infoCluster: Information Theoretic Mixture Clustering for 'dynEGA'

In hfgolino/EGA: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Information Theoretic Mixture Clustering for dynEGA

Description

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
...	Additional arguments to be passed on to jsd

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

Author(s)

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

See Also

plot.EGAnet for plot usage in EGAnet

Examples

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

## Not run: 
# 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)
## End(Not run)

hfgolino/EGA documentation built on Nov. 11, 2024, 9:28 p.m.