1) Usage
Function to calculate confidence intervals in parallel in a model.
omxParallelCI(model, run = TRUE)
2) Arguments
model - a fitted MxModel object.
run - a boolean indicating if model should be run. Set to TRUE by default
3) Examples
data(demoOneFactor)
manifests - names(demoOneFactor)
latents - c("factor")
nManifest - length(manifests)
nVars - nManifest + length(latents)
factorModel - mxModel("One Factor", type="RAM",
manifestVars = manifests,
latentVars = latents,
mxPath(from=latents, to=manifests, free=c(FALSE,TRUE,TRUE,TRUE,TRUE), values=1),
mxPath(from=manifests, arrows=2, lbound=.0001),
mxPath(from=latents, arrows=2, free=TRUE, values=1.0),
mxData(cov(demoOneFactor), type="cov", numObs=500),
# mxPath(from="one", to=manifests, arrows=1, free=T, values=mean(demoOneFactor)),
# mxData(demoOneFactor, type="raw"),
mxMatrix("Iden", nrow=nVars, name="I"),
mxMatrix("Full", free=FALSE, values=diag(nrow=nManifest, ncol=nVars), name="Eff"),
mxAlgebra(Eff%*%solve(I-A), name="Z"),
mxAlgebra(Z%*%S%*%t(Z), name="C"),
mxAlgebra(sqrt(diag2vec(C)), name="P"),
mxCI(c("P"))
)
factorFit - mxRun(factorModel, intervals=FALSE)
factorParallel - omxParallelCI(factorFit)
# See the results...
print(factorParallel@output$confidenceIntervals)
lbound ubound
One Factor.P[1,1] 0.4193000 0.4747328
One Factor.P[2,1] 0.5082290 0.5754222
One Factor.P[3,1] 0.5755068 0.6515979
One Factor.P[4,1] 0.6871788 0.7780409
One Factor.P[5,1] 0.7704188 0.8722923
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