OpenMx General Help

non-independent submodels
mStructure <- mxModel("Structure",
manifestVars = "Y", latentVars = "G",
mxPath(from = "G", to = "Y"),
## other model stuff)
mFull <- mxModel("Overall",
type = "RAM",
mxData(mydata[, c("all", "manifest", "variables", "for any submodel")], type = "raw"),
mMeasure,
mStructure)
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Handy R tips
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Accessing MxModel objects by other than name
algObj <- mxAlgebra(-2*sum(log(classProbs[1,1]%x%Class1.objective + classProbs[2,1]%x%Class2.objective)), name="mixtureObj")
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Confidence Intervals for Univariate Ordinal ACE Model with 2 Thresholds
I made sure to specify the mxCI for my standardized variance components call in my model and to put the "intervals=T" in my mxRun line. In my model fit $output$confidenceIntervals exists, but does not have any values.
Is this normal? If not, is there a way to create confidence intervals in the ordinal case? If it's just a silly code error, my code is below.
Any help would be appreciated!
univACEOrdModel <- mxModel("univACEOrd",
mxModel("ACE",

Specifying the Correlation Among all Exogenous Variables
I was wondering if there is an easy way to tell OpenMx to estimate all of the correlations among a set of exogenous variables, rather than specifying each of the correlations line-by-line in mxPath statements.
For example, suppose exogenous variables are:
IVs <- c("COREREL", "JOBPAY", "SEX_2")
I tried to automate a way to include the exogenous correlations for an arbitrary number of variables as:
These.Pairs <- combn(IVs, 2)
Here <- NA
for(i in 1:dim(These.Pairs)[2])
{

OpenMx script for twin data to parallelize
I have a script for ordinal twin data that takes about 5-6 hours to compute in my laptop. I was wondering whether it would be beneficial in this particular case to parallelize the work in order to reduce the computational time. Would it be possible to adapt this script to do the job?
I have been reading the OpenMx notes in the manual to implement the parallelization with the "Snowfall" package, but they seemed a bit odd to me (I have not too much experience in parallelizing with R). Any indication would be very appreciated. I attach here the script if somebody wish to have a look.
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How does SEM computational complexity in OpenMx scale with sample size?
Thanks!

Issue with indexing in MxMatrix
@values
X Y G
X 1 2 3
Y 4 5 6
Then:
testMatrix[c('X'), c('X', 'G')]
yields:
@values
[,1]
X 1
G 3
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summary(MxModel) object
this is what i get when i read in the code using "source" and run the model:
> head(summary(thisOMxModel)$parameters)
name matrix row col Estimate Std.Error
1
2
3
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Derivation of the Saturated Model Likelihood result.
Shouldn't the Saturated model be zero always except for some machine error? Instead it tends to be fairly close to a model with 1 degree of freedom.
I might be misunderstanding something but for the saturated model where the Implied model under this condition is equal to the Sample covariance matrix which should yield a discrepancy function of zero.
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