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 Thu, Jun 9, 2011 at 6:19 AM, yrosseel <yross...@gmail.com> wrote: > 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. > ______________________________________________ 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.
