Input argument "north" is undefined

*i'm trying to run the function below:

function result = gwolr(y,x,xl,xg,east,north,info);
 % memeriksa data input
if nargin == 7 % user options
 if ~isstruct(info)
    error('gwolr: must supply the option argument as a structure variable');
 else
 fields = fieldnames(info);
 nf = length(fields);
 [nobs1 nxl] = size(xl);
 [nobs2 nxg] = size(xg);
 [nobs nvar] = size(x);
 [nobs3 junk] = size(y);
 [nobs4 junk] = size(north);
 [nobs5 junk] = size(east);
 result.north = north;
 result.east = east;
 if nobs ~= nobs3
  error('gwolr: y and x must contain same # obs');
 elseif nobs1 ~= nobs3
  error('gwolr:xl and x must contain same #obs');
 elseif nobs2 ~= nobs1
  error('gwolr:xg and x must contain same #obs');
 elseif nobs4 ~= nobs
  error('gwolr: north coordinates must equal # obs');
 elseif nobs4 ~= nobs5
  error('gwolr: east coordinates must equal # in north');
 end;

    stdx = ones(nobs,1)*std(x);
    xmean = ones(nobs,1)*mean(x);  
    xstd = x ./ stdx ;     % standardize x
    xnew = [xstd(:,1) x(:,2) xstd(:,3:5)];
    x = xnew;
    ymin = min(y); 
    ymax = max(y); 
    G = ymax;
    ncat = ymax - ymin;
    d0 = ( y*ones(1,ncat+1) ) == ( ones(nobs,1)*(ymin:ymax) );
    yd  = ( y*ones(1,ncat) ) == ( ones(nobs,1)*(ymin:(ymax-1)) );
    yd1 = ( y*ones(1,ncat) ) == ( ones(nobs,1)*((ymin+1):ymax) );
    yd  = yd(:,any(yd)); 
    yd1 = yd1(:,any(yd1)); 
    [ryd cyd] = size(yd);
    [rd0 cd0] = size(d0);

% nilai batasan untuk bandwidth
 bwidth = 0; dtype = 1; 
 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 = 1;
       elseif strcmp(dstring,'exponential')
        dtype = 2;
       elseif strcmp(dstring,'tricube')
        dtype = 3;
        elseif strcmp(dstring,'bisquare')
        dtype = 4;
       end;
    elseif strcmp(fields{i},'bmin');
        bmin = prior.bmin;   
    elseif strcmp(fields{i},'bmax');
        bmax = prior.bmax; 
    end; 
  end; 
 end; 
elseif nargin == 6
 bwidth = 0;  dtype = 1; dstring = 'gaussian'; bmin = 0.1; bmax = 20.0;
else
 error('Wrong # of arguments to gwolr');
end;

% penentuan bandwidth optimum dengan metode CV
if bwidth == 0 
options = optimset('fminbnd');
optimset('MaxIter',500);
if dtype == 1     % Gaussian weights
[bdwt,junk,exitflag,output] = fminbnd('scoreCV_gwolr',bmin,bmax,options,y,x,east,north,dtype);
elseif dtype == 2 % exponential weights
[bdwt,junk,exitflag,output] = fminbnd('scoreCV_gwolr',bmin,bmax,options,y,x,east,north,dtype);
elseif dtype == 3 % tricube weights
[bdwt,junk,exitflag,output] = fminbnd('scoreCV_gwolr',bmin,bmax,options,y,x,east,north,dtype);
elseif dtype == 4 % bisquare weights
[bdwt,junk,exitflag,output] = fminbnd('scoreCV_gwolr',bmin,bmax,options,y,x,east,north,dtype);
end;           
 if output.iterations == 500,
 fprintf(1,'gwolr: cv convergence not obtained in %4d iterations',output.iterations);
 else
 result.iter = output.iterations;
 end;
else
 bdwt = bwidth*bwidth; % user supplied bandwidth
end;       
% penaksiran parameter model GWOLR
for i = 1:nobs;
    dx = east - east(i,1);
    dy = north - north(i,1);
    d = (dx.*dx + dy.*dy);
    d = sqrt(d);
    sd = std(d);
    if dtype == 1,     % Pembobot Gausian (Le Sage )
        wt = normpdf(d/(sd*bdwt));                    
       elseif dtype == 2, % Pembobot exponential
        wt = exp(-(d/bdwt).^2);
       elseif dtype == 4, % Pembobot Bisquare
         wt = zeros(nobs,1);
         nzip = find(d <= bdwt);
         wt(nzip,1) = (1-(d(nzip,1)/bdwt).^2).^2;  
       elseif dtype == 3, % Pembobot tricube 
         wt = zeros(nobs,1);
         nzip = find(d <= bdwt);
         wt(nzip,1) = (1-(d(nzip,1)/bdwt).^3).^3;
       end; % end of if,else
    wt(:,i) = wt;  

