Fit Functions

Hi All,

Can R perform sem models (with generalized outcomes; mine are binary) and give me some kind of model fit, such as something analogous to the RMSEA or CFI that you'd get in sem with continuous outcomes?

Hello,

I have estimated my SEM model in OpenMx. For two days I was looking for answer but I haven't solved it.
How can I obtain p-values for coefficients to check significance of my variables?

Unfortunately, I can't attach my data due to confidentiality agreement.

Thank you,

Hello,

I am using OpenMx for latent variable path modeling. I have faced two problems in the results of the summary() function, about which any help would be much appreciated:

Hi,

I'm running a simulation in which a model will be fit to the simulated data 1000 times by running a loop.

However, some simulated data will make the model return error, like below:
Error: The job for model 'myModel' exited abnormally with the error message: Objective function returned a value of NaN at iteration 52.23.

May I use the option unsafe=TRUE in mxRun to skip error and continue the loop?

Thank you.

Best,
Jean

In fitting a bivariate threshold model using a definition variable, OpenMx converged but the final result gave the following predicted covariance matrix
InitT1 InitT2
InitT1 1.00000 1.48088
InitT2 1.48088 1.00000

Obviously not positive definite. Yet I did not get any warnings or errors.

Script and data attached.

Greg

I fitting twins data and comparing an ACE model (say) to the fully saturated model, using the chi-square from this as a measure of goodness-of-fit.

I have ten parameters in the fully saturated model (2 x 3 covariance parameters and 2 x 2 mean parameters) and four in the ACE model (a,c & e plus overall mean), so am comparing to a chi(6).

I am worried that in comparing this models as a goodness-of-fit test I am to some extent testing whether an overall mean should be fitted (as distinct from MZ mean 1, MZ mean 2, DZ mean 1, DZ mean 2) rather than just whether the ACE model is a good fit.

I am fitting ACE and ADE families of models and comparing them to the fully saturated model. I am getting results like this:

observed statistics: 2965
estimated parameters: 4
degrees of freedom: 2961
-2 log likelihood: 848.4525
saturated -2 log likelihood: 844.858
number of observations: 2266
chi-square: 3.594549
p: 1

Hi!

I am in a learning phase for SEM and was wondering which are the best fit statistics to consider while judging a model fit. I know that SEM programs produce a variety of model fit indices and wanted to understand which ones deserve more weightage. I would appreciate some thoughts on this topic.

Thanks!

Hi,

I have been comparing simple models between Mx and OpenMx and I get the exact same results using the same data for an univariate ACE model but as soon as I add a definition variable, path coefficients and fit indices start to differ considerably. Can someone please tell me how or how to find out how the calculation of likelihood may or may not differ between the two programs?

I thought this may be partly due to differences in dealing with unmet assumptions, or calculation of definition variables? Or a model misspecification?

Jane

Hello!

I hope someone can help with a rather simple question:
When you compare the fits of an ACE model to the nested AE, CE and E submodels should you then compare the E model to the AE (or CE) sbmodel or to the ACE model?
I would tend to compare the E model to the "nextlarger" submodel to test for a significant deterioration of the fit, but I have the impression that others compare all models to the ACE model.

Thanks for your help

Henning