umx (version 4.20.0)

umxFitIndices: Get additional fit-indices for a model with umxFitIndices

Description

Computes a variety of fit indices. Originated in this thread: http://openmx.ssri.psu.edu/thread/765

Usage

umxFitIndices(model, ...)

Value

List of fit statistics

Arguments

model

The mxModel for which you want fit indices.

...

Additional parameters passed to summary.MxModel.

Author

Brenton M. Wiernik, Athanassios Protopapas, Paolo Ghisletta, Markus Brauer

Details

Note: This function is currently not robust across multi-group designs or definition variables. It is designed to provide residual-based fit indices (SRMR, CRMR, SMAR, CMAR, etc.) and less-often reported fit indices where Reviewer 2 wants something other than CFA/TLI/RMSEA.

Fit information reported includes:

Model characteristics: numObs, estimated parameters, observed statistics, observed summary statistics, -2*log(Likelihood), degrees of freedom

Chi-squared test: Chi, ChiDoF, p (of Chi), ChiPerDoF,

Noncentrality-based indices: RMSEA, RMSEACI, RMSEANull, RMSEAClose (p value), independenceRMSEA, NCP, NCPCI, F0, F0CI, Mc (aka NCI, MFI)

Comparative fit indices: TLI (aka NNFI), CFI, IFI, PRATIO, PCFI

Residual-based indices: RMR, SRMR, SRMR_mplus, CRMR, MAR, SMAR, SMAR_mplus, CMAR

Information-theory criteria (computed using chi-square or -2LL; df or parameters penalties) AIC, AICc, BIC, SABIC, CAIC, BCC ECVI, ECVICI, MECVI, MECVICI

LISREL and other early fit indices (we recommend not reporting these) GFI, AGFI, PGFI, GH, NFI, PNFI, RFI

Want more? Open an Issue at GitHub.

See Also

Other Reporting functions: RMSEA.MxModel(), RMSEA.summary.mxmodel(), RMSEA(), extractAIC.MxModel(), loadings.MxModel(), loadings(), residuals.MxModel(), tmx_show.MxMatrix(), tmx_show(), umxCI_boot(), umxCI(), umxConfint(), umxExpCov(), umxExpMeans(), umxRotate()

Examples

if (FALSE) {
library(umx)
data(demoOneFactor)
latents = c("G")
manifests = names(demoOneFactor)
m1 = umxRAM("One Factor",
	data = mxData(cov(demoOneFactor), type = "cov", numObs = 500),
	umxPath(latents, to = manifests),
	umxPath(var = manifests),
	umxPath(var = latents, fixedAt = 1)
)
umxFitIndices(m1)

# And with raw data
m2 = umxRAM("m1", data = demoOneFactor,
	umxPath(latents, to = manifests),
	umxPath(v.m. = manifests),
	umxPath(v1m0 = latents)
)
umxFitIndices(m1, refModels = mxRefModels(m2, run = TRUE))
}