tefi: Total Entropy Fit Index using Von Neumman's entropy (Quantum...

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

Total Entropy Fit Index using Von Neumman's entropy (Quantum Information Theory) for correlation matrices

Description

Computes the fit (TEFI) 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

tefi(data, structure = NULL, verbose = TRUE)

Arguments

data	Matrix, data frame, or *EGA class object. Matrix or data frame can be raw data or a correlation matrix. All *EGA objects are accepted. hierEGA input will produced the Generalized TEFI (see genTEFI)
structure	Numeric or character vector (length = ncol(data)). Can be theoretical factors or the structure detected by EGA
verbose	Boolean (length = 1). Whether messages and (insignificant) warnings should be output. Defaults to TRUE to see all messages and warnings for every function call. Set to FALSE to ignore messages and warnings

Value

Returns a data frame with columns:

Non-hierarchical Structure

VN.Entropy.Fit	The Total Entropy Fit Index using Von Neumman's entropy
Total.Correlation	The total correlation of the dataset
Average.Entropy	The average entropy of the dataset

Hierarchical Structure

VN.Entropy.Fit	The Generalized Total Entropy Fit Index using Von Neumman's entropy
Lower.Order.VN	Lower order (only) Total Entropy Fit Index
Higher.Order.VN	Higher order (only) Total Entropy Fit Index

Author(s)

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

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

# Estimate EGA model
ega.wmt <- EGA(
  data = wmt, model = "glasso",
  plot.EGA = FALSE # no plot for CRAN checks
)

# Compute entropy indices for empirical EGA
tefi(ega.wmt)

# User-defined structure (with `EGA` object)
tefi(ega.wmt, structure = c(rep(1, 5), rep(2, 5), rep(3, 8)))

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