pcapro.m

偏最小二乘算法在MATLAB中的实现

function [scores,res,tsqvals] = pcapro(newdata,loads,ssq,q,tsq,plots,mo)
%PCAPRO Projects new data on old principal components model
%  The input variables are the new data (newdata), the old 
%  loadings (loads), the old variance info (ssq), the limit
%  for q (q), the limit for t^2 (tsq) and an optional variable 
%  (plots) which suppresses the plots when set to 0.  The outputs
%  are the new scores (scores), residuals (res) and t^2 values
%  (tsqvals).  All of these are plotted as the function proceeds 
%  if plots ~= 0.  The I/O format is:
%  [scores,resids,tsqs] = pcapro(newdata,loads,ssq,q,tsq,plots);
%
%  Warning:  Be sure to use the same scaling as original data!

%  Copyright
%  Barry M. Wise
%  1991, 1992
%  Modified by B.M. Wise, November 1993

[m,n] = size(loads);
[ms,ns] = size(newdata);
[mss,nss] = size(ssq);
if nargin < 6
  plots = 1;
end
if ns ~= m
  error('Number of variables in new data not consistent with loadings')
end
scores = newdata*loads;
scl = 1:ms;
scllim = [1 ms];
temp = [1 1];
if plots ~= 0
  for i = 1:n
    if nargin == 7
      pclim = temp*sqrt(ssq(i,2))*ttestp(.025,mo-i,2);
    else
      pclim = temp*sqrt(ssq(i,2))*1.96;
    end
    plot(scl,scores(:,i),scllim,pclim,'--b',scllim,-pclim,'--b')
    hold on, plot(scl,scores(:,i),'+g'), hold off
    xlabel('Sample Number')
	   str = sprintf('Score on PC# %g',i);
    ylabel(str)
    title('New Sample Scores with 95% Limits from Old Model')
    pause
  end
end
if ms > m
  I = eye(m);
  resmat = ((I - loads*loads')*newdata');
else
  resmat = newdata' - loads*scores';
end
res = (sum(resmat.^2))'; 
lim = [q q];
if plots ~= 0
  plot(scl,res,scl,res,'+g',scllim,lim,'--b')
  title('New Sample Residuals with Limits from Old Model')
  xlabel('Sample Number')
  ylabel('Residual')
  pause
end
tsqvals = sum((scores.^2*inv(diag(ssq(1:n,2))))');
if plots ~= 0
  if n == 1
    disp('T^2 not displayed when number of PCs = 1')
  else
    tlim = [tsq tsq];
    plot(scl,tsqvals,scl,tsqvals,'+g',scllim,tlim,'--b')
    title('New Samples Value of T^2 with 95% Limit From Old Model')
    xlabel('Sample Number')
    ylabel('Value of T^2')
  end
end