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Performs hierarchical clustering using Jensen-Shannon distance followed by the Louvain algorithm with consensus clustering. The method iteratively identifies smaller and smaller clusters until there is no change in the clusters identified

Usage

infoCluster(dynEGA.object, plot.cluster = TRUE)

Arguments

dynEGA.object

A dynEGA or a dynEGA.ind.pop object that is used to match the arguments of the EII object

plot.cluster

Boolean (length = 1). Should plot of optimal and hierarchical clusters be output? Defaults to TRUE. Set to FALSE to not plot

Value

Returns a list containing:

clusters

A vector corresponding to cluster each participant belongs to

clusterTree

The dendogram from hclust the hierarhical clustering

clusterPlot

Plot output from results

JSD

Jensen-Shannon Distance

See also

plot.EGAnet for plot usage in EGAnet

Author

Hudson Golino <hfg9s at virginia.edu> & Alexander P. Christensen <alexander.christensen at Vanderbilt.Edu>

Examples

# Obtain data
sim.dynEGA <- sim.dynEGA # bypasses CRAN checks

if (FALSE) {
# Dynamic EGA individual and population structure
dyn.ega1 <- dynEGA.ind.pop(
  data = sim.dynEGA, n.embed = 5, tau = 1,
  delta = 1, id = 25, use.derivatives = 1, 
  ncores = 2, corr = "pearson"
)

# Perform information-theoretic clustering
clust1 <- infoCluster(dynEGA.object = dyn.ega1)}