| omxConstrainMLThresholds {OpenMx} | R Documentation |
Add constraint to ML model to keep thresholds in order
omxConstrainMLThresholds(model, dist = 0.1)
model |
the MxModel to which constraints should be added |
dist |
unused |
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.
a new MxModel object with the constraints added
# 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)
#N.B. FAILS!