Indeed, looking at sem.R in the package, we see that at the heart of  
sem is a version of the maximum likelihood discrepancy function.  It  
should be easy to use, say, another flag (e.g. set the default to  
method="ML" for the current behavior) and for other methods, use  
different discrepancy functions.  One would only need an if statement.

A little extra work might be needed to incorporate ADF methods, but it  
should not be intractable.  Note, the sem package is on r-forge.

-Jarrett


----------------------------------------
Jarrett Byrnes
Postdoctoral Associate, Santa Barbara Coastal LTER
Marine Science Institute
University of California Santa Barbara
Santa Barbara, CA 93106-6150
http://www.lifesci.ucsb.edu/eemb/labs/cardinale/people/byrnes/index.html

On Dec 2, 2009, at 10:22 AM, Jeremy Miles wrote:

> In the world of SEM, GLS has pretty much fallen by the wayside - I
> can't recall anything I've seen arguing for it's use in the past 10
> years, and I also can't recall anyone using it over ML.   The
> recommendations for non-normal distributions tend to be robust-ML, or
> robust weighted least squares.  These are more computationally
> intensive, and I *think* that John Fox (author of sem) has written
> somewhere that it wouldn't be possible to implement them within R,
> without using a lower level language - or rather that it might be
> possible, but it would be really, really slow.
>
> However, ML and GLS are pretty similar, if you dug around in the
> source code, you could probably make the change (see,
> http://www2.gsu.edu/~mkteer/discrep.html for example, for the
> equations; in fact GLS is somewhat computationally simpler, as you
> don't need to invert the implied covariance matrix at each iteration).
> However, the fact that it's not hard to make the change, and that no
> one has made the change, is another argument that it's not a change
> that needs to be made.
>
> Jeremy
>
>
>
> 2009/12/2 Ralf Finne <Ralf.Finne@novia.fi>:
>> Hi R-colleagues.
>>
>> I have been using the sem(sem) function.  It uses
>> maximum likelyhood as optimizing. method.
>> According to simulation study in Umeå Sweden
>> (http://www.stat.umu.se/kursweb/vt07/stad04mom3/?download=UlfHolmberg.pdf
>> Sorry it is in swedish, except the abstract)
>> maximum likelihood is OK for large samples and normal distribution
>> the SEM-problem should be optimized by GLS (Generalized Least  
>> Squares).
>>
>>
>> So to the question:
>>
>> Is there any R-function that solves SEM with GLS?
>>
>>
>> Ralf Finne
>> Novia University of Applied Science
>> Vasa  Finland
>>
>> ______________________________________________
>> 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.
>>
>
>
>
> -- 
> Jeremy Miles
> Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
>
> ______________________________________________
> 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.


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