EGAnet (version 1.2.3)

jsd.ergoInfo: Jensen-Shannon Distance Test for Ergodicity

Description

Tests the Jensen-Shannon Distance (jsd) of each individual's network structure to the population's network structure. Using a threshold, the proportion of jsd values greater than the threshold are computed. If this proportion is greater or equal to the maximum allowable proportion of individuals, then the system is determined to be nonergodic. If the proportion is fewer than the maximum allowable proportion, then the system is determined to be ergodic

Usage

jsd.ergoInfo(
  dynEGA.object,
  method = c("kld", "spectral"),
  threshold = 0.2,
  max.proportion = 0.1
)

Value

Returns a list containing:

boot.ergoInfo

The values of the Ergodicity Information Index obtained in the bootstrap

p.value

The two-sided *p*-value of the bootstrap test for the Ergodicity Information Index. The null hypothesis is that the empirical Ergodicity Information index is equal to the expected value of the EII with small variation in the population structure

effect

Indicates wheter the empirical EII is greater or less then the bootstrap distribution of EII.

interpretation

How you can interpret the result of the test in plain English

plot.dist

Histogram of the bootstrapped ergodicity information index

methods

Methods to report for print/summary S3methods and automated Methods section

Arguments

dynEGA.object

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

method

Character. Method to compute Jensen-Shannon Distance. Defaults to "spectral". Options:

• "kld" Uses Kullback-Leibler Divergence

• "spectral" Uses eigenvalues of combinatiorial Laplacian matrix to compute Von Neumann entropy

threshold

Numeric. Sets the threshold of the jsd determined to be too distant for ergodicity to hold. Defaults to 0.20. Values can range between 0 and 1

max.proportion

Numeric. Sets the proportion of jsd values that are greater than the specified threshold. Defaults to 0.10. Values can range between 0 and 1

Author

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

References

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

Examples

if (FALSE) {
# Dynamic EGA individual and population structures
dyn1 <- dynEGA.ind.pop(
  data = sim.dynEGA[,-26], n.embed = 5, tau = 1,
  delta = 1, id = 25, use.derivatives = 1,
  model = "glasso", ncores = 2, corr = "pearson"
)

# JSD Ergodicity Test
testing.ergoinfo <- jsd.ergoInfo(
  dynEGA.object = dyn1
)}