 Usage:  stat = gplmstat(code,x,t,y,b,m{,wx{,off}})  

 

 Input:



  code                   text string, the short code for the model (e.g. 

                         "bilo" for logit or "noid" for ordinary PLM). 

                         

  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. 

                         

  h                      q x 1 vector, the bandwith. 

                         

  b                      p x 1 vector, estimated coefficients. 

                         

  m                      n x 1 vector, estimated nonparametric part 

                         

  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.weights            string, type of observation weights. Can be 

                         "frequency" for replication counts, or "prior" 

                         (default) for prior weights in weighted regression. 

                         

  opt.wx                 scalar or n x 1 vector, frequency or prior 

                         weights. If not given, set to 1. 

                         

  opt.off                scalar or n x 1 vector, offset in linear predictor. 

                         If not given, set to 0. 

                         

  opt.pow                scalar, power for power link. If not given, set 

                         to 0 (logarithm). 

                         

  opt.nbk                scalar, extra parameter k for negative binomial 

                         distribution. If not given, set to 1 (geometric 

                         distribution). 

                         

 Output:



  stat                   list with the following statistics: 

                         

  stat.serror            standard errors of parameter estimates. 

                         

  stat.tvalue            t-values for parameter estimates. 

                         

  stat.pvalue            p-values for significance of parameter estimates. 

                         

  stat.df                degrees of freedom. 

                         

  stat.deviance          deviance. 

                         

  stat.pearson           generalized pearson's chi^2. 

                         

  stat.loglik            log-likelihood. 

                         

  stat.dispersion        dispersion parameter estimate =pearson/df. 

                         

  stat.r2                (pseudo) R^2. 

                         

  stat.adr2              adjusted (pseudo) R^2. 

                         

  stat.aic               AIC criterion. 

                         

  stat.bic               BIC criterion. 

                         

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

