matlab 加权回归估计_matlab代码：地理加权回归（GWR）示例

【实例简介】地理加权回归(GWR)matlab代码，亲测可用，该代码利用matlab实现了地理加权回归的代码，内附实际算例。

【实例截图】

【核心代码】

function result = gwr(y,x,east,north,info);

% PURPOSE: compute geographically weighted regression

%—————————————————-

% USAGE: results = gwr(y,x,east,north,info)

% where: y = dependent variable vector

% x = explanatory variable matrix

% east = x-coordinates in space

% north = y-coordinates in space

% info = a structure variable with fields:

% info.bwidth = scalar bandwidth to use or zero

% for cross-validation estimation (default)

% info.bmin = minimum bandwidth to use in CV search

% info.bmax = maximum bandwidth to use in CV search

% defaults: bmin = 0.1, bmax = 20

% info.dtype = ‘gaussian’ for Gaussian weighting (default)

% = ‘exponential’ for exponential weighting

% = ‘tricube’ for tri-cube weighting

% info.q = q-nearest neighbors to use for tri-cube weights

% (default: CV estimated)

% info.qmin = minimum # of neighbors to use in CV search

% info.qmax = maximum # of neighbors to use in CV search

% defaults: qmin = nvar 2, qmax = 4*nvar

% —————————————————

% NOTE: res = gwr(y,x,east,north) does CV estimation of bandwidth

% —————————————————

% RETURNS: a results structure

% results.meth = ‘gwr’

% results.beta = bhat matrix (nobs x nvar)

% results.tstat = t-stats matrix (nobs x nvar)

% results.yhat = yhat

% results.resid = residuals

% results.sige = e’e/(n-dof) (nobs x 1)

% results.nobs = nobs

% results.nvar = nvars

% results.bwidth = bandwidth if gaussian or exponential

% results.q = q nearest neighbors if tri-cube

% results.dtype = input string for Gaussian, exponential weights

% results.iter = # of simplex iterations for cv

% results.north = north (y-coordinates)

% results.east = east (x-coordinates)

% results.y = y data vector

%—————————————————

% See also: prt,plt, prt_gwr, plt_gwr to print and plot results

%—————————————————

% References: Brunsdon, Fotheringham, Charlton (1996)

% Geographical Analysis, pp. 281-298

%—————————————————

% NOTES: uses auxiliary function scoref for cross-validation

%—————————————————

% written by: James P. LeSage 2/98

% University of Toledo

% Department of Economics

% Toledo, OH 43606

% jpl@jpl.econ.utoledo.edu

if nargin == 5 % user options

if ~isstruct(info)

error(‘gwr: must supply the option argument as a structure variable’);

else

fields = fieldnames(info);

nf = length(fields);

% set defaults

[n k] = size(x);

bwidth = 0; dtype = 0; q = 0; qmin = k 2; qmax = 5*k;

bmin = 0.1; bmax = 20.0;

for i=1:nf

if strcmp(fields{i},’bwidth’)

bwidth = info.bwidth;

elseif strcmp(fields{i},’dtype’)

dstring = info.dtype;

if strcmp(dstring,’gaussian’)

dtype = 0;

elseif strcmp(dstring,’exponential’)

dtype = 1;

elseif strcmp(dstring,’tricube’)

dtype = 2;

end;

elseif strcmp(fields{i},’q’)

q = info.q;

elseif strcmp(fields{i},’qmax’);

qmax = info.qmax;

elseif strcmp(fields{i},’qmin’);

qmin = info.qmin;

elseif strcmp(fields{i},’bmin’);

bmin = info.bmin;

elseif strcmp(fields{i},’bmax’);

bmax = info.bmax;

end;

end; % end of for i

end; % end of if else

elseif nargin == 4

bwidth = 0; dtype = 0; dstring = ‘gaussian’;

bmin = 0.1; bmax = 20.0;

else

error(‘Wrong # of arguments to gwr’);

end;

% error checking on inputs

[nobs nvar] = size(x);

[nobs2 junk] = size(y);

[nobs3 junk] = size(north);

