Computes the normed fit index (NFI), adjusted normed fit index (adj NFI),
comparative fit index (CFI), Tucker-Lewis fit index (TLI), and root mean
square error of approximation index (RMSEA). Of the first four indices, the
adj NFI index is recommended because it has smaller sampling variability
than CFI and TLI and less negative bias than NFI.
For more details, see Section 2.14 of Bonett (2021, Volume 4)
Usage
fitindices(chi1, df1, chi2, df2, n)
Arguments
- chi1
chi-square test statistic for full model
- df1
degrees of freedom for full model
- chi2
chi-square test statistic for reduced model
- df2
degrees of freedom for reduced model
- n
sample size
Value
Returns NFI, adj NFI, CFI, TLI, and RMSEA
References
Bonett DG (2021).
Statistical Methods for Psychologists https://dgbonett.sites.ucsc.edu/.
Examples
fitindices(14.21, 10, 258.43, 20, 300)
#> NFI adj NFI CFI TLI RMSEA
#> 0.945 0.9837 0.98 0.9647 0.0375
# Should return:
# NFI adj NFI CFI TLI RMSEA
# 0.945 0.9837 0.98 0.9647 0.0375