umxSummary reports the rA, rC, and rE for models. But this is done in the summary, and doesn't include CIs by default.

It’s computed as

solve(sqrt(I*A)) %*% A %*% solve(sqrt(I*A))

So you can add an mxAlgebra computing that into the model, then do mxSE on that

m2 = mxModel(m1, mxAlgebra(name="ra", solve(sqrt(top.I*top.A)) %*% top.A %*% solve(sqrt(top.I*top.A))) ) m2 = mxRun(m2) mxSE(ra, m2)

That returns SEs, and CI= ± 1.96 * SE

best, t

Note that correlations (phenotypic, genetic, whatever) are defined on a -1 to +1 scale. Also note that a large correlation has a smaller SE than has a small correlation. In finite samples, the error distribution of a correlation can be asymmetric, with a narrower interval on the side nearer 1 (or -1 if the estimated correlation is negative).

mxCI takes longer to run than mxSE, but the results of mxCI, which permits asymmetric confidence intervals, can be more informative.

The standard S3 method

`confint`

is also available. This use is likeHowever, these confidence intervals are based on the standard errors (Wald-type CIs), so the same caveats as Mike Neale alluded to still apply. In my experience, SE-based CIs are often "good enough" for unbounded parameters, but profile likelihood CIs (e.g. from

`mxCI`

) are far superior for bounded parameters like variances.