OpenMx Structural Equation Modeling

Whenever I try to fit models and add covariances with arrows=2, I always have a heck of a time getting the model past the 'expected covariance is non-positive definite' error. Is this normal? I do not have nearly this much trouble with arrows = 1. Here is a little example using a built in dataset:

#########################
summary(mxRun(m <- mxModel("Example", type = "RAM",
manifestVars = colnames(ability.cov$cov), latentVars = "G",
mxData(ability.cov$cov, type = "cov", numObs = ability.cov$n.obs),
mxPath(from = "G", to = colnames(ability.cov$cov)),

hi all,
If you do "summary(fit)$CI" you get the upper, lower, and estimate:
lbound estimate ubound
top.a_std[1,1] -0.68048731 -0.6216239 -0.5515234

but "fit@output$confidenceIntervals" gives just the upper and lower:
lbound ubound
top.a_std[1,1] -0.68048731 -0.5515234

Because summary takes ages to run on big models, it would be nice if "fit@output$confidenceIntervals" had the estimate in there as well: Also good for consistency.

Best, t

Can anyone direct me to a script indicating how to run a constraint model? I am combining two twin data sets and want to run a fully constrained and fully unconstrained model in order to know if the two samples can be combined. How can I tweak my multivariate Chlolesky to do this?

Hello,

When analyzing a correlation matrix, do I need to put a constraint to the estimated Sigma so that its diagonal equals identity during optimization? As in Mx? Or has this been taken care of already in openMx?
Thanks in advance!

Suzanne

I am starting a new thread on this but see
http://openmx.psyc.virginia.edu/thread/831
for background.

I am still attempting to replicate the results from Sorbom's paper using the code posted by Rine and some of the new options to mxRun outlined in the old thread. I am attaching three files.
mi.r contains my code for modification indexes
sorbom.r creates the correlation matrix from one of the examples in Sorbom's paper and runs it
sorbom.txt is the resultant output showing the value of mi I get,

hi,
I have a model with existing CIs

fit1a
...
@intervals : 'top.a_std', 'top.c_std', and 'top.e_std'
...


I want to delete those, and run with just 1 request, so I tried
fit1b= mxModel(fit1a,mxCI(c("top.e_std[1,2]")))
but this seems to add the CIs onto the end of the list (rather than stamp on the old list)