ergoInfo: Ergodicity Information Index

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

Ergodicity Information Index

Description

Computes the Ergodicity Information Index

Usage

ergoInfo(
  dynEGA.object,
  use = c("edge.list", "unweighted", "weighted"),
  shuffles = 5000
)

Arguments

dynEGA.object	A dynEGA.ind.pop object
use	Character (length = 1). A string indicating what network element will be used to compute the algorithm complexity, the list of edges or the weights of the network. Defaults to use = "unweighted". Current options are: "edge.list" — Calculates the algorithm complexity using the list of edges "unweighted" — Calculates the algorithm complexity using the binary weights of the encoded prime transformed network. 0 = edge absent and 1 = edge present "weighted" — Calculates the algorithm complexity using the weights of encoded prime-weight transformed network
shuffles	Numeric. Number of shuffles used to compute the Kolmogorov complexity. Defaults to 5000

Value

Returns a list containing:

PrimeWeight	The prime-weight encoding of the individual networks
PrimeWeight.pop	The prime-weight encoding of the population network
Kcomp	The Kolmogorov complexity of the prime-weight encoded individual networks
Kcomp.pop	The Kolmogorov complexity of the prime-weight encoded population network
complexity	The complexity metric proposed by Santora and Nicosia (2020)
EII	The Ergodicity Information Index

Author(s)

Hudson Golino <hfg9s at virginia.edu> and Alexander Christensen <[e-mail]>

References

Original Implementation
Golino, H., Nesselroade, J. R., & Christensen, A. P. (2022). Toward a psychology of individuals: The ergodicity information index and a bottom-up approach for finding generalizations. PsyArXiv.

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[,-26], n.embed = 5, tau = 1,
  delta = 1, id = 25, use.derivatives = 1,
  ncores = 2, corr = "pearson"
)

# Compute empirical ergodicity information index
eii <- ergoInfo(dyn.ega1)
## End(Not run)

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