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

In EGAnet: 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	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

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.

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)

EGAnet documentation built on Sept. 28, 2024, 9:06 a.m.