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📄 make_neighborsw.m

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function W = make_neighborsw(xc,yc,m)% PURPOSE: constructs a row-stochastic nearest neighbor spatial weight matrix%          asymmetric, but row-sums are unity, based on m neighbors% --------------------------------------------------------% USAGE: W = make_neighborsw(xc,yc,nn)%       where: %             xc = x-coordinate for each obs (nobs x 1)%             yc = y-coordinate for each obs (nobs x 1)%             nn = # of nearest neighbors to be used% --------------------------------------------------------% RETURNS: W an (nobs x nobs) spatial weight matrix based on nn%          nearest neighbors (a sparse matrix)% --------------------------------------------------------% NOTES: %        W takes a form such that: W*y would produce a vector%        consisting of the values of y for the nn nearest neighbors%        for each observation i in the (nobs x 1) vector y% To construct a weight matrix based on 4 nearest neighbors% W4 = make_neighborsw(xc,yc,4);%   ---> This function will is similar to make_nnw, but uses less%        memory and takes more time. If you run out of memory using%        make_nnw, try this function% --------------------------------------------------------% SEE ALSO: find_neighbors(), find_nn(), make_nnw()% --------------------------------------------------------% written by:% James P. LeSage, 5/2002% updated 1/2003% Dept of Economics% University of Toledo% 2801 W. Bancroft St,% Toledo, OH 43606% jlesage@spatial-econometrics.comif nargin == 3[n junk] = size(xc);    else,error('make_neighborsw: Wrong # of input arguments');end;nnlist = find_neighbors(xc,yc,m);% convert the list into a row-standardized spatial weight matrixrowseqs=(1:n)';vals1=ones(n,1)*(1/m);vals0=zeros(n,1);for i=1:m;colseqs=nnlist(:,i);ind_to_keep=logical(colseqs>0);z1=[rowseqs colseqs vals1];z1=z1(ind_to_keep,:);z2=[rowseqs rowseqs vals0];%this last statement makes sure the dimensions are rightz=[z1   z2];if i == 1    W = spconvert(z);else    W = W + spconvert(z);end;end;

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