OpenMx General Help

I am still getting used to OpenMx, so this may be a stupid question. Say I have a single factor ("G") measurement model, in a mxModel object called "mMeasure". Is it now possible to use the mMeasure object as part of a structural model? I imagine something like (pseudocode):

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)

Put your UI- and R-tips here.

So I am familiar with being able to refer to an MxModel object, and in particular its objective function, by name, thusly:

algObj <- mxAlgebra(-2*sum(log(classProbs[1,1]%x%Class1.objective + classProbs[2,1]%x%Class2.objective)), name="mixtureObj")

I was wondering if there was a reason I can't seem to get confidence intervals for an 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",

Hello,

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])
{

Hi!
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.

Sorry if this is a question that is answered elsewhere, but does anyone know how the time to compute a full SEM in OpenMx scales with the number of subjects that are used?

Thanks!

So, given an mxMatrix, testMatrix, with values:

@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