Linear least squares with singular value deconposition - single experiment

  
  fs    = 10;
  nsecs = 10;

  % Elements of the fit basis
  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;

  % random additive noise
  n  = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));

  % coefficients of the linear combination
  c1 = ao(1,plist('yunits','m/T'));
  c1.setName;

  c2 = ao(2,plist('yunits','m/T'));
  c2.setName;

  c3 = ao(3,plist('yunits','m T^-1'));
  c3.setName;

  % build output of linear system
  y = c1*B1 + c2*B2 + c3*B3 + n;
  y.simplifyYunits;

  
  % Get a fit with linlsqsvd
  pobj1 = linlsqsvd(B1, B2, B3, y)

  ---- pest 1 ----
         name: a1*B1+a2*B2+a3*B3
  param names: {'a1', 'a2', 'a3'}
            y: [0.81162366736073077;1.8907151217948008;3.0098623857384701]
           dy: [0.091943725803872112;0.089863977231447567;0.097910574305897308]
       yunits: [m T^(-1)][m T^(-1)][m T^(-1)]
          pdf: []
          cov: [3x3], ([0.00845364871469762 0.000268768332741779 0.000180072770333592;0.000268768332741779 0.00807553440385413 0.00125972375325089;0.000180072770333592 0.00125972375325089 0.00958648056091064])
         corr: [3x3], ([1 0.0325289738130578 0.020003055941376;0.0325289738130578 1 0.143172656986983;0.020003055941376 0.143172656986983 1])
        chain: []
         chi2: 0.87276552675043451
          dof: 97
       models: 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, B3/tsdata Ndata=[100x1], fs=10, nsecs=10, t0=1970-01-01 00:00:00.000 UTC
  description: 
         UUID: b8628843-a1e8-4815-b69b-90efdadc16c2
  ----------------

  % do linear combination: using eval
  yfit = pobj1.eval(B1, B2, B3);

  % Plot - compare data with fit result
  iplot(y, yfit)