genTEFI: 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)

Value

Returns a three-column data frame of the Generalized Total Entropy Fit Index using Von Neumman's entropy (VN.Entropy.Fit) (first column), as well as Lower.Order.VN - TEFI for the first-order factors (second column), and Higher.Order.VN, the equivalent for the second-order factors.

Arguments

data

Matrix, data frame, or hierEGA object. Can be raw data or correlation matrix

structure

For high-order and correlated bifactor structures, structure should be a list containing:

lower_order --- A vector (length = ncol(data)) representing the first-order structure (numbers or labels for each item in each first-order factor or community)
higher_order --- A vector (length = ncol(data) or number of lower_order communities)representing the second-order structure (numbers or labels for each item in each second-order factor or community)

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

Author

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

Examples

Run this code

# 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)