[nobs4 junk] = size(east);

result.north = north;

result.east = east;

if nobs ~= nobs2

error(‘gwr: y and x must contain same # obs’);

elseif nobs3 ~= nobs

error(‘gwr: north coordinates must equal # obs’);

elseif nobs3 ~= nobs4

error(‘gwr: east coordinates must equal # in north’);

end;

switch dtype

case{0,1} % bandwidth cross-validation

if bwidth == 0 % cross-validation

options = optimset(‘fminbnd’);

optimset(‘MaxIter’,500);

if dtype == 0 % Gaussian weights

[bdwt,junk,exitflag,output] = fminbnd(‘scoref’,bmin,bmax,options,y,x,east,north,dtype);

elseif dtype == 1 % exponential weights

[bdwt,junk,exitflag,output] = fminbnd(‘scoref’,bmin,bmax,options,y,x,east,north,dtype);

end;

if output.iterations == 500,

fprintf(1,’gwr: cv convergence not obtained in %4d iterations’,output.iterations);

else

result.iter = output.iterations;

end;

else

bdwt = bwidth*bwidth; % user supplied bandwidth

end;

case{2} % q-nearest neigbhor cross-validation

if q == 0 % cross-validation

q = scoreq(qmin,qmax,y,x,east,north);

else

% use user-supplied q-value

end;

otherwise

end;

% do GWR using bdwt as bandwidth

[n k] = size(x);

bsave = zeros(n,k);

ssave = zeros(n,k);

sigv = zeros(n,1);

yhat = zeros(n,1);

resid = zeros(n,1);

wt = zeros(n,1);

d = zeros(n,1);

for iter=1:n;

dx = east – east(iter,1);

dy = north – north(iter,1);

d = (dx.*dx dy.*dy);

sd = std(sqrt(d));

% sort distance to find q nearest neighbors

ds = sort(d);

if dtype == 2, dmax = ds(q,1); end;

if dtype == 0, % Gausian weights

wt = stdn_pdf(sqrt(d)/(sd*bdwt));

elseif dtype == 1, % exponential weights

wt = exp(-d/bdwt);

elseif dtype == 2, % tricube weights

wt = zeros(n,1);

nzip = find(d <= dmax);

wt(nzip,1) = (1-(d(nzip,1)/dmax).^3).^3;

end; % end of if,else

wt = sqrt(wt);

% computational trick to speed things up

% use non-zero wt to pull out y,x observations

nzip = find(wt >= 0.01);

ys = y(nzip,1).*wt(nzip,1);

xs = matmul(x(nzip,:),wt(nzip,1));

xpxi = invpd(xs’*xs);

b = xpxi*xs’*ys;

% compute predicted values

yhatv = xs*b;

yhat(iter,1) = x(iter,:)*b;

resid(iter,1) = y(iter,1) – yhat(iter,1);

% compute residuals

e = ys – yhatv;

% find # of non-zero observations

nadj = length(nzip);

sige = (e’*e)/nadj;

% compute t-statistics

sdb = sqrt(sige*diag(xpxi));

% store coefficient estimates and std errors in matrices

% one set of beta,std for each observation

bsave(iter,:) = b’;

ssave(iter,:) = sdb’;

sigv(iter,1) = sige;

end;

% fill-in results structure

result.meth = ‘gwr’;

result.nobs = nobs;

result.nvar = nvar;

if (dtype == 0 | dtype == 1)

result.bwidth = sqrt(bdwt);

else

result.q = q;

end;

result.beta = bsave;

result.tstat = bsave./ssave;

result.sige = sigv;

result.dtype = dstring;

result.y = y;

result.yhat = yhat;

% compute residuals and conventional r-squared

result.resid = resid;

sigu = result.resid’*result.resid;

ym = y – mean(y);

rsqr1 = sigu;

rsqr2 = ym’*ym;

result.rsqr = 1.0 – rsqr1/rsqr2; % r-squared

rsqr1 = rsqr1/(nobs-nvar);

rsqr2 = rsqr2/(nobs-1.0);

result.rbar = 1 – (rsqr1/rsqr2); % rbar-squared

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