On Thu, 31 Oct 2019, Letícia Dal' Canton wrote:
In my Ph.D. I am comparing Lee and Anselin's methodology for estimating bivariate spatial autocorrelation. Lee's methodology led me to the spdep package. My questions are as follows:1) The weight matrix W, which is part of the "nb2listw" function. First, is it possible to view all elements of this matrix? Because I could only visualize the elements whose autocorrelation is not null. That would be enough, but I need to present this matrix and visualize the autocorrelation between all points. That is, knowing what are the coordinates of the points where the autocorrelation is nonzero and also where it is null.
This is very unclear. Yes, nb2mat() gives a dense matrix, but you do not show how you "visualize the elements whose autocorrelation is not null". You need to add a reproducible example using a built-in data set. Note that the matrix showing the variance-covariance of the observations is dense by definition (I - \rho W)^{-1} - see Melanie Wall's 2004 article.
2) In the "dnearneight" function, is it possible to view the matrix of neighbors? Because getting this matrix, would be able to standardize and get the matrix of weights W without using the nb2list function.
You do not explain why this makes sense. Of course you can use nb2mat(), listw2mat() or coerce a listw object to a sparse matrix, but you need to motivate this (best with an example).
Roger
Thankfully, Let???cia Dal' Canton. Matem???tica. Mestre em Engenharia Agr???cola. Doutoranda em Engenharia Agr???cola com linha de pesquisa em Estat???stica Espacial. Universidade Estadual do Oeste do Paran??? - UNIOESTE. Curr???culo Lattes: http://lattes.cnpq.br/1085422685501012. Contato: (45) 9 9962-7492. [[alternative HTML version deleted]]
-- Roger Bivand Department of Economics, Norwegian School of Economics, Helleveien 30, N-5045 Bergen, Norway. voice: +47 55 95 93 55; e-mail: roger.biv...@nhh.no https://orcid.org/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
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