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To request confidence intervals from a model, simply include a call to mxCI in the model, and add "intervals=TRUE" to the call to mxRun()

i.e.

require(OpenMx) data(demoOneFactor) manifests <- names(demoOneFactor) latents <- c("G") m1 <- mxModel("OneFactor",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 <- mxModel(m1, mxCI(c('OneFactor.A'))) m1 = mxRun(m1, intervals=TRUE) summary(m1)

The interval and type defaults give the 95% upper and lower CIs that most users will want.

confidence intervals: lbound estimate ubound OneFactor.A[1,6] 0.3679342 0.3971527 0.4290522 OneFactor.A[2,6] 0.4695034 0.5036616 0.5411646 OneFactor.A[3,6] 0.5389615 0.5772421 0.6193344 OneFactor.A[4,6] 0.6578832 0.7027746 0.7522656 OneFactor.A[5,6] 0.7464224 0.7962510 0.8512582

The CI request list is quite intelligent and can take any mixture of labels, matrix or algebra names, matrices with column and row indices. So the following are all fine, as long as they are legal objects found in the model or its submodels.

mxCI(c('m1.A', 'm1.A[,1]', 'myAlg'))

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