Documentation of compare

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Function Synopsis

[yh,fit] = compare(z,th,m,nr,za)

Help text

COMPARE Compares the simulated/predicted output with the measured output.
   YH = COMPARE(Z,TH,M)

   Z : The output - input data for which the comparison is made (the
       validation data set.
   TH: The model in the THETA format (see also THETA).

   M : The prediction horizon. Old outputs up to time t-M are used to
       predict the output at time t. All relevant inputs are used.
       M = inf gives a pure simulation of the system.(Default M=inf).
   YH: The resulting simulated/predicted output.
   COMPARE also plots YH together with the measured output in Z, and
       displays the mean square fit between these two signals.
       (blue(cyan)/solid is YH. black(white)/dashed is measured output)

   [YH,FIT] = COMPARE(Z,TH,M,SAMPNR,LEVELADJUST)  
   gives access to some options:
   FIT: The mean square fit.
   SAMPNR: The sample numbers from Z to be plotted and used for the
      computation of FIT. (Default: SAMPNR = all rows of Z)
   LEVELADJUST: 'yes' adjusts the first values of YH and Z(:,1) to
     zero before plot and computation of FIT. 'no' is default.
   
   See also IDSIM and PREDICT.

Cross-Reference Information

Listing of function compare

function [yh,fit] = compare(z,th,m,nr,za)

%   L. Ljung 10-1-89,10-10-93
%   Copyright (c) 1986-98 by The MathWorks, Inc.
%   $Revision: 3.5 $  $Date: 1997/12/02 03:42:50 $

if nargin<2
   disp('Usage: COMPARE(Z,TH)')
   disp('       [YH,FIT] = COMPARE(Z,TH,PRED_HOR,SAMPLES)')
   return
end     
[nrow,nc]=size(z);nu=th(1,3);ny=nc-nu;
if isthss(th) nytest=th(1,4);else nytest=1;end
if ny~=nytest,
disp(str2mat('The number of columns in the data set is not', ...
   'consistent with the model''s number of inputs and outputs!')),error(' '),
end
if nargin<5,za=[];end
if nargin<4,nr=[];end
if nargin<3, m=[];end
if isempty(za),za='n';end
if isempty(nr),nr=1:nrow;end
if isempty(m),m=inf;end
[nrr,nrc]=size(nr);if nrr>nrc,nr=nr';end
if ~isinf(m),if m<1|m~=floor(m)
   error('The prediction horizon M must by a positive integer.')
end,end
nu=th(1,3); 
if nu==0 & m==inf
  error(['For a time series, there must be a finite prediction horizon' ...
         '\n(The argument M must be less than inf)'])
end
if m==inf,yh=idsim(z(:,ny+1:nc),th);else yh=predict(z,th,m);end
[Ncap,ny]=size(yh);
if za(1)=='y' | za(1)=='Y'
yh(nr,:)=yh(nr,:)-ones(length(nr),1)*yh(nr(1),:);
z(nr,1:ny)=z(nr,1:ny)-ones(length(nr),1)*z(nr(1),1:ny);end
fit=[];
for ky=1:ny
color=get(0,'BlackandWhite');
if color(1:2)=='of',
   axcol=get(gca,'color');
   if sum(axcol)<1.5|sum(axcol)==432
     plot(nr,yh(nr,ky),'c',nr,z(nr,ky),'w'),
     xlabel(['Cyan: Model output, White: Measured output']),
   else
     plot(nr,yh(nr,ky),'b',nr,z(nr,ky),'k'),
     xlabel(['Blue: Model output, Black: Measured output']),
   end
else 
   plot(nr',yh(nr,ky),'-',nr',z(nr,ky),'--'),
   xlabel('Solid: Model output,  Dashed: Measured output')
end
fittemp = norm(yh(nr,ky)-z(nr,ky))/sqrt(length(nr));    
title(['Output # ',int2str(ky),' Fit: ', num2str(fittemp)])
fit=[fit,fittemp];
if ky<ny,pause,end
end