vn.entropy {EGAnet} R Documentation

## 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

### Author(s)

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

### References

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.

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

# Load data
dep <- depression[,24:44]

# 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)



[Package EGAnet version 1.1.0 Index]