OpenMx (version 2.2.2)

omxConstrainMLThresholds: omxConstrainMLThresholds

Description

Add constraint to ML model to keep thresholds in order

Usage

omxConstrainMLThresholds(model, dist = 0.1)

Arguments

model

the MxModel to which constraints should be added

dist

unused

Value

• a new MxModel object with the constraints added

Details

This function adds a nonlinear constraint to an ML model. The constraint keeps the thresholds in order. Constraints often slow model estimation, however, keeping the thresholds in increasing order helps ensure the likelihood function is well-defined. If you're having problems with ordinal data, this is one of the things to try.

Examples

# a simple one factor ordinal model
require(OpenMx)

data(myFADataRaw)

oneFactorOrd <- myFADataRaw[,c("z1", "z2", "z3")]

oneFactorOrd$z1 <- mxFactor(oneFactorOrd$z1, levels=c(0, 1))
oneFactorOrd$z2 <- mxFactor(oneFactorOrd$z2, levels=c(0, 1))
oneFactorOrd$z3 <- mxFactor(oneFactorOrd$z3, levels=c(0, 1, 2))

	oneFactorModel <- mxModel("Common Factor Model Path Specification",
	type="RAM",
	mxData(
		observed=oneFactorOrd,
		type="raw"
	),
	manifestVars=c("z1","z2","z3"),
	latentVars="F1",
	# residual variances
	mxPath(
		from=c("z1","z2","z3"),
		arrows=2,
		free=FALSE,
		values=c(1,1,1),
		labels=c("e1","e2","e3")
	),
	# latent variance
	mxPath(
		from="F1",
		arrows=2,
		free=TRUE,
		values=1,
		labels ="varF1"
	),
	# factor loadings
	mxPath(
		from="F1",
		to=c("z1","z2","z3"),
		arrows=1,
		free=c(FALSE,TRUE,TRUE),
		values=c(1,1,1),
		labels=c("l1","l2","l3")
	),
	# means
	mxPath(
		from="one",
		to=c("z1","z2","z3","F1"),
		arrows=1,
		free=FALSE,
		values=0,
		labels=c("meanz1","meanz2","meanz3","meanF")
	),
	# thresholds
	mxThreshold(vars=c("z1", "z2", "z3"),
		nThresh=c(1,1,2),
		free=TRUE,
		values=c(-1, 0, -.5, 1.2)
		)
) # close model

oneFactorCon <- omxConstrainMLThresholds(oneFactorModel)
oneFactorResults <- mxRun(oneFactorCon)