Actually wouldn't the row standardization imply that the weights become
(1/dij)/ sum dij)? The objective is get the rows to sum to 1. I answered your other question in the last email. (I won't repeat) It appears that row standardizing will change the economics interpretation (therefore the coefficient values). Something that I will find interesting to look into empirically. I'll get back to you. -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 ********************************************************* >>> "Munroe, Darla K" <dkmunroe at email.uncc.edu> 02/27/04 03:06PM >>> oops - I meant to say - with row-standardization your weights become: 1/dij/n-1 (with 0 elements on the diagonal). So, you've just rescaled dij by n-1 for all i, but you haven't lost the ranking of the distances from low to high. -----Original Message----- From: Roger Bivand To: Jill Caviglia-Harris Cc: r-sig-geo at stat.math.ethz.ch Sent: 2/27/04 2:40 PM Subject: Re: [R-sig-Geo] LM tests 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 _______________________________________________ R-sig-Geo mailing list R-sig-Geo at stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list R-sig-Geo at stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/r-sig-geo |
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