 Usage:  {m,b,bv,const} = gintestpl(code,x,t,y,h,g{,opt})  
 
 Input:

  code                   text string, the short code for the model (e.g. 
                         "bilo" for logit or "noid" for ordinary PLM), 
                         see tutorial. 
                         
  x                      n x d matrix, the discrete predictor variables. 
                         
  t                      n x p matrix, the continuous predictor variables. 
                         
  y                      n x 1 vector, the response variables. 
                         
  h                      p x 1 vector or scalar, chosen bandwidth for 
                         the directions of interest. 
                         
  g                      p x 1 vector or scalar, chosen bandwidth for 
                         the directions not of interest. 
                         
  opt                    optional, a list with optional input. The macro 
                         "gamopt" can be used to set up this parameter. 
                         The order of the list elements is not important. 
                         Parameters which are not given are replaced by 
                         defaults (see below). 
                         
  opt.tg                 ng x 1 vector, a grid for continuous part. If tg is 
                         given, the nonparametric function will also be 
                         computed on this grid. 
                         
  opt.wx                 scalar or n x 1 vector, prior weights. For 
                         binomial models usually the binomial index vector. 
                         If not given, set to 1. 
                         
  opt.b0                 d x 1 vector, the initial coefficients. If not 
                         given, all coefficients are set to GLM pre-estimation. 
                         
  opt.wt                 n x 1 vector, weights for t (trimming factors). 
                         If not given, all set to 1. 
                         
  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). 
                         
  opt.nosort             integer, if exists and =1, the continuous variables 
                         t and the grid tg are assumed to be sorted by the 
                         1st column. Sorting is required by the algorithm, 
                         hence you should switch if off only when the data 
                         are already sorted. 
                         
  opt.miter              integer, maximal number of iterations. The default 
                         is 10. 
                         
  opt.cnv                integer, convergence criterion. The default is 0.0001. 
                         
  opt.fscor              integer, if exists and =1, a Fisher scoring is 
                         performed (instead of the default Newton-Raphson 
                         procedure). This parameter is ignored for 
                         canonical links. 
                         
  opt.wtc                n x 1 vector, weights for convergence criterion, 
                         w.r.t. m(t) only. If not given, opt.wt is used. 
                         
  opt.off                scalar or n x 1 vector, offset. Can be used for 
                         constrained estimation. If not given, set to 0. 
                         
  opt.pow                scalar, power for power link. If not given, 
                         set to 0. 
                         
  opt.nbk                scalar, extra parameter k for negative binomial 
                         distribution. If not given, set to 1 (geometric 
                         distribution). 
                         
 Output:

  m                      n x 1 vector, estimated nonparametric part 
                         
  b                      d x 1 vector, estimated coefficients 
                         
  bv                     d x d matrix, estimated covariance matrix for coeff. 
                         
  const                  ng x 1 vector, estimated nonparametric part on grid 
                         
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(C) MD*TECH Method and Data Technologies, 17.8.2000
