umx

Bivariate ACE model with moderator
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ACE latent growth modeling
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Genetic correlations that doesn't make sense
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robust standard errors and CIs using umx
I ran an ADE Cholesky model with 4 variables. When trying to obtain CIs for the one-headed paths (using the command umxConfint), I got weird CIs and the note "alpha level not reached" near some of the CIs. After consultation in the behavioral genetics forum, I understood that this the result of an optimization problem.
AdminRobK suggested that instead of looking at the CIs, I will try to get robust standard errors, then use mxSE() to get standard errors for the elements and form a CI from those standard errors
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How do I get a CI on a parameter derived from a model?
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

Correlated factors solution for umxACE
I'm using umxACE for a bivariate model. The current output is in the format of a cholesky model in addition to rG, rC, rE. Is there a way to get an output of the correlated factors solution? I know that I can easily calculate the estimates, but because they are not part of the model's output I didn't succeed in getting CI's for the calculated estimates (or the r's). Is there a way to get these (as these are more intuitive for presenting)?
Thanks for all the help!
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umxSexLim - heritability doesn't match correlations

Obtain CIs on genetic correlations
Thanks in advance.
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default constraints in multi group model?
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Multiple Moderators
some days ago I read the paper by Shaun Purcell (2002) "Variance Components Models for Gene-Environment Interaction in Twin Analysis" and I'm especially interested in modelling a three-way interaction (GxExE). So, for example, the genetic effect may be moderated by the socioeconomic status and by age, so that the additive genetic variance component is:
$$
(a+\beta_{Age}M_{Age}+\beta_{SES}M_{SES}+\beta_{AgexSES}M_{Age}M_{SES})^{2}
$$
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