Entropy Fit Index

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

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

Examples

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

if (FALSE) { # \dontrun{
# Estimate EGA model
ega.wmt <- EGA(data = wmt)} # }

# Compute entropy indices
entropyFit(data = wmt, structure = ega.wmt$wc)
#>   Total.Correlation Total.Correlation.MM Entropy.Fit Entropy.Fit.MM
#> 1         0.2429484            0.2222733   -1.103339      -1.127812
#>   Average.Entropy
#> 1       -1.836265