EGAnet (version 0.9.8)

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)

Arguments

data

A datafram or a correlation matrix

structure

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

Golino, H., Moulder, R. G., Shi, D., Christensen, A. P., Garrido, L. E., Neito, 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. 10.31234/osf.io/mtka2

See Also

EGA to estimate the number of dimensions of an instrument using EGA and CFA to verify the fit of the structure suggested by EGA using confirmatory factor analysis.

Examples

# NOT RUN {
# Load data
dep <- depression[,24:44]

# }
# NOT RUN {
# Estimate EGA
## plot.type = "qqraph" used for CRAN checks
## plot.type = "GGally" is the default
ega.dep <- EGA(data = dep, model = "glasso", plot.type = "qgraph")

# Compute entropy indices
vn.entropy(data = ega.dep$correlation, structure = ega.dep$wc)
# }
# NOT RUN {
# }