OpenMx Help

I am running a trivariate Cholesky decomposition using a script from the workshop. This script did not include CIs but I included it from another source. However, while the CI script allows me to run CIs for the genetic and environmental correlations, I can't figure out how to modify the script so I can run CIs for my standardized path estimates. The portion of my script that runs the CIs for correlations is below:

corComp_A <- mxAlgebra(solve(sqrt(ACE.I*ACE.A))%*%ACE.A%*% solve(sqrt(ACE.I*ACE.A)), name="corComp_A")

Dear all,

Hi,

As I am doing simulations, I can see many warning messages from NPSOL. It seems that @output$status is the place to look for those different convergence types. I can see (from a successful run) that "status" has three empty slots. May I know what the "status" will be for an unsuccessful/non-conventional convergence? Is there any reference or documentation on this topic? Thanks.

- Hao

Hi All,

Is there already a helper function to allow users to easily extract the model-implied covariance matrix for the latent variables in a RAM-type model? If not, then I created one (attached). If there was already one, then I've duplicated it under possibly different implementation.

If this is useful to many people, then it could be incorporated into OpenMx at some point.

I'm trying to use a couple of nifty functions that I cribbed from a script from the Twin Workshop. When I try to use
formatOutputMatrices or tableFitStatistics I get an error message saying R "could not find function".

Suggestions?

Thanks,
Carol

Hi again,

I'm trying to run an ordinal model with age and sex as covariates. The ACE model seems to run Ok, but then I receive the following error with an AE model:

> univAEOrdFit <- mxRun(univAEOrdModel, intervals=T)
Running univAEOrd
Error: The job for model 'univAEOrd' exited abnormally with the error message: Objective function returned an infinite value.
In addition: Warning message:
In runHelper(model, frontendStart, intervals, silent, suppressWarnings, :
Not calculating confidence intervals because of error status.

The attached code generates the following error message:


> first <- mxRun(model)
Running Alg