pca.m

wang xiao ping 版遗传算法

function [scores,loads,ssq,res,q,tsq] = pca(data,plots,scl,lv)
%PCA Principal components analysis
%  This function uses the svd to perform pca on a data matrix.
%  It is assumed that samples are rows and variables are columns. 
%  The inputs are the input matrix (data), an optional variable
%  (plots) that controls the graphs produced (see below), an
%  optional vector (scl) for plotting scores against and
%  an optional variable (lv) which specifies the number of
%  principal components to use in the model and which suppresses 
%  the prompt for number of PCs.  The outputs are the scores
%  (scores), loadings (loads), variance info (ssq), residuals (res),
%  calculated q limit (q), and t^2 limit (tsq). The I/O format is 
%  [scores,loads,ssq,res,q,tsq] = pca(data,plots,scl,lvs);
%
%  Set plots = 0 to suppress all plots, plots = 1 for plots with
%  no confidence limits and plots = 2 for plots with limits.
%  Note: with plots = 0 and lv specified, this routine requires
%  no interactive user input. If you would like to scale the data
%  before processing use the functions auto or scale. 

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

if nargin < 2
  plots = 1;
end
if plots > 2
  error('Plot option must be 0, 1 or 2')
elseif plots < 0
  error('Plot option must be 0, 1 or 2')
end
[m,n] = size(data);
if n < m
  cov = (data'*data)/(m-1);
  [u,s,v] = svd(cov);
  temp2 = (1:n)';
  escl = 1:n;
else
  cov = (data*data')/(m-1);
  [u,s,v] = svd(cov);
  v = data'*v;
  for i = 1:m
    v(:,i) = v(:,i)/norm(v(:,i));
  end
  temp2 = (1:m)';
  escl = 1:m;
end
temp = diag(s)*100/(sum(diag(s)));
ssq = [temp2 diag(s) temp cumsum(temp)];
%  This section calculates the standard errors of the
%  eigenvalues and plots them
if plots == 2
  eigmax = ssq(:,2)/(1-(1.96*sqrt(2/m)));
  eigmin = ssq(:,2)/(1+(1.96*sqrt(2/m)));
  clg
  plot(escl,ssq(:,2),escl,eigmax,'--b',escl,eigmin,'--b',escl,ssq(:,2),'og')
  title('Eigenvalue vs. PC Number showing 95% Confidence Limits')
  xlabel('PC Number')
  ylabel('Eigenvalue')
elseif plots == 1
  clg
  plot(escl,ssq(:,2),escl,ssq(:,2),'og')
  title('Eigenvalue vs. PC Number')
  xlabel('PC Number')
  ylabel('Eigenvalue')
end 
%  Print out the amount of variance captured 
disp('   ')
disp('   Percent Variance Captured by PCA Model')
disp('  ')
disp('    PC#       Eigval   %Var      %TotVar')
disp(ssq)
if nargin < 4
  input('How many principal components do you want to keep?  ');
  lv = ans;
else
  sf = sprintf('Now calculating statistics based on %g PC model',lv);
  disp(sf)
end
if lv > n
  error('No. of PCs must be <= no. of variables')
end
if lv > m
  error('No. of PCs must be <= no. of samples')
end
%  Form the PCA Model Based on the Number of PCs Chosen
loads = v(:,1:lv);
scores = data*loads;
%I = eye(n);
%  Calculate the standard error on the PC loadings if needed
if plots == 2
  loaderr = zeros(n,lv);
  if n > m, nn = m; else, nn = n; end
  for i = 1:lv
    dif = (ssq(:,2)-ones(nn,1)*ssq(i,2)).^2;
    dif = sort(dif);
    sig = sum((ones(nn-1,1)*ssq(i,2))./dif(2:nn,1));
    loaderr(:,i) = ((ssq(i,2)/m)*loads(:,i).^2)*sig;
  end
  loadmax = loads+loaderr;
  loadmin = loads-loaderr;
end
%  Calculate the residuals matrix and Q values
resmat = (data - scores*loads')';
res = (sum(resmat.^2))';
%  Create the scale vectors to plot against
if plots >= 1.0
  if nargin < 3
    scl = 1:m;
    scllim = [1 m];
  else
    scllim = [min(scl) max(scl)];
  end
  scl2 = 1:n;
  temp = [1 1];
  for i = 1:lv
    pclim = sqrt(s(i,i))*temp*1.96;
    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('Sample Scores with 95% Limits')
    pause
    if plots == 2  plot(scl2,loads(:,i),scl2,loads(:,i),'og',scl2,loadmax(:,i),'--b',scl2,loadmin(:,i),'--b',[1 n],[0 0])
    elseif plots == 1
      plot(scl2,loads(:,i),scl2,loads(:,i),'og',[1 n],[0 0])
    end
  xlabel('Variable Number')
  str = sprintf('Loadings for PC# %g',i);
  ylabel(str)
  if plots == 2
    str = sprintf('Variable # vs. Loadings for PC# %g Showing Standard Errors',i);
    title(str)
  else
    str = sprintf('Variable Number vs. Loadings for PC# %g',i);
    title(str)
  end
  pause
end
end
%  Calculate Q limit using unused eigenvalues
temp = diag(s);
if n < m
  emod = temp(lv+1:n,:);
else
  emod = temp(lv+1:m,:);
end
th1 = sum(emod);
th2 = sum(emod.^2);
th3 = sum(emod.^3);
h0 = 1 - ((2*th1*th3)/(3*th2^2));
if h0 <= 0.0
h0 = .0001;
disp('  ')
disp('Warning:  Distribution of unused eigenvalues indicates that')
disp('          you should probably retain more PCs in the model.')
end
q = th1*(((1.65*sqrt(2*th2*h0^2)/th1) + 1 + th2*h0*(h0-1)/th1^2)^(1/h0));
disp('  ')
disp('The 95% Q limit is')
disp(q)
if plots >= 1
  lim = [q q];
  plot(scl,res,scl,res,'+g',scllim,lim,'--b')
  str = sprintf('Process Residual Q with 95 Percent Limit Based on %g PC Model',lv);
  title(str)
  xlabel('Sample Number')
  ylabel('Residual')
  pause
end
%  Calculate T^2 limit using ftest routine
if lv > 1
  if m > 300
    tsq = (lv*(m-1)/(m-lv))*ftest(.95,300,lv);
  else
    tsq = (lv*(m-1)/(m-lv))*ftest(.95,m-lv,lv);
  end
  disp('  ')
  disp('The 95% T^2 limit is')
  disp(tsq)
%  Calculate the value of T^2 by normalizing the scores to
%  unit variance and summing them up
  if plots >= 1.0
    temp2 = scores*inv(diag(ssq(1:lv,2).^.5));
    tsqvals = sum((temp2.^2)');
    tlim = [tsq tsq];
    plot(scl,tsqvals,scl,tsqvals,'+g',scllim,tlim,'--b')
    str = sprintf('Value of T^2 with 95 Percent Limit Based on %g PC Model',lv);
    title(str)
    xlabel('Sample Number')
    ylabel('Value of T^2')
  end
else
  disp('T^2 not calculated when number of latent variables = 1')
  tsq = 1.96^2;
end