OpenMx Help

We have found that the default blas (basic linear algebra system) libraries supplied with R for MAC OS X have poor performance. Specifically, the following three commands, executed as root in a Terminal.app window reduces OpenMx execution time by 75% in some cases (ymmv).

cd /Library/Frameworks/R.framework/Libraries
sudo rm libRblas.dylib
sudo ln -s libRblas.0.dylib libRblas.dylib

Note that these commands simply reconfigure the link, they don't actually delete any libraries. To *undo* these commands one could do the following:

cd /Library/Frameworks/R.framework/Libraries

I have observed this for a while in two 32-bit and one 64-bit computers with Fedora 13.

When I installed OpenMx inside R with a root account by source('http://openmx.psyc.virginia.edu/getOpenMx.R'), I got the following errors:

** testing if installed package can be loaded
Error in dyn.load(file, DLLpath = DLLpath, ...) :
unable to load shared library '/usr/lib/R/library/OpenMx/libs/OpenMx.so':
/usr/lib/R/library/OpenMx/libs/OpenMx.so: cannot restore segment prot after reloc: Permission denied
ERROR: loading failed
* removing ‘/usr/lib/R/library/OpenMx’

Hello all,

I am fitting some variance known models (akin to meta-analysis). To handle models with possible covariates, I use a RAM model. The results based on 0.5.2 are comparable to those based on other statistical packages. After upgraded to 0.9.0, the results look odd.

The followings are an example. The -2LL of the 0.5.2 and 0.9.0 versions are 27.79916 and 29.02565, respectively. Any suggestions are highly appreciated. Thanks.

Regards,
Mike

yi <- c(-0.264,-0.230,0.166,0.173,0.225,0.291,0.309,0.435,0.476,0.617,0.651,0.718,0.740,0.745,0.758,0.922,0.938,0.962,1.522,1.844)

Hi there,

I am new to OpenMx and trying to fit a factor model. The main part of the script used is shown below:

> manifests <-names(hetcor_T)
> latents <-c("F1","F2","F3","F4")
> twofactorModel<-mxModel(name = "Four Factor Model Path Specification",type="RAM",manifestVars = manifests, latentVars = manifests,
+ mxData(observed=cov(hetcor_T),type="cov",numObs=709),
+ # residual variances
+ mxPath(from= manifests, arrows=2), # latent variances and covariance
+ mxPath(from= latents, arrows=2), # factor loadings for business performance measure variables
+ mxPath(
+ from=c("F1"),

Dear all:
I'm a new learner of OpenMX and R , I want to use it to do simple SEM analysis, but I can't find the estimator information in OpenMxUserGuide. please tell me how can I find and how can I understand the estimator in OpenMx .

I'm posting a link to a comment from one of the news updates, because those comments appear without a listing in the "Recent Threads" panel on the right-hand side of the page.

http://openmx.psyc.virginia.edu/2010/08/openmx-050

I received this error when trying to run a latent growth curve model:

Error: The expected covariance matrix associated with the FIML objective in model 'MZ' is not of the same length as the dimnames provided by the objective function.

I used the traceback() function to try to determine the error and received the following output:

9: stop(msg, call. = FALSE)
8: updateObjectiveDimnames(.Object, job, model@name, "FIML")
7: genericObjModelConvert(flatObjective, job, model, namespace,
flatJob)
6: genericObjModelConvert(flatObjective, job, model, namespace,
flatJob)

I have been running some models that decompose multi-wave data into a stable trait component, an autoregressive trait component, and a state component. I have eight waves, with three indicators at each wave, 600 observations, and I'm using raw data (and there are missing data). With the data I have, these models (and some variants) can take a little while to run. But I've noticed that they actually run considerably slower with version .5.0 than with .3.0 (I hadn't upgraded until .5.0 came out).