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On Fri, 24 Oct 2003, Volker Bahn wrote:
> >  On Wed, 22 Oct 2003, Volker Bahn wrote: >  >  > Dear Roger, >  > >  > thanks for your tips. I will check Haining. Concerning your latter > comment: >  >  >  >  Perhaps just fit lm(), AIC should be OK, as should the likelihood > ratio >  >  test. I don't have access to the software, so I'm generalising from > how >  >  similar classes work in R. >  > >  > I initially fit my "null" model in lm() but the resulting loglik was so >  > different from the corresponding slm() loglik that a comparsion was not >  > possible. I figured that this must be because of the different > optimisation >  > processes in lm() and slm(). >  >  I wouldn't think so  the basic R logic in logLik.lm() is: >  >  val < 0.5 * (sum(log(w))  N * (log(2 * pi) + 1  log(N) + >  log(sum(w * res^2)))) >  >  where w is a vector of 1's if there are no weights. val is adjusted for >  REML estimators, but otherwise is as you would expect. Maybe simply the >  slm() is a much better fit? >  > > This is starting to be out of my league  I don't really know or understand > the formula or its consequences. What I do know, though, is that my loglik > of "null" models calculated with lm() was actually around 0 while the loglik > of the equivalent slm() model was around 2000 suggesting that the slm() was > way worse. I then used the following formula to calculate the loglik of > slm() myself from the RSE, but, as I learned later, this formula really only > applies to OLS regressions: > > Loglik = (n / 2) * LN(RSE^2 * df / n)  (n / 2) * LN(2*PI())  (n / 2) > > The results were pretty credible, though: The slm() were just about as much > better than the corresponding lm() as I had expected. My next line of attack > was to set the spatial coefficient rho or just "parameter" as it is called > in Splus to 0 to get a comparable loglik for the nonspatial model. I > accomplished > this indirectly through a likelihood ratio test (lrt.slm), which allows me > to control the parameter settings for the reduced model. However, I would > prefer to be able to do this directly so that I can see whether the > resulting model is really similar in coefficients and all to an lm model. > Looking through the help files didn't show me an obvious way to accomplish > this and looking through the functions themselves is just a little bit over > my head. If you would happen to know how to control the spatial parameter in > slm() or would have other suggestions I would appreciate to know. Since I don't have access to SPLUS myself, I don't think I can see how to help. Does anyone have access to SpatialStats and feel able to help (CCed to rsiggeo)? I'm pretty uncomfortable with your logLik by hand, because the Jacobian seems to be omitted, is that correct? Roger > > Cheers > > Volker > > >  Roger Bivand Economic Geography Section, Department of Economics, Norwegian School of Economics and Business Administration, Breiviksveien 40, N5045 Bergen, Norway. voice: +47 55 95 93 55; fax +47 55 95 93 93 email: Roger.Bivand at nhh.no
Roger Bivand
Department of Economics Norwegian School of Economics Helleveien 30 N5045 Bergen, Norway 
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