LM tests

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LM tests

Jill Caviglia-Harris
List members:

I have been using the function lm.LMtests developed using the spdep
package to test for spatial lag and error.  My problem is that these
tests assume that the weights matrix is row standardized, while I have a
weights matrix that is set up as the inverse distance between neighbors.
  Converting it into a row standardized matrix would result in the loss
of important information.  Have there been any functions developed that
any of you know about that are not dependent upon this assumption?
Thanks.  -Jill


***************************************************
Jill L. Caviglia-Harris, Ph.D.
Assistant Professor
Economics and Finance Department
Salisbury University
Salisbury, MD 21801-6860
   phone: (410) 548-5591
   fax: (410) 546-6208



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LM tests

Roger Bivand
Administrator
On Fri, 27 Feb 2004, Jill Caviglia-Harris wrote:

> List members:
>
> I have been using the function lm.LMtests developed using the spdep
> package to test for spatial lag and error.  My problem is that these
> tests assume that the weights matrix is row standardized, while I have a
> weights matrix that is set up as the inverse distance between neighbors.

Certainly lm.LMtests() prints a warning, and the tradition it comes from
usually presupposes row standardisation. Curiously, quite a lot of the
distribution results in Cliff and Ord actually assume symmetry, which can
lead to fun with negative variance in Geary's C and join count statistics
even with row standardised weights.

>   Converting it into a row standardized matrix would result in the loss
> of important information.  Have there been any functions developed that
> any of you know about that are not dependent upon this assumption?

Have you tried (probably yes) and does it make a difference? Are the
results from a binary IDW and a row standardised IDW very different? Is
your IDW matrix full or sparse? Can Moran's I be applied instead (despite
its covering lots of misspecification problems)? Are the IDW weights
symmetric (probably, but not always)?

I'm not sure why distances should be helpful if the data are observed on
areal units, so that measuring distances is between arbitrarily chosen
points in those units, a change of support problem. That may be why there
aren't methods too, though there's no reason not to try to develop things.
But error correlation specified by distance does movbe rather close to
geostatistics, doesn't it?

Any other views, anyone?

Roger

> Thanks.  -Jill
>
>
> ***************************************************
> Jill L. Caviglia-Harris, Ph.D.
> Assistant Professor
> Economics and Finance Department
> Salisbury University
> Salisbury, MD 21801-6860
>    phone: (410) 548-5591
>    fax: (410) 546-6208
>
> _______________________________________________
> R-sig-Geo mailing list
> R-sig-Geo at stat.math.ethz.ch
> https://www.stat.math.ethz.ch/mailman/listinfo/r-sig-geo
>

--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Breiviksveien 40, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 93 93
e-mail: Roger.Bivand at nhh.no



Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N-5045 Bergen, Norway