EGAnet (version 2.0.8)

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 <[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)