OpenMx Structural Equation Modeling

Convert RAM Model to LISREL
This shows the specification for the endogenous and exogenous variables in the LISREL version:
manifestVars=list(
endo=c("c_el","c_nc_el","c_or","c_nc_or",
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P value of 1 in mxCompare
I am working on an analysis of a univariate sex limitation model for depression and I have run into a potential problem. When comparing the homogeneity ACE model to the AE model, I get a p-value of 1 in mxCompare. The fit of the models are very similar:
> mxCompare(HomACEModelFit,HomAEModelFit)
base comparison ep minus2LL df AIC diffLL diffdf p
1 HomACE
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Constraining loadings on factor so average of loadings equals 1 and average of intercepts equals 0
oneFactorModel <- mxModel("CFA",
type="RAM",


Simplex model & sex differences
I am working on a simplex script in openMx. There are quantitative and qualitative sex differences for my phenotype, so I estimate separate paths for males and females and I would also like to freely estimate the dos correlations between the latent As. However, I am struggling with the transmission paths.
Probably, someone else has already dealt with this. I would like to know whether there are any simplex scripts available that take into account sex differences?
Regards
Charlotte
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Multilevel SEM with complex sampling
Would you confirm if OpenMx supports multilevel SEM on complex sampling survey data, with categorical outcome? (each datapoint has different weight)
Thank you!
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Returned matrices (expCov, expMean, A,S, etc) only reflect last row of data

Standardized estimates under equality constraints
I ran a linear latent growth curve model in OpenMx and constrained all residual variances to be equal. Not surprisingly, they all get to be the same unstandardized estimate. However, OpenMx reports only a single standardized estimate for the residual, which happens to be the one associated with the first observation. Even if the unstandardized estimates are constrained to be equal, the unstandardized ones need not be and in reality seldom are the same.
[code]

Adding new algebras in submodels and constraining them
I am running a bivariate moderation model and would like to set some constraints in order to test for some nonlinear effects. I am struggling though with adding new algebras into submodels (with subsequent equating them). Let's say that I specify
pathAm <- mxMatrix(name = "am", type = "Lower", nrow = nv, ncol = nv, free=T, labels = c("amM","amC","amU"), values=pathVal)
pathCm <- mxMatrix(name = "cm", type = "Lower", nrow = nv, ncol = nv, free=T, labels = c("cmM","cmC","cmU"), values=pathVal)

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