vn.entropy: 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)

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

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

Author

Hudson Golino <hfg9s at virginia.edu>, Alexander P. Christensen <alexpaulchristensen@gmail.com>, and Robert Moulder <rgm4fd@virginia.edu>

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

Run this code

# 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)

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