Moran's I negative binomial generalized linear model
On Fri, 23 Apr 2010, Wild Life wrote:
Yes I know the work of Jacqmin-Gadda et al. (1997) which proposes the use of a new statistic T. However, more recent work of Lin and Zhang (2007) uses the test of Moran's I to access the autocorrelation of residuals. I'm more interested in using this approach, which has been used by several authors. The question would be if I should calculate the test using a sparse matrix (lm.morantest) since it's on residuals or if I should calculate it by the normal way (moran.test) as suggested by Lin and Zhang (2007).
If you examine the class of a model object fitted using glm(), you'll see that it also inherits from "lm". This means that you may use lm.morantest() at your own risk. This is not the same as moran.test(), and I do not believe that Lin and Zhang suggested its use. I have no idea why you think that sparse matrices are involved. In testing model residuals, it is essential to handle the presence of the X variables, which is what lm.morantest() does. Since no studies have been done to find out how accurately this test detects spatial autocorrelation in glm errors, it is premature to simply extend findings for lm objects. It is also unclear whether this is what Lin and Zhang did, as I am unaware of them having published their code. Hope this helps, Roger
Many thanks for the reply. Regards, Ant?nio Silva On Tue, Apr 20, 2010 at 11:15 AM, Roberto Patuelli <roberto.patuelli at usi.ch> wrote:
Dear Antonio, You may want to look at Jacqmin-Gadda et al. (1997), Tests of Geographical Correlation with Adjustment for Explanatory Variables: An Application to Dyspnoea in the Elderly, Statistics in Medicine 16, 1283-97. Cheers Roberto **************************** Dear list, I'm interested in testing the spatial autocorrelation of the residuals of an negative binomial generalized linear model (glm.nb) using Moran's I test. My question is about the best method to do this. Can I use the function lm.morantest? Or is there another more appropriate way? Regards, Antonio Silva ******************** Roberto Patuelli, Ph.D. Istituto Ricerche Economiche (IRE) (Institute for Economic Research) Universit? della Svizzera Italiana (University of Lugano) via Maderno 24, CP 4361 CH-6904 Lugano Switzerland Phone: +41-(0)58-666-4166 Fax: +39-02-700419665 Email: roberto.patuelli at usi.ch Homepage: http://www.people.lu.unisi.ch/patuellr
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Roger Bivand Economic Geography Section, Department of Economics, Norwegian School of Economics and Business Administration, Helleveien 30, N-5045 Bergen, Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43 e-mail: Roger.Bivand at nhh.no