Linear least squares with singular value deconposition - multiple experiments

  fs    = 10;
  nsecs = 10;

  % fit basis for 2 experiments case
  B1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
  B1.setName;
  B2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
  B2.setName;
  B3 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
  B3.setName;
  B4 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
  B4.setName;

  C1 = matrix(B1,B2,plist('shape',[2,1]));
  C1.setName;
  C2 = matrix(B3,B4,plist('shape',[2,1]));
  C2.setName;

  % make additive noise
  n1  = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
  n1.setName;
  n2  = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
  n2.setName;

  % coefficients of the linear combination
  a1 = ao(1,plist('yunits','m/T'));
  a1.setName;
  a2 = ao(2,plist('yunits','m/T'));
  a2.setName;

  % assign output values
  % y is a matrix containing the outputs of two experiments:
  y1 = a1*B1 + a2*B3 + n1;
  y2 = a1*B2 + a2*B4 + n2;
  y = matrix(y1,y2,plist('shape',[2,1]));

  % Get a fit with linlsqsvd
  pobj = linlsqsvd(C1, C2, y)

  ---- pest 1 ----
         name: a1*C1+a2*C2
  param names: {'a1', 'a2'}
            y: [0.97312642877028477;2.0892132651873916]
           dy: [0.06611444020240001;0.065007088662104057]
       yunits: [T^(-1) m][T^(-1) m]
          pdf: []
          cov: [2x2], ([0.00437111920327673 -0.000390118937121542;-0.000390118937121542 0.00422592157632266])
         corr: []
        chain: []
         chi2: 0.85210029717685576
          dof: 198
       models: matrix(B1/tsdata Ndata=[100x1], fs=10, nsecs=10, t0=1970-01-01 00:00:00.000 UTC, B2/tsdata Ndata=[100x1], fs=10, nsecs=10, t0=1970-01-01 00:00:00.000 UTC), matrix(B3/tsdata Ndata=[100x1], fs=10, nsecs=10, t0=1970-01-01 00:00:00.000 UTC, B4/tsdata Ndata=[100x1], fs=10, nsecs=10, t0=1970-01-01 00:00:00.000 UTC)
  description: 
         UUID: 545c9699-e749-40d5-bbe1-1322599c9c5d
  ----------------

  % do linear combination: using eval
  yfit = pobj.eval;

  % extract objects
  yfit1 = getObjectAtIndex(yfit,1);
  yfit2 = getObjectAtIndex(yfit,2);

  % Plot - compare data with fit
  iplot(y1, yfit1)
  iplot(y2, yfit2)