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

>

> _______________________________________________

> 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