In the first step, we have a build-in dataset of 30-individual time series where ID1, ID2, ID3 are leaders at coordination intervals: [1,200], [201,400], and [401,600] respectively. These individuals move within two-dimensional space. Time series of each individual represents a sequence of coordinate (x,y) at each time step. A leader is an initiator who initiates coordinated movement that everyone in a faction follows.
library(mFLICA) # mFLICA::TS[i,t,d] is an element of ith time series at time t in the dimension d. Here, we have only two dimensions: x and y. The time series length is 800, so, t is in the range [1,800]. There are 30 individuals, so, i is in the range [1,30]. plotMultipleTimeSeries(TS=mFLICA::TS[,,1],strTitle="x axis")
plotMultipleTimeSeries(TS=mFLICA::TS[,,2],strTitle="y axis") ?followingRelation
To make it short, we choose only the interval [1,200] that ID1 is a leader. The framework is used to analyze the data below.
obj1<-mFLICA(TS=mFLICA::TS[,1:200,],timeWindow=60,sigma=0.5)
The network densities of a dynamic following network is shown below.
plotMultipleTimeSeries(TS=obj1$dyNetOut$dyNetBinDensityVec, strTitle="Network Dnesity")
We plot time series of faction size ratios of all leaders
plotMultipleTimeSeries(TS=obj1$factionSizeRatioTimeSeries, strTitle="Faction Size Ratios")
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