On 06/08/2011 11:56 PM, R Help wrote:
Yes, that is the difference. For the last SEM I built I fixed the factor variances to 1, and I think that's what I want to do for the CFA I'm doing now. Does that make sense for a CFA?
If you have a latent variable in your model (like a factor in CFA), you need to define its metric/scale. There are typically two ways to do this: 1) fix the variance of the latent variable to a constant (typically 1.0), or 2) fix the factor loading of one of the indicators of the factor (again to 1.0). For CFA with a single group, it should not matter which method you choose. The fit measures will be identical.
Lavaan by default uses the second option. If you prefer the first (fixing the variances), you can simply add the 'std.lv=TRUE' option to the cfa() call, and lavaan will take care of the rest.
I'll try figuring out how to do that with lavaan later, but my model takes so long to fit that I can't try it right now.
You can use the 'verbose=TRUE' argument to monitor progress. You may also use the options se="none" (no standard errors) and test="none" (no test statistic) to speed things up, if you are still constructing your model. Or the model does not convergence, but I should see both the model and the data to determine the possible cause.
Hope this helps, Yves Rosseel http://lavaan.org ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
