[R-sig-Geo] LM tests

[Jill Caviglia-Harris]

Roger:

>Have you tried (probably yes) and does it make a difference? Are the 
> results from a binary IDW and a row standardized 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)?

Yes, the IDW weights are symmetric - each observation in the sample is
considered a neighbor - therefore the inverse distance between the
neighbors indicates the "degree of neighborliness"  I will row
standardize these numbers and look into a rule for determining a
neighbor form a non-neighbor in my sample (for the binary weight matrix)
and get back to you about the differences.  

> 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?

I haven't tried these other ways of defining the weights matrix (as of
yet) because of Anselin (1988) "...distance decay has a meaningful
economic interpretation, scaling the rows so that the weights sum to one
may result in a loss of that interpretation"

-Jill



>>> Roger Bivand <Roger.Bivand at nhh.no> 02/27/04 02:40PM >>>
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
> 
> _______________________________________________
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> R-sig-Geo at stat.math.ethz.ch 
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> 

-- 
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