inertiaStat: Calculate inertia statistics

In rem: Relational Event Models (REM)

Description

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).

Usage

inertiaStat(data, time, sender, target, halflife,
    weight = NULL, 
    eventtypevar = NULL,
    eventtypevalue = "valuematch", 
    eventfiltervar = NULL,
    eventfiltervalue = NULL, 
    eventvar = NULL,
    variablename = "inertia", 
    returnData = FALSE,
    showprogressbar = FALSE, 
    inParallel = FALSE, cluster = NULL)

Arguments

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 halflife = 5, the weight of an event that occured 5 units in the past is halved. Smaller halflife values give more importance to more recent events, while larger halflife values should be used if time does not affect the sequence of events that much.
weight	An optional numeric variable that represents the weight of each event. If weight = NULL each event is given an event weight of 1.
eventtypevar	An optional variable that represents the type of the event. Use eventtypevalue to specify how the eventtypevar should be used to filter past events.
eventtypevalue	An optional value (or set of values) used to specify how paste events should be filtered depending on their type. eventtypevalue = "valuematch" indicates that only past events that have the same type as the current event should be used to calculate the inertia statistic. eventtypevalue = "valuemix" indicates that past and present events of specific types should be used for the inertia statistic. All the possible combinations of the eventtypevar-values will be used. E.g. if eventtypevar contains two unique values "a" and "b", 4 inertia statistics will be calculated. The first variable calculates the inertia effect where the present event is of type "a" and all the past events are of type "b". The next variable calculates inertia for present events of type "b" and past events of type "a". Additionally, a variable is calculated, where present events as well as past events are of type "a" and a fourth variable calculates inertia for events with type "b" (i.e. valuematch on value "b"). eventtypevalue = c(.., ..) is similar to the "nodmix"-option, all different combinations of the values specified in eventtypevalue are used to create inertia statistics.
eventfiltervar	An optional numeric/character/or factor variable for each event. If eventfiltervar is specified, eventfiltervalue has to be provided as well.
eventfiltervalue	An optional character string that represents the value for which past events should be filtered. To filter the current events, use eventtypevar.
eventvar	An optional dummy variable with 0 values for null-events and 1 values for true events. If the data is in the form of counting process data, use the eventvar-option to specify which variable contains the 0/1-dummy for event occurrence. If this variable is not specified, all events in the past will be considered for the calulation of the inertia statistic, regardless if they occurred or not (= are null-events).
variablename	An optional value (or values) with the name the inertia statistic variable should be given. To be used if returnData = TRUE or multiple inertia statistics are calculated.
returnData	TRUE/FALSE. Set to FALSE by default. The new variable(s) are bound directly to the data.frame provided and the data frame is returned in full.
showprogressbar	TRUE/FALSE. Can only be set to TRUE if the function is not run in parallel.
inParallel	TRUE/FALSE. An optional boolean to specify if the loop should be run in parallel.
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 makeCluster-command in the doParallel-package, the loop can be run on multiple nodes/cores. E.g., cluster = makeCluster(12, type="FORK").

Details

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^{-(t-t_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.

Author(s)

Laurence Brandenberger [e-mail]

See Also

rem-package

Examples

# 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 null-events) - 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)

Example output

`geom_smooth()` using method = 'loess' and formula 'y ~ x'

rem documentation built on May 2, 2019, 3:25 a.m.