From: Rick Bilonick <rab45+_at_pitt.edu>

Date: Thu 17 Aug 2006 - 03:07:14 EST

R-help@stat.math.ethz.ch 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. Received on Thu Aug 17 03:22:21 2006

Date: Thu 17 Aug 2006 - 03:07:14 EST

On Wed, 2006-08-16 at 08:47 -0400, John Fox wrote:

> Dear Rick,

*>
**> There are a couple of problems here:
**>
**> (1) You've fixed the error variance parameters for each of the observed
**> variables to 1 rather than defining each as a free parameter to estimate.
**> For example, use
**>
**> X1 <-> X1, theta1, NA
**>
**> Rather than
**>
**> X1 <-> X1, NA, 1
**>
**> The general principle is that if you give a parameter a name, it's a free
**> parameter to be estimated; if you give the name as NA, then the parameter is
**> given a fixed value (here, 1). (There is some more information on this and
**> on error-variance parameters in ?sem.)
**>
**> (2) I believe that the model you're trying to specify -- in which all
**> variables but X6 load on F1, and all variables but X1 load on F2 -- is
**> underidentified.
**>
**> In addition, you've set the metric of the factors by fixing one loading to
**> 0.20 and another to 0.25. That should work but strikes me as unusual, and
**> makes me wonder whether this was what you really intended. It would be more
**> common in a CFA to fix the variance of each factor to 1, and let the factor
**> loadings be free parameters. Then the factor covariance would be their
**> correlation.
**>
**> You should not have to specify start values for free parameters (such as
**> g11, g22, and g12 in your model), though it is not wrong to do so. I would
**> not, however, specify start values that imply a singular covariance matrix
**> among the factors, as you've done; I'm surprised that the program was able
**> to get by the start values to produce a solution.
**>
**> BTW, the Thurstone example in ?sem is for a confirmatory factor analysis
**> (albeit a slightly more complicated one with a second-order factor). There's
**> also an example of a one-factor CFA in the paper at
**> <http://socserv.socsci.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf>, though this
**> is for ordinal observed variables.
**>
**> I hope this helps,
**> John
**>
**> --------------------------------
**> John Fox
**> Department of Sociology
**> McMaster University
**> Hamilton, Ontario
**> Canada L8S 4M4
**> 905-525-9140x23604
**> http://socserv.mcmaster.ca/jfox
**> --------------------------------
*

Thanks for the information. I think I understand how to handle the residual variance after reading the sem help file more carefully. Now I have to figure out how to constrain each column of the factor matrix to sum to one. Maybe this will fix the problem with being under-identified.

Rick B.

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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. Received on Thu Aug 17 03:22:21 2006

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