Dear users,

I was trying to model latent variable via the common factor model using path-centric model specification as it is shawn in the examples in OpenMx User Guide, Release 1.2.0-1919, chapter 2.2 Factor Analysis, Path Specification (http://openmx.psyc.virginia.edu/docs/OpenMx/latest/OpenMxUserGuide.pdf). My purpose was to to estimate factor loadings, residual variances and means. But in all my attmpts I did not receive the chi-square statistics and some other goodness of fit measures like CFI, TLI and RMSEA. Even in the example exactly copied from the above source (http://openmx.psyc.virginia.edu/svn/tags/stable-1.2/demo/OneFactorModel_PathRaw.R) I have not received the chi-square value. Is there any mistakes?

best regards,

Krzysiek

If you were using raw data, that is mxData with type="raw", then these fit indices are not returned by default so that the model estimates faster. These fit indices require a best-fitting comparison model that we call the Saturated Model. By default, OpenMx does not fit this model for you because it often doubles the amount of run-time. The saturated model is a model with all the means and covariances freely estimated as parameters. Future versions of OpenMx will have a helper function for making this model. I've attached the R file that defines this function for you to use. Running the attached R file defines a function called mxSaturatedModel that takes a simple mxModel, builds, and optionally fits the corresponding saturated model.

The general format of use is as follows:

Once the SaturatedLikelihood argument is given the saturated model, the summary should return all the fit indices you mentioned. Let us know if it does not!

Mike H, that's very nice! I hope a multiple group version will be forthcoming :).

One thing I noted, your file defines omxSaturatedModel() not mxSaturatedModel() so a slight tweak to read

seems needed.

Thank you very much!

It really helps me!

Krzysiek