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.fi...@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. [[alternative HTML version deleted]]
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