 Usage:  {b,bv,it,ret} = glmcore(code,x,y,wx,off,ctrl)  

 

 Input:



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

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

                         

  x                      n x p matrix, the predictor variables. 

                         

  y                      n x 1 or n x 2 or n x 3 matrix, 

                         either the response values, 

                         or sums of response values in 1st column and sums of 

                         a function of response values in the 2nd column (e.g. 

                         sums of y^2 or log(y)). 

                         (In the case of replicated data, the number of 

                         replications should be given in wx, y[,1] contains 

                         the sums of all responses for a replication, 

                         y[,2] contains sums of y^2 or log(y) for a 

                         replication.) 

                         

  wx                     n x 1 vector or scalar, weights. Set wx=1 to ignore. 

                         

  off                    n x 1 vector or scalar, offset. Set off=0 to ignore. 

                         

  ctrl                   6 x 1 vector or scalar, contains control parameters 

                         shf (default=0), 

                         miter (default=10), 

                         cnv (default=0.0001), 

                         fscor (default=0), 

                         pow (default=0, power for power link), 

                         nbk (default=1, parameter for negative binomial). 

                         Alternatively, one can give here shf only. Set to 0 

                         to use the defaults. 

                         The parameters correspond to the optional parameters 

                         which can be given in glminit. 

                         They are all ignored when not applicable. 

                         

 Output:



  b                      p x 1 vector, estimated coefficients. 

                         

  bv                     p x p matrix, estimated covariance matrix for b. 

                         Not yet corrected for dispersion! 

                         

  it                     integer, number of iterations needed. 

                         

  ret                    scalar, return code: 

                         0 o.k., 

                         1 maximal number of iterations reached 

                         (if applicable), 

                         -1 missing values have been encountered. 

                         

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

