genTEFI: Generalized Total Entropy Fit Index using Von Neumman's...

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

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

Description

Computes the fit (Generalized TEFI) of a hierarchical or correlated bifactor dimensionality structure (or hierEGA objects) using Von Neumman's entropy when the input is a correlation matrix. Lower values suggest better fit of a structure to the data

Usage

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

Arguments

data	Matrix, data frame, or hierEGA object. Can be raw data or correlation matrix
structure	List (length = levels). A list containing the hierarchical structure. Each list element corresponds to increasing levels (1 = first level, 2 = second level, etc.). The length of the first element (first level) should be the same as the number of variables in data. Each level after should either be the number of variables (ncol(data)) or the maximum number of dimensions from the preceding level (max(previous_dimensions))
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 levels + 1 columns data frame of the Generalized Total Entropy Fit Index using Von Neumman's entropy (VN.Entropy.Fit) (first column) as well as each individual's levels entropy

Author(s)

Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <[e-mail]>

Examples

# Example using network scores
opt.hier <- hierEGA(
  data = optimism, scores = "network",
  plot.EGA = FALSE # No plot for CRAN checks
)

# Compute the Generalized Total Entropy Fit Index
genTEFI(opt.hier)

hfgolino/EGA documentation built on June 13, 2025, 8:26 a.m.