 Usage:  {m,b,const} = backfit(t,y,h,loc,kern{,opt})  

 

 Input:



  t                      n x p matrix, the observed continuous 

                         explanatory variable 

                         

  y                      n x 1 matrix, the observed response variable 

                         

  h                      p x 1 vector or scalar, the bandwidth if loc>-1, 

                         else the second parameter of knn. 

                         

  loc                    {-1,0,1,2}, if loc>-1, the degree of the chosen 

                         local polynomial smoother else the knn is chosen. 

                         

  kern                   string, the kernel to be used. 

                         If loc=-1 it has no meaning. 

                         

  opt.x                  n x d matrix, optional, the observed discrete 

                         explanatory variables (linear part) 

                         

  opt.miter              integer, maximal number of iterations. The default 

                         is 50. 

                         

  opt.cnv                integer, convergence criterion. The default is 1.0e-6. 

                         

  opt.shf                integer, (show-how-far) if exists and =1, an output 

                         is produced which indicates how the iteration 

                         is going on (additive function / point of estimation / 

                         number of iteration). 

                         

 Output:



  m                      n x pp matrix, where pp is p*(loc+1). The estimates 

                         of the additive components are given in column 1 to p, 

                         the first derivatives in column (p+1) to (2p) and 

                         the second derivatives in (2p+1) to (3p). 

                         

  b                      d x 1 vector, parameter estimate of the linear part 

                         

  const                  scalar, estimate of the constant 

                         

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

