 Usage:  ynew = spline(x,y,{xnew,lambda,w})  

 

 Input:



  x                      n x 1 vector of the predictor variable. There should be 

                         at least 4 distinct x values. The x values must be ordered 

                         x[1] <= x[2] <= ...<= x[n]. 

                         

  y                      n x 1 vector of the regressor variable. This vector 

                         must be of the same dimensionality as x. 

                         

  xnew                   m x 1 vector of the new ordered points at which the cubic 

                         spline must be computed xnew[1] <= xnew[2] <= ...<= xnew[n]. 

                         The default value is xnew = x. 

                         

  lambda                 a scalar that controls the choice of a smoothing 

                         method. If lambda = 0 the interpolating 

                         cubic spline is computed. In the case of lambda > 0 

                         the ordinary cubic smoothing spline with the smoothing 

                         parameter lambda is computed. 

                         If lambda < 0 then the smoothing parameter is chosen 

                         based on the Generalized Cross Validation method. The 

                         default value is -1. 

                         

  w                      n x 1 vector of the weights. The default value is w[i]=1. 

                         

 Output:



  ynew                   m x 1 vector that contains the smoothed data. 

                         

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(C) MD*TECH Method and Data Technologies, 21.9.2000

