Re: [R] Estimators for non-normal data
Yes, I meant confirmatory factor analysis, I'm sorry if I wasn't clear. I was doing my analyses in lavaan and saw that there are several robust options (MLM, MLMVS, MLMV, MLF, MLR), but I wasn't sure about their specifics, so that was what I was actually asking about. I will definitely consult the links you sent me, thank you! 2016-07-25 11:46 GMT+02:00 Martin Maechler : > > Nika Sušac > > on Sat, 23 Jul 2016 19:39:48 +0200 writes: > > > Hi! I have non-normal data (items are continuous on a > > 9-point scale, but not normally distributed) and I want to > > conduct cfa. Which of the estimators available in lavaan > > do you recommend me to use? Thanks in advance! > > I think you want *robust* statistical methods then. > > Robust factor analysis (if 'cfa' means something like that) is > somewhat prominent topic. > Simple approaches will already be available by using > MASS::cov.rob() for a robust covariance matrix which you then > can pass to other methods. > > For more (and more modern) methods and approaches, > > - for R packages, I'd recommend you consult the CRAN task view > about robust statistical methods, > https://cran.r-project.org/web/views/Robust.html > notably the section on 'Multivariate Analysis' > > - for more specific help and expertise, I strongly recommend > the dedicated R-SIG-robust mailing list (instead of R-help), >--> https://stat.ethz.ch/mailman/listinfo/r-sig-robust > > Best regards, > Martin Maechler, ETH Zurich > > > [[alternative HTML version deleted]] > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
Re: [R] Estimators for non-normal data
> Nika Sušac > on Sat, 23 Jul 2016 19:39:48 +0200 writes: > Hi! I have non-normal data (items are continuous on a > 9-point scale, but not normally distributed) and I want to > conduct cfa. Which of the estimators available in lavaan > do you recommend me to use? Thanks in advance! I think you want *robust* statistical methods then. Robust factor analysis (if 'cfa' means something like that) is somewhat prominent topic. Simple approaches will already be available by using MASS::cov.rob() for a robust covariance matrix which you then can pass to other methods. For more (and more modern) methods and approaches, - for R packages, I'd recommend you consult the CRAN task view about robust statistical methods, https://cran.r-project.org/web/views/Robust.html notably the section on 'Multivariate Analysis' - for more specific help and expertise, I strongly recommend the dedicated R-SIG-robust mailing list (instead of R-help), --> https://stat.ethz.ch/mailman/listinfo/r-sig-robust Best regards, Martin Maechler, ETH Zurich > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] Estimators for non-normal data
Hi! I have non-normal data (items are continuous on a 9-point scale, but not normally distributed) and I want to conduct cfa. Which of the estimators available in lavaan do you recommend me to use? Thanks in advance! [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
