network.fit: Traditional Fit Metrics for Networks

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

Traditional Fit Metrics for Networks

Description

Computes several traditional fit metrics for networks including

• chi-square (\chi^2)

• root mean square error of approximation (RMSEA) with confidence intervals

• confirmatory fit index (CFI)

• Tucker-Lewis inded (TLI)

• standardized root mean residual (SRMR)

• log-likelihood

• Akaike's information criterion (AIC)

• Bayesian information criterion (BIC)

Usage

network.fit(network, n, S, ci = 0.95)

Arguments

network	Matrix or data frame. A p by p sqaure network matrix
n	Numeric (length = 1). Sample size
S	Matrix or data frame. A p by p sqaure zero-order correlation matrix corresponding with the input network
ci	Numeric (length = 1). Confidence interval for RMSEA

Value

Returns a named vector of fit statistics

Author(s)

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

References

Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017). Generalized network psychometrics: Combining network and latent variable models. Psychometrika, 82(4), 904–927.

Examples

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

# Obtain correlation matrix
S <- auto.correlate(wmt)

# EBICglasso (default for EGA functions)
glasso_network <- network.estimation(
  data = wmt, model = "glasso"
)

# Obtain fit (expects continuous variables!)
network.fit(network = glasso_network, n = nrow(wmt), S = S)
# Scaled metrics are not yet available for
# dichotomous or polytomous data!

EGAnet documentation built on April 13, 2026, 5:07 p.m.