Behavioral Genetics Models

Multivariate code ACE models (path and variance based models)
Hi all!
I have two questions regarding the script I am using to fit a multivariate model exploring the association between body dissatisfaction (exposure X1) and depressive symptoms at ages 21 and 26 (outcomes Y1 and Y2) whilst accounting for twins' sex and age (residuals being regressed).
I have tried to adapt Hermine's code but I am not sure this is correct. I do get estimates and correlations for all the parameters (X1, Y1, Y2).
However, I am not sure about the following:

Very small estimate with a very large standard error in univariate modelling.
Dear all,
This is the first time to have a post in this forum, and thanks for the platform.
I am a researcher working on the Finnish Twin Cohorts, and I am doing a univariate twin modeling on depression. I have 113 DZ pairs and 69 MZ pairs. The outcome of depression is basically normal-distributed.
Then in the ADE model, I get the data summary and results like this:


How to add object to a model without re-run
I have fitted a very computationally demanding model (around one week) and now I need to add an mxAlgebra object into this model:
threG2 <- mxAlgebra( expression= threG[,1:7], name="threG2" )
I know I can do it in this way:
modelIPAE2 <- mxModel( fitIPAE,threG2, name="mulIPAE2c" )
fitIPAE2 <- mxTryHardOrdinal( modelIPAE2, intervals=F, extraTries=11 )
But I wonder if there is any way to modify the model without re-running it
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Van der Sluis in OpenMX?
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Fix mean latent variable
I am working on a CP model (using a reference script from Boulder). I would like to know if it is possible to fix (or freely estimate) the mean of the latent variables (here the latent factor) using a model specification like this one (see below). Would it be possible by fixing one of the manifest means? I know it is possible (and must be done) to fix the variance to one so that the model is identified but I am also curious if the mean of latent variables can be fixed (or freely estimated). If so how could I do it?
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Multivariate moderation
I'm an undergrad working on my dissertation, and I've managed to build my first models thanks to these forums, the Boulder Workshops, as well as [Hermine's wonderful stash of scripts](https://hermine-maes.squarespace.com/). I have several questions, however, just to make sure I'm doing things the right way, and if what I want to do is possible.
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when to standardize?
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impact of ignoring higher level clustering
I've read before that when cases are nested, ignoring the clustering generally affects standard errors but not the regression coefficients. Is this the case for ACE model parameters as well, e.g., when twin pairs are nested in schools or geographic regions?
Thanks!
George
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