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

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

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.

Author

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

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)
#>   VN.Entropy.Fit Total.Correlation Average.Entropy
#> 1      -2.556997                 0        -1.65189