vn.entropy: Entropy Fit Index using Von Neumman's entropy (Quantum...

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

Entropy Fit Index using Von Neumman's entropy (Quantum Information Theory) for correlation matrices

Description

Computes the fit of a dimensionality structure using Von Neumman's entropy when the input is a correlation matrix. Lower values suggest better fit of a structure to the data

Usage

vn.entropy(data, structure)

Arguments

data	Matrix or data frame. Contains variables to be used in the analysis
structure	Numeric or character vector (length = ncol(data)). A vector representing the structure (numbers or labels for each item). Can be theoretical factors or the structure detected by EGA

Value

Returns a list containing:

VN.Entropy.Fit	The Entropy Fit Index using Von Neumman's entropy
Total.Correlation	The total correlation of the dataset
Average.Entropy	The average entropy of the dataset

Author(s)

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

References

Initial formalization and simulation
Golino, H., Moulder, R. G., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2020). Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research.

Examples

# Get EGA result
ega.wmt <- EGA(
  data = wmt2[,7:24], model = "glasso",
  plot.EGA = FALSE # no plot for CRAN checks
)

# Compute Von Neumman entropy
vn.entropy(ega.wmt$correlation, ega.wmt$wc)

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