar = FALSE)

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