umx (version 1.9.1)

umxExpCov: Get the expected vcov matrix

Description

Extract the expected covariance matrix from an mxModel

Usage

umxExpCov(object, latents = FALSE, manifests = TRUE, digits = NULL, ...)

Arguments

object

an mxModel to get the covariance matrix from

latents

Whether to select the latent variables (defaults to TRUE)

manifests

Whether to select the manifest variables (defaults to TRUE)

digits

precision of reporting. NULL (Default) = no rounding.

...

extra parameters (to match vcov)

Value

- expected covariance matrix

References

- http://openmx.ssri.psu.edu/thread/2598 Original written by http://openmx.ssri.psu.edu/users/bwiernik

See Also

- umxRun, umxCI_boot

Other Reporting functions: RMSEA.MxModel, RMSEA.summary.mxmodel, RMSEA, extractAIC.MxModel, loadings, plot.MxModel, residuals.MxModel, umxCI_boot, umxCI, umxCompare, umxConfint, umxExpMeans, umxFitIndices, umxPlotACEcov, umxPlotACEv, umxPlotACE, umxPlotCP, umxPlotGxE, umxPlotIP, umxSummary.MxModel, umxSummaryACEv, umxSummaryACE, umx_drop_ok, umx_standardize_RAM

Examples

# NOT RUN {
require(umx)
data(demoOneFactor)
latents  = c("G")
manifests = names(demoOneFactor)
m1 <- mxModel("One Factor", type = "RAM", 
	manifestVars = manifests, latentVars = latents, 
	mxPath(from = latents, to = manifests),
	mxPath(from = manifests, arrows = 2),
	mxPath(from = latents, arrows = 2, free = FALSE, values = 1.0),
	mxData(cov(demoOneFactor), type = "cov", numObs = 500)
)
m1 = umxRun(m1, setLabels = TRUE, setValues = TRUE)
vcov(m1)
umxExpCov(m1, digits = 3)
# }