OpenMx Help

I was wondering how OpenMx treats standardized estimates under equality constraints.

I ran a linear latent growth curve model in OpenMx and constrained all residual variances to be equal. Not surprisingly, they all get to be the same unstandardized estimate. However, OpenMx reports only a single standardized estimate for the residual, which happens to be the one associated with the first observation. Even if the unstandardized estimates are constrained to be equal, the unstandardized ones need not be and in reality seldom are the same.

[code]

When installing OpenMx on OSX 10.9.2, I ran into the following error:
"tar: Option --delete is not supported"

Particularly, this error happens after successful compilation when the npsol-related files are deleted from the resulting gzip file. On my machine, tar does in fact not support the "--delete" option. For Mac users, MacPorts provides gnutar, which does the job. However, with the default path of MacPorts, one needs to explicitly create another symlink:

sudo ln -s /opt/local/bin/gnutar /usr/bin/gnutar

and change line 268 in the Makefile to:

hi, I am a beginner using Mx open packages, so I do not quite understand. I have run my program. output is given as attached.
The value of RSMEA, CFI and TLI its very ugly because its value doesn't meet either criteria . What can I do in advance to improve the model?
thank you.

I'm trying to run a model in which I have several matrices that should contain the same parameter estimates (they overlap partly). I know I can use labels or mxConstraint to put equality constraints on parameter estimates, but I was wondering if it is also possible to refer directly to the parameter estimates of a matrix in other parts of the OpenMx environment. For example, I have tried to build an mxAlgebra that refers to the parameter estimates of an mxMatrix by using:

mxAlgebra(expression=cbind(Model.mxMatrix[,i],Model.mxMatrix[,j]), name="part_mxMatrix")

I'm trying to run multiple SEM-models within a loop (i.e., for simulation purposes), but am experiencing some problems when a model does not run properly (i.e., objective function returns NaN). The default action is to abort and display an error message, which results in aborting the whole loop.

I am looking for a way to make it possible to make sure the loop will continue when one of the models is not returning a result (and of course, I would also like to save the non-result in some way).

This may be an easy question, but I can't think of the answer. I've built a regression model (predicting Y) with two independent variables (X and Z). I want to compute R^2. Is that built into the model somehow? If not, any ideas on how to compute it? Here's the model I have:

multiRegModel <- mxModel("Multiple Regression, All Variables",
type="RAM",
manifestVars=c("x", "y", "z"),
# variance paths
mxPath(
from=c("x", "y", "z"),
arrows=2,
free=TRUE,
values = c(.5, .5, .5),
labels=c("varx", "residual", "varz")
),

Hi.
I am running a bivariate moderation model and would like to set some constraints in order to test for some nonlinear effects. I am struggling though with adding new algebras into submodels (with subsequent equating them). Let's say that I specify


pathAm <- mxMatrix(name = "am", type = "Lower", nrow = nv, ncol = nv, free=T, labels = c("amM","amC","amU"), values=pathVal)
pathCm <- mxMatrix(name = "cm", type = "Lower", nrow = nv, ncol = nv, free=T, labels = c("cmM","cmC","cmU"), values=pathVal)