triadStat: Calculate triad statistics

Description

Calculate the endogenous network statistic triads that measures the tendency for events to close open triads.

Usage

triadStat(data, time, sender, target, halflife, 
    weight = NULL, 
    eventtypevar = NULL, 
    eventtypevalues = NULL, 
    eventfiltervar = NULL,
    eventfilterAI = NULL, 
    eventfilterBI = NULL,
    eventfilterAB = NULL, 
    eventvar = NULL,
    variablename = 'triad', 
    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 dummy variable that represents the type of the event. Use eventtypevalues to specify how the eventtypevar should be used to filter past events. Specifying the eventtypevar is needed to calculate effects of social balance theory, such as 'friend-of-friend' or 'enemy-of-enemy' statistics.

eventtypevalues

Two string values that represent the type of the past events. The first string value represents the eventtype that exists for all past events that include the current sender (either as sender or target) and a third actor. The second value represents the eventtype for all past events that include the target (either as sender or target) as well as the third actor. An example: Let the eventtypevar indicate whether an event is of cooperative or hostile nature. To test whether the hypothesis 'the friend of my friend is my friend' holds, both eventtypevalues must be the same and point to the cooperative type (e.g. eventtypevalues = c("cooperation", "cooperation")) depending on how the eventtypevar is coded. To test whether the hypothesis 'the friend of my enemy is my enemy' holds, the first value in eventtypevalues represents the hostile event between current sender and a third actor and the second value represents the cooperative event between the third actor and the target. To test the hypothesis 'the enemy of my enemy is my friend', the first value represents the hostile events between current sender and a third actor and the second value represents the hostile event between the current target and the third actor. For the fourth hypothesis, to test social balance theory 'the enemy of my friend is my enemy', the first value represents a cooperative event between the current sender and a third actor and the second value represents a hostile event between the current target and the third actor.

eventfiltervar

An optional string (or factor or numeric) variable that can be used to filter past and current events. Use eventfilterAI, eventfilterBI or eventfilterAB to specify which past events should be filtered and by what value.

eventfilterAI

An optional value used to specify how paste events should be filtered depending on their attribute. Each distinct edge that form a triad can be filtered. eventfilterAI refers to the past event involving the current sender (a) and a third actor (i). eventfilterBIreferrs to past events involving target (b) and the third actor (i). eventfilterAB refers to the current event involving sender (a) and target (b).

eventfilterBI

see eventfilterAI.

eventfilterAB

see eventfilterAI.

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 triad statistic, regardless if they occurred or not (= are null-events).

variablename

An optional value (or values) with the name the triad statistic variable should be given. To be used if returnData = TRUE.

returnData

TRUE/FALSE. Set to FALSE by default. The new variable is 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 triadStat()-function calculates an endogenous statistic that measures whether events have a tendency to form closing triads.

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 $w_t$:

$$ w_t(i, j) = \sum_{e:a = i, b = j} | w_e | \cdot e^{-(t-t_e)\cdot\frac{ln(2)}{T_{1/2}}} \cdot \frac{ln(2)}{T_{1/2}}, $$

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 triad effect, the past events $G_t$ are filtered to include only events where the current event closes an open triad in the past.

$$triad(G_t , a , b) = \sqrt{\sum_{i \in A} w_t(a, i) \cdot w_t(i, b)}$$

An exponential decay function is used to model the effect of time on the endogenous statistics. The further apart the past event is from the present event, the less weight is given to this event. The halflife parameter in the triadStat()-function determines at which rate the weights of past events should be reduced. Therefore, if the one (or more) of the two events in the triad have occurred further in the past, less weight is given to this triad because it becomes less likely that the sender and target actors reacted to each other in the way the triad assumes.

The eventtypevar- and eventattributevar-options help filter the past events more specifically. How they are filtered depends on the eventtypevalue- and eventattributevalue-option.

Examples

Run this code

# NOT RUN {
# 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', 'UNK', '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', 'UNK', 'IRN')
target <- c('BNG', 'RUS', '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', 'IRN', 'CHN')
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)
important <- sample(c('important', 'not important'), 33,
                    replace = TRUE)

# combine them into a data.frame
dt <- data.frame(sender, target, time, type, important)

# 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)
dtrem <- dts[[1]]
dt <- dts[[2]]
# manually sort the data set
dtrem <- dtrem[order(dtrem$eventTime), ]
# merge type-variable back in
dtrem$type <- dt$type[match(dtrem$eventID, dt$eventID)]

# calculate triad statistic
dtrem$triad <- triadStat(data = dtrem, time = dtrem$eventTime, 
                      sender = dtrem$sender, target = dtrem$target, 
                      eventvar = dtrem$eventDummy,
                      halflife = 2)

# calculate friend-of-friend statistic
dtrem$triad.fof <- triadStat(data = dtrem, time = dtrem$eventTime, 
                          sender = dtrem$sender, target = dtrem$target, 
                          halflife = 2, eventtypevar = dtrem$type, 
                          eventtypevalues = c("cooperation",
                                              "cooperation"), 
                          eventvar = dtrem$eventDummy)

# calculate friend-of-enemy statistic
dtrem$triad.foe <- triadStat(data = dtrem, time = dtrem$eventTime, 
                             sender = dtrem$sender, target = dtrem$target, 
                             halflife = 2, eventtypevar = dtrem$type, 
                             eventtypevalues = c("conflict",
                                                 "cooperation"), 
                             eventvar = dtrem$eventDummy)

# calculate enemy-of-friend statistic
dtrem$triad.eof <- triadStat(data = dtrem, time = dtrem$eventTime, 
                             sender = dtrem$sender, target = dtrem$target, 
                             halflife = 2, eventtypevar = dtrem$type, 
                             eventtypevalues = c("cooperation",
                                                 "conflict"), 
                             eventvar = dtrem$eventDummy)

# calculate enemy-of-enemy statistic
dtrem$triad.eoe <- triadStat(data = dtrem, time = dtrem$eventTime, 
                             sender = dtrem$sender, target = dtrem$target, 
                             halflife = 2, eventtypevar = dtrem$type, 
                             eventtypevalues = c("conflict",
                                                 "conflict"), 
                             eventvar = dtrem$eventDummy)
# }

Run the code above in your browser using DataLab

Description

Usage

Arguments

Details

See Also

Examples