entropyFit: Entropy Fit Index

In hfgolino/EGA: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Entropy Fit Index

Description

Computes the fit of a dimensionality structure using empirical entropy. Lower values suggest better fit of a structure to the data.

Usage

entropyFit(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:

Total.Correlation	The total correlation of the dataset
Total.Correlation.MM	Miller-Madow correction for the total correlation of the dataset
Entropy.Fit	The Entropy Fit Index
Entropy.Fit.MM	Miller-Madow correction for the Entropy Fit Index
Average.Entropy	The average entropy of the dataset

Author(s)

Hudson F. 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

# Load data
wmt <- wmt2[,7:24]

## Not run: 
# Estimate EGA model
ega.wmt <- EGA(data = wmt)
## End(Not run)

# Compute entropy indices
entropyFit(data = wmt, structure = ega.wmt$wc)

hfgolino/EGA documentation built on Nov. 11, 2024, 9:28 p.m.