umx (version 4.0.0)

umxSummary.MxModel: Shows a compact, publication-style, summary of a RAM model

Description

Report the fit of a model in a compact form suitable for a journal. Alerts you when model fit is worse than accepted criterion (TLI >= .95 and RMSEA <= .06; (Hu & Bentler, 1999; Yu, 2002).

Usage

# S3 method for MxModel
umxSummary(
  model,
  refModels = NULL,
  std = FALSE,
  digits = 2,
  report = c("markdown", "html"),
  filter = c("ALL", "NS", "SIG"),
  SE = TRUE,
  RMSEA_CI = FALSE,
  ...,
  matrixAddresses = FALSE
)

Arguments

model

The mxModel() whose fit will be reported

refModels

Saturated models if needed for fit indices (see example below: If NULL will be competed on demand. If FALSE will not be computed. Only needed for raw data.

std

If TRUE, model is standardized (Default FALSE, NULL means "don't show").

digits

How many decimal places to report (Default 2)

report

If "html", then show results in browser (alternative = "markdown")

filter

whether to show significant paths (SIG) or NS paths (NS) or all paths (ALL)

SE

Whether to compute SEs... defaults to TRUE. In rare cases, you might need to turn off to avoid errors.

RMSEA_CI

Whether to compute the CI on RMSEA (Defaults to FALSE)

...

Other parameters to control model summary

matrixAddresses

Whether to show "matrix address" columns (Default = FALSE)

Value

• parameterTable returned invisibly, if estimates requested

Details

Note: For some (multi-group) models, you will need to fall back on summary()

CIs and Identification This function uses the standard errors reported by OpenMx to produce the CIs you see in umxSummary These are used to derive confidence intervals based on the formula 95%CI = estimate +/- 1.96*SE)

Sometimes they appear NA. This often indicates a model which is not identified (see http://davidakenny.net/cm/identify.htm). This can include empirical under-identification - for instance two factors that are essentially identical in structure. use mxCheckIdentification() to check identification.

One or more paths estimated at or close to zero suggests that fixing one or two of these to zero may fix the standard error calculation, and alleviate the need to estimate likelihood-based or bootstrap CIs

If factor loadings can flip sign and provide identical fit, this creates another form of under-identification and can break confidence interval estimation. Fixing a factor loading to 1 and estimating factor variances can help here.

References

• Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.

• Yu, C.Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. University of California, Los Angeles, Los Angeles. Retrieved from https://www.statmodel.com/download/Yudissertation.pdf

https://tbates.github.io

See Also

• umxRun()

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

Examples

# NOT RUN {
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)
m1 = umxRAM("One Factor", data = demoOneFactor, type = "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1)
)
umxSummary(m1, std = TRUE)
# output as latex
umx_set_table_format("latex")
umxSummary(m1, std = TRUE)
umx_set_table_format("markdown")
# output as raw
umxSummary(m1, std = FALSE)

# switch to a raw data model
m1 = umxRAM("One Factor", data = demoOneFactor[1:100, ],
	umxPath("G", to = manifests),
	umxPath(v.m. = manifests),
	umxPath(v1m0 = "G")
)
umxSummary(m1, std = TRUE, filter = "NS")
# }