 Usage:  myfit = eivplmnor(x,t,y,sigma,h{,opt})  

 

 Input:



  x                      n x p matrix, the discrete predictor variables. 

                         

  t                      n x q matrix, the continuous predictor variables. 

                         

  y                      n x 1 vector, the response variables. 

                         

  sigma                  scalar, the variance of the measurement error. 

                         

  h                      q x 1 vector, the bandwidth. 

                         

  opt                    optional, a list with optional input. The macro 

                         "gplmopt" 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.b0                 p x 1 vector, the initial coefficients. If not 

                         given, all coefficients are put =0 originally. 

                         

  opt.wx                 scalar or n x 1 vector, prior weights. If not 

                         given, set to 1. 

                         

  opt.wt                 n x 1 vector, weights for t (trimming factors). 

                         If not given, all set to 1. 

                         

  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.shf                integer, if exists and =1, some output is produced 

                         which indicates how the iteration is going on. 

                         

  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 this option should be given only when data 

                         are sorted. 

                         

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

                         is 10. 

                         

  opt.cnv                integer, convergence criterion. The default is 0.0001. 

                         

  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 in predictor. 

                         

 Output:



  myfit.b                k x 1 vector, estimated coefficients. 

                         

  myfit.bv               k x k matrix, estimated covariance matrix for 

                         coefficients. 

                         

  myfit.m                n x 1 vector, estimated nonparametric part. 

                         

  myfit.mg               ng x 1 vector, estimated nonparametric part on grid 

                         if tg was given. This component will not exist, if 

                         tg was not given. 

                         

  myfit.stat             list with the following statistics: 

                         

  myfit.stat.deviance    deviance, 

                         

  myfit.stat.pearson     generalized pearson's chi^2, 

                         

  myfit.stat.r2          pseude R^2, 

                         

  myfit.stat.dispersion  dispersion parameter estimate, 

                         

  myfit.stat.it          scalar, number of iterations needed. 

                         

--------------------------------------------------------------

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

