umx (version 1.4.0)

umx_standardize_RAM: umx_standardize_RAM

Description

umx_standardize_RAM takes a RAM-style model, and returns standardized version.

Usage

umx_standardize_RAM(model, return = "parameters", Amatrix = NA, Smatrix = NA, Mmatrix = NA)

Arguments

model

The mxModel you wish to standardise

return

What to return. Valid options: "parameters", "matrices", or "model"

Amatrix

Optionally tell the function what the name of the asymmetric matrix is (defaults to RAM standard A)

Smatrix

Optionally tell the function what the name of the symmetric matrix is (defaults to RAM standard S)

Mmatrix

Optionally tell the function what the name of the means matrix is (defaults to RAM standard M)

Value

- a mxModel or else parameters or matrices if you request those

References

- http://github.com/tbates/umx

See Also

Other Reporting functions: RMSEA.MxModel, RMSEA.summary.mxmodel, RMSEA, confint.MxModel, extractAIC.MxModel, loadings, logLik.MxModel, plot.MxModel, residuals.MxModel, umxCI_boot, umxCI, umxCompare, umxExpCov, umxExpMeans, umxFitIndices, umxPlotACEcov, umxPlotACE, umxPlotCP, umxPlotGxE, umxPlotIP, umxSummary.MxModel, umxSummaryACE, umx_drop_ok

Examples

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)
m1 = umx_standardize_RAM(m1, return = "model")
summary(m1)