OpenMx Structural Equation Modeling

Fixing the parameter estimates at the starting values
I want to fit a model by using the starting values as the parameter estimates. This can quickly be done by treating them as fixed parameters. However, I also want to get some standard errors on these estimates though the starting values may not be the optimal solution.
I can fix the starting values as the estimates with `mxRun(my.model, useOptimizer = FALSE)`. But it does not provide any standard errors. Attached is an example. Any suggestions? Thanks in advance.
Mike

mxCI() not providing the confidence intervals. Why?

How does OpenMx handle missing data with WLS?

Confidence intervals of ACE model fit
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Custom Optimizer in OpenMx
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CIs on mxAlgebra
I have a mxAlgebra of a parameter multiplied by a constant, say new_x = 2*x. When I construct an LBCI on both x and new_x, I expect that the CIs on new_x are the same as the CIs on x multiplied by 2. It turns out that they are not exactly the same (see the "diff" in the following output). When the constants are larger, say 5 or 10, the CIs on the new_x even become NA.
Any ideas why this happens? Thanks.
## Multiplied by 2
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CFA with categorical variables
I am fitting some CFA models with all categorical variables with FIML. The information matrix is often not positive definite when the number of variables is larger than 4 or above. Attached are some examples.
Any suggestions to improve its stability? Thanks in advance.
Best,
Mike
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Help in translating a longitudinal path model to OpenMx SEM syntax
As I'm new to SEM (in general) and OpenMx I would like to ask you for some help in specifying the model in question properly.
I'm trying to reproduce results for a 3 time points path model where specific indirect effects where of interest. The original model was depicted as in attached image.

latent difference score issues
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Looking for help with including a moderator to a path model
this is my 1st post, so please forgive me if this is offtopic.
In sort of new to SEM / modelling in general. So far I managed to make some sense of lavaan() syntax in R, to run a path model like this (file attached).
Before introducing the W moderator (continuous) this model was specified (in lavaan) as:
model <- '
X1 ~~ X2
A ~ X1 + X2
Y ~ A'
All variables are observed and continuous.
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