      % nilai awal untuk theta nol
      betanol = zeros(nl,1);
      gammanol = zeros(ng,1)
      ydwt = dmult(wt(:,i),yd);
      g0 = cumsum(sum(ydwt))'./sum(wt(:,i)); 
      alfanol = log(g0./(1-g0));
      thetanol = [alfanol;betanol;gammanol];

      % mendapatkan vektor q dan matriks H untuk theta nol
      e  = exp( [yd x]*thetanol ); 
      e1 = exp( [yd1 x]*thetanol );
      g  = e./(1+e);
      g1 = e1./(1+e1);
      g  = max( y==max(y),g );
      g1 = min( y>min(y),g1 );
      p = g-g1;
      % first derivative (vektor q)
      v = g.*(1-g)./p;
      v1 = g1.*(1-g1)./p;
      dlogp = [dmult(v,yd)-dmult(v1,yd1) dmult(v-v1,x)];
      dlogpwt = dmult(wt(:,i),dlogp);
      q = sum(dlogpwt)';
      % second derivative (H)
      w = v.*(1-2*g);
      w1 = v1.*(1-2*g1);
      s = dmult(w,[yd x]);
      t = dmult(w1,[yd1 x]);
      H = [yd x]'*dmult(wt(:,i),s)-[yd1 x]'*dmult(wt(:,i),t)-dlogp'*dlogpwt;
  % newton raphson
      iter = 0;
      theta = thetanol;
      tol=1e-6;
      while abs(q'*(H\q)/length(q)) > tol
          iter = iter+1;
          thetaold = theta;
          theta = thetaold - H\q;
          % mendapatkan vektor q dan matriks H untuk theta nol
           e  = exp                 ( [yd x]*theta ); 
           e1 = exp( [yd1 x]*theta );
           g  = e./(1+e);
           g1 = e1./(1+e1);
           g  = max( y==max(y),g );
           g1 = min( y>min(y),g1 );
            p = g-g1;
          % first derivative (vektor q)
            v = g.*(1-g)./p;
           v1 = g1.*(1-g1)./p;
           dlogp = [dmult(v,yd)-dmult(v1,yd1) dmult(v-v1,x)];
           dlogpwt = dmult(wt(:,i),dlogp);
            q = sum(dlogpwt)';
          % second derivative (H)
            w = v.*(1-2*g);
           w1 = v1.*(1-2*g1);
            s = dmult(w,[yd x]);
            t = dmult(w1,[yd1 x]);
            H = [yd x]'*dmult(wt(:,i),s)-[yd1 x]'*dmult(wt(:,i),t)-dlogp'*dlogpwt;
      end;
      thetatopi(i,:) = theta';
      alfatopi(i,:) = thetatopi(i,1:cyd);
      betatopi(i,:) = thetatopi(i,(cyd+1):(cyd+nl));
      gammatopi(i,:) = thetatopi(i,(cyd+1):(cyd+ng));

% penentuan derajat bebas model GWOLR
    X = [yd x];
    for j = 1:nobs;
        W(j,j) = wt(j,i);  % matriks W
        nuu(j,:) =((xl(j,:)*(betatopi(i,:)'))*ones(1,cyd))+ ((xg(j,:)*(gammatopi(i,:)'))*ones(1,cyd))+ alfatopi(i,:);
        p2(j,:) = [0 exp(nuu(j,:))./(1+exp(nuu(j,:))) 1];
        pi(j,:) = diff(p2(j,:)')';
        if y(j,:) == 1;
            phiji(j,i) = pi(j,1);
            z(j,i) = (1-phiji(j,i))/sqrt(phiji(j,i)*(1-phiji(j,i)));
        elseif y(j,:) == 2;
            phiji(j,i) = pi(j,2);
            z(j,i) = (1-phiji(j,i))/sqrt(phiji(j,i)*(1-phiji(j,i)));

