Results of CFA with Lavaan

Ok, I think this is the last question I have.  My model is producing
an estimate of intercepts for my variables along with my loadings.

From the documentation it appears that this is controlled by the

meanstructure	option in cfa.  It says that setting it to TRUE includes
the intercepts, and setting it to "default" means thatthe value is set
based on the user-specified model, and/or the values of other
arguments.  I've included my model specification below, and I would
prefer not to fit intercepts, but setting it to FALSE does not seem to
achieve this.

Thanks,
Sam

F1 =~ reFDE + ReFUIDGreg + reFDRwithDDRV + reparD + reparDR +
reparRisk + reWDD + reWDH + reWSP + reWDIS + reWCell + reWFAT + reAanx
+ reDanx + reDstress + reAstress
F2 =~ reSI1 + reSI2 + reSI3 + reSI4 + reSimDE + reSimDD + reSimDrug + reSimDRD
F3 =~ RENOINTEND + RETRYNOTD + RENOSTARTD + REUSEDD + REWILLD1 + REDU1
+ REDA1 + RERIDE1 + REAFTER1 + REUSEC1 + REUSESP1 + REUM1 + REABUSE1 +
RESB1 + REMIGHT1
F4 =~ retrydrink + RetryDope + reNoD + reLeaveD + reDeDR + reDopeNo +
reDopeleave + reDopeDD + reP3D
F5 =~ reP3DA + reP3DD + reP3DRD + reP3Equip + reP3UC + reP3SP + reP3UM
+ reP3Abuse + reP3SB + reP3helmet + reP1DADR + reP1DRUG + reP1SP
F6 =~ reinjwhileDU + reinjwhileWDUDRV + reinjwhileDA +
reinjwhileDRafterD + reinjwhileUcrack + reinjwhileUM +
reinjwhileabusePRDG + reinjwhilenoSB + reinjwhilenohelmet
F7 =~ relikeDR + relikeSP + relikeDIS + relikeCELL + relikeDROW +
relikeDRUG + restupid + reimmature + takerisksFthinkcool +
takeriskFthinkIMP + takeriskFthinkbrave + takeriskFthinkexciting +
reSELF + reNORISK + reNOPERSON + reNOCONSE + reWRONG + reGEAR +
reCONSEQ + reSUCES

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

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