 Usage:  myfit = glmest(code,x,y{,opt})  
 
 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 predictor variables. 
                         
  y                      n x 1 vector, the response variables. 
                         
  opt                    optional, a list with optional input. The macro 
                         "glmopt" 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 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. Can be used for 
                         constrained estimation. If not given, set to 0. 
                         
  opt.shf                integer, if exists and =1, some output is produced 
                         which indicates how the iteration is going on. 
                         
  opt.norepl             integer, if exists and =1, the data are assumed to 
                         have no replications in x. Otherwise, the data 
                         are searched for replications to fasten the 
                         algorithm. 
                         
  opt.miter              integer, maximal number of iterations. The default 
                         is 10. 
                         
  opt.cnv                scalar, 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.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:

  myfit.b                p x 1 vector, estimated coefficients. 
                         
  myfit.bv               p x p matrix, estimated covariance matrix 
                         for coefficients. 
                         
  myfit.stat             list with components as computed by glmstat: 
                         serror (standard errors of coefficients), 
                         tvalue (t-values for coefficients), 
                         pvalue (p-values for coefficients), 
                         df (degrees of freedom), 
                         deviance (deviance), 
                         pearson (generalized pearson's chi^2), 
                         loglik (log-likelihood), 
                         dispersion (estimated dispersion =pearson/df), 
                         r2 ((pseudo) coefficient of determination), 
                         adr2 (adjusted (pseudo) coefficient of determination), 
                         aic (Akaike's AIC criterion), 
                         bic (Schwarz' BIC criterion), and 
                         it (number of iterations needed), 
                         ret (return code, 
                         0 if everything went o.k., 
                         1 if maximal number of iterations reached, 
                         -1 if missing values have been encountered), 
                         nr (number of replications found in x). 
                         
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(C) MD*TECH Method and Data Technologies, 17.8.2000