elseif y(j,:) == 3; 
            phiji(j,i) = pi(j,3);
            z(j,i) = (1-phiji(j,i))/sqrt(phiji(j,i)*(1-phiji(j,i)));
        end;
        A(j,j) = phiji(j,i)*(1-phiji(j,i)); % matriks A
     end;
    R(i,:) = X(i,:)*inv(X'*W*A*X)*X'*W*A; % matriks R
    nu(i,:) =((x(i,:)*(betatopi(i,:)'))*ones(1,cyd))+ alfatopi(i,:);
    p1(i,:) = [0 exp(nu(i,:))./(1+exp(nu(i,:))) 1];
    etopi  = exp( [yd(i,:) x(i,:)]*thetatopi(i,:)' ); 
    e1topi = exp( [yd1(i,:) x(i,:)]*thetatopi(i,:)' ); 
    gtopi  = etopi/(1+etopi);
    g1topi = e1topi/(1+e1topi);
    gtopi  = max( y(i,:)==max(y),gtopi );
    g1topi = min( y(i,:)>min(y),g1topi );
    phitopi(i,:) = gtopi - g1topi;
    AA(i,i) = phitopi(i,:)*(1-phitopi(i,:));
    % uji parameter model GWOLR secara parsial
    se(i,:) = sqrt(diag(inv(-H)));
    zstat(i,:)= thetatopi(i,:)./se(i,:);
end;
for i = 1:nobs;
    for j = 1:nobs;
        S(i,j) = R(i,j)*z(i,j)/z(j,j); % matriks S
    end;
end;
K = trace(S); % jumlah parameter model 
Kvar = trace(S'*AA*S*inv(AA));
df0 = nobs - trace(S); % df model GWOLR
df = nobs - trace(2*S-S'*AA*S*inv(AA)); %df residual

%uji kesamaan model GWOLR dan regresi logistik ordinal
dev1 = 239.063 ; % devians model regresi logistik ordinal
df1 = 255;% df model regresi logistik ordinal
like1 = sum(log(phitopi));
devians = -2*like1;
Fhit = (dev1/df1)/(devians/df);

% uji parameter model GWOLR secara serentak
 for i = 1:nobs;
    enol  = exp( yd(i,:)*alfanol ); 
    e1nol = exp( yd1(i,:)*alfanol ); 
    gnol  = enol/(1+enol);
    g1nol = e1nol/(1+e1nol);
    gnol  = max( y(i,:)==max(y),gnol );
    g1nol = min( y(i,:)>min(y),g1nol );
    phi_nol(i,:) = gnol-g1nol;
 end;
 like0 = sum(log(phi_nol)); %maximumm likelihood dibawah Ho
 like1 = sum(log(phitopi)); %maximumm likelihood dibawah H1
 G2 = -2*(like0-like1);
 %ukuran kebaikan model
 AIC = devians + 2*K;
 % nilai phi masing-masing kategori
  pp = diff(p1')';

%ringkasan statistik koordinat
    minkoord(1,:) = min(east);
    minkoord(2,:) = min(north);
    maxkoord(1,:) = max(east);
    maxkoord(2,:) = max(north);
    rangekoord(1,:) = max(east) - min(east) ;
    rangekoord(2,:) = max(north) - min(north);
%ringkasan statistik parameter
for i = 1:nvar+cyd;
    mintheta(i,:) = min(thetatopi(:,i));
    maxtheta(i,:) = max(thetatopi(:,i));
    meantheta(i,:) = mean(thetatopi(:,i));
    rangetheta(i,:) = max(thetatopi(:,i))-min(thetatopi(:,i));
    stdevtheta(i,:) = std(thetatopi(:,i));
end;

with the Score_CV function:

function score = scoreCV_gwolr(bdwt,y,x,xl,xg,east,north,flag)
% variabel indikator dari y
x = [xl xg];
[n ng] = size(xg);
[n nl] = size(xl);
[nobs nvar] = size(x);
ymin = min(y); 
ymax = max(y); 
ncat = ymax - ymin;
d0 = ( y*ones(1,ncat+1) ) == ( ones(nobs,1)*(ymin:ymax) );
yd  = ( y*ones(1,ncat) ) == ( ones(nobs,1)*(ymin:(ymax-1)) );
yd1 = ( y*ones(1,ncat) ) == ( ones(nobs,1)*((ymin+1):ymax) );
yd  = yd(:,any(yd)); 
yd1 = yd1(:,any(yd1)); 
[ryd cyd] = size(yd);
[rd0 cd0] = size(d0);

