A named numeric vector of traditional and likelihood-based fit
indices. The vector always contains the following elements:
chisq
Chi-square statistic
(\(\chi^2 = n \cdot F_{ML}\)), where \(F_{ML}\) is the maximum
likelihood discrepancy function between the model-implied and empirical
correlation matrices
df
Degrees of freedom: total number of unique off-diagonal
correlations minus the number of non-zero edges in network
chisq.p.value
p-value for the chi-square test of exact fit
(H0: model-implied covariance equals the population covariance)
RMSEA
Root mean square error of approximation. Values
\(\leq\) 0.05 indicate close fit; values \(\leq\) 0.08 indicate
acceptable fit
RMSEA.XX.lower, RMSEA.XX.upper
Lower and upper
bounds of the ci-level confidence interval for RMSEA, where
XX is the integer percentage (e.g., RMSEA.95.lower and
RMSEA.95.upper for a 95% CI)
RMSEA.p.value
p-value for the one-sided test of close fit
(H0: RMSEA \(\leq\) 0.05)
CFI
Comparative fit index, comparing the target model to
an independence (null) baseline. Values \(\geq\) 0.95 indicate
acceptable fit
TLI
Tucker-Lewis index (non-normed fit index). Values
\(\geq\) 0.95 indicate acceptable fit; can fall outside \([0, 1]\)
for severely misspecified models
SRMR
Standardized root mean residual: the root mean squared
difference between the model-implied and observed correlation matrices.
Values \(\leq\) 0.08 indicate acceptable fit
logLik
Gaussian log-likelihood of the model-implied
correlation matrix, assuming zero mean structure (means are not
estimated)
AIC
Akaike's information criterion:
\(-2 \cdot \log L + 2 \cdot E\), where \(E\) is the number of
non-zero edges in network
BIC
Bayesian information criterion:
\(-2 \cdot \log L + E \cdot \log(n)\)