wt = zeros(nobs,1); 
for i = 1:nobs;
   dx = east - east(i,1);
   dy = north -north(i,1);
   d = (dx.*dx + dy.*dy);
   sd = std(sqrt(d));
   d = (dx.*dx + dy.*dy);
   d = sqrt(d);
   sd = std(d); 
   if flag == 1,     % Gausian weights 
      wt = normpdf(d/(sd*bdwt));
   elseif flag == 2, % Exponential weights
          wt = exp(-(d/bdwt).^2);
   elseif flag == 3, % Tricube weights
         wt = zeros(nobs,1);
         nzip = find(d <= bdwt);
         wt(nzip,1) = (1-(d(nzip,1)/bdwt).^3).^3;  
   elseif flag == 4, % Bisquare weights
         wt = zeros(nobs,1);
         nzip = find(d <= bdwt);
         wt(nzip,1) = (1-(d(nzip,1)/bdwt).^2).^2;  
   end; 
   wt(i,1) = 0.0;

     % nilai awal untuk theta nol
     betanol = zeros(nl,1);
     gammanol = zeros(ng,1)
     ydwt = dmult(wt,yd);
     g0 = cumsum(sum(ydwt))'./sum(wt); alfanol = log(g0./(1-g0));
     thetanol = [alfanol;betanol;gammanol];

      % mendapatkan vektor q dan matriks H untuk theta nol
      e  = exp( [yd x]*thetanol ); 
      e1 = exp( [yd1 x]*thetanol );
      g  = e./(1+e);
      g1 = e1./(1+e1);
      g  = max( y==max(y),g );
      g1 = min( y>min(y),g1 );
      p = g-g1;
      % first derivative (vektor q)
      v = g.*(1-g)./p;
      v1 = g1.*(1-g1)./p;
      dlogp = [dmult(v,yd)-dmult(v1,yd1) dmult(v-v1,x)];
      dlogpwt = dmult(wt,dlogp);
      q = sum(dlogpwt)';
      % second derivative (H)
      w = v.*(1-2*g);
      w1 = v1.*(1-2*g1);
      s = dmult(w,[yd x]);
      t = dmult(w1,[yd1 x]);
      H = [yd x]'*dmult(wt,s)-[yd1 x]'*dmult(wt,t)-dlogp'*dlogpwt;

      % newton raphson
      iter = 0;
      theta = thetanol;
      tol=1e-6;
      while abs(q'*(H\q)/length(q)) > tol
          iter = iter+1;
          thetaold = theta;
          theta = thetaold - H\q;
          % mendapatkan vektor q dan matriks H untuk theta nol
           e  = exp( [yd x]*theta ); 
           e1 = exp( [yd1 x]*theta );
           g  = e./(1+e);
           g1 = e1./(1+e1);
           g  = max( y==max(y),g );
           g1 = min( y>min(y),g1 );
            p = g-g1;
          % first derivative (vektor q)
            v = g.*(1-g)./p;
           v1 = g1.*(1-g1)./p;
           dlogp = [dmult(v,yd)-dmult(v1,yd1) dmult(v-v1,x)];
           dlogpwt = dmult(wt,dlogp);
            q = sum(dlogpwt)';
          % second derivative (H)
            w = v.*(1-2*g);
           w1 = v1.*(1-2*g1);
            s = dmult(w,[yd x]);
            t = dmult(w1,[yd1 x]);
            H = [yd x]'*dmult(wt,s)-[yd1 x]'*dmult(wt,t)-dlogp'*dlogpwt;
      end;
   % menghitung estimasi peluang
  alfa = theta(1:cyd,1);
  beta = theta((cyd+1):(cyd+nl),1);
  gamma = theta((cyd+1):(cyd+ng),1);
  etopi =((xl(i,:)*beta)*ones(1,cyd))+ ((xg(i,:)*gamma)*ones(1,cyd))+alfa';
  p1(i,:) = [0 exp(etopi)./(1+exp(etopi)) 1];
  end; 

% menghitung score CV
p = diff(p1')';
residual = d0 - p;
score = sum(sum(residual.^2));

result.meth = 'scoreCV_gwolr';

i'm calling the function of the gwolr with:

clear all;
% load the DBD data set
load('DBD.mat');
y = data(:,3);
nobs = length(y);
xl = data(:,7:8);
[n nl] = size(xl);
xg = data(:,4:6);
[n ng] = size(xg);
x = [xl xg];
[nobs nvar] = size(x);
east = data(:,1);
north = data(:,2);
vnames = strvcat('tinggi','rasio','abj','jarak','penduduk');
info.dtype = 'gaussian'; % Gaussian distance weighting
result = gwolr(y,x,xl,xg,east,north,info);

However I am getting an error of

??? Input argument "north" is undefined.

Error in ==> scoreCV_gwolr at 21
   dy = north -north(i,1);

Error in ==> fminbnd at 212
x= xf; fx = funfcn(x,varargin{:});

Error in ==> gwolr at 81
[bdwt,junk,exitflag,output] =
fminbnd('scoreCV_gwolr',bmin,bmax,options,y,x,east,north,dtype);

Error in ==> gwolr_d at 16
result = gwolr(y,x,xl,xg,east,north,info);

i'm not so sure what i did wrong. if anyone know the wrong that i make, please help me to fix it. thank you.

[per isakson]

Without access to DBD.mat it's difficult to say.

[asrafiah]

i'm so sorry because i'm not so sure how to add the data, i just can write like this:

east north y x1 x2 x3 x4 x5

119.4826 -5.1575 1 3 0.13 91 1 6389

119.4117 -5.1687 1 1 0.28 95 2 28587

119.4136 -5.1815 1 1 0.06 79 2 14002

119.4221 -5.1508 1 1 0.09 69 5 20431

119.4784 -5.1732 1 3 0.10 81 5 5732

119.4213 -5.1631 1 1 0.05 88 5 16428

119.4309 -5.1397 1 1 0.20 73 1 31945

119.4283 -5.1463 3 1 0.18 86 2 44973

119.4341 -5.1429 3 1 0.18 91 2 46357

119.4315 -5.1409 2 1 0.21 97 2 48775

119.4257 -5.1368 2 1 0.23 86 2 46153

119.3597 -5.0778 2 2 0.21 44 10 7554

119.3250 -5.0417 3 2 0.14 33 10 8896

119.4224 -5.1285 1 4 0.21 51 1 27386

119.4364 -5.2101 2 1 0.10 55 5 1577

119.4686 -5.1536 2 3 0.07 81 2 11241

119.4851 -5.0731 2 3 0.06 90 1 1175

119.4174 -5.1678 1 1 0.14 82 5 29238

119.4205 -5.1295 3 4 0.33 95 1 13992

119.4166 -5.1276 2 4 0.19 94 1 18257

Answer 1

[Walter Roberson]

You cannot use that method to pass parameters to a function called by fminbnd(). You aren't supposed to pass a quoted string as the function name either: only passing a function handle is defined behavior.

As the fminbnd() documentation says:

Parameterizing Functions in the MATLAB Mathematics documentation, explains how to pass additional parameters to your objective function fun.

The link is to http://www.mathworks.com/help/techdoc/math/bsgprpq-5.html

[asrafiah]

i'm so sorry because i'm not really understand what you mean. i tried the same function before but i just use x variable, there are not xl and xg variable and i'm not getting error.

please help me with more explanation. thank you.

[Walter Roberson]

Look again at the fminbnd documentation http://www.mathworks.com/help/techdoc/ref/fminbnd.html

Notice that there are only two calling sequences permitted:

x = fminbnd(fun,x1,x2)

x = fminbnd(fun,x1,x2,options)

You are using the options structure, so you are permitted to pass in only the function handle, two parameters, and the options structure. Instead you pass in those and an additional 5 parameters. Those extra 5 parameters are being ignored and are not being passed to your function.

In order to use those extra variables in your target function, you must find some other method getting the variables in to that function. A good way of doing that is to use anonymous functions. The link above shows you how to pass extra variables in by using anonymous functions.

[asrafiah]

oh..i get it now. i use '[x,fval,exitflag,output] = fminbnd(...)'. so i think there is not problem about fminbnd.thank you so much for your help.