NAME

       gbnlprobit - Non linear probit regression

SYNOPSIS

       gbnlprobit [options] <function definition>

DESCRIPTION

       Non linear probit estimation. Minimize the negative log-likelihood

              sum_{i in N_0} log(1-F(g(X_i))) + sum_{i in N_1} log(F(g(X_i)))

       where  N_0  and  N_1  are the sets of 0 and 1 observations, g is a generic function of the
       independent variables and F is the normal CDF. It is also possible to minimize  the  score
       function

       w_0 sum_{i in N_0} theta(F(g(X_i))-t) +

              w_1 sum_{i in N_1} theta(t-F(g(X_i)))

       where  theta  is the Heaviside function and t a threshold level. Weights w_0 and w_1 scale
       the contribution of the two subpopulations. The first column of data contains 0/1 entries.
       Successive  columns  are  independent  variables.  The  model  is  specified by a function
       g(x1,x2...) where x1,..  stands for the first,second .. N-th column independent variables.

   options:
       -O     type of output (default 0)

       0      parameters

       1      parameters and errors

       2      <variables> and probabilities

       3      parameters and variance matrix

       4      marginal effects

       -V     variance matrix estimation (default 0)

       0      <gradF gradF^t>

       1      < J^{-1} >

       2      < H^{-1} >

       3      < H^{-1} J H^{-1} >

       -z     take zscore (not of 0/1 dummies)

       -F     input fields separators (default " \t")

       -v     verbosity level (default 0)

       0      just results

       1      comment headers

       2      summary statistics

       3      covariance matrix

       4      minimization steps (default 10)

       5      model definition

       -g     set number of point for global optimal threshold identification

       -h     this help

       -t     set threshold value (default 0)

       0      ignore threshold

       (0,1)  user provided threshold

       1      compute optimal only global

       2      compute optimal

       -M     estimation method

       0      maximum likelihood

       1      min. score (w0=w1=1)

       2      min. score (w0=1/N0, w1=1/N1)

       -A     MLL   optimization   options   (default   0.01,0.1,100,1e-6,1e-6,5)   fields    are
              step,tol,iter,eps,msize,algo. Empty fields for default

       step   initial step size of the searching algorithm

       tol    line search tolerance iter: maximum number of iterations

       eps    gradient tolerance : stopping criteria ||gradient||<eps

       algo   optimization     methods:     0     Fletcher-Reeves,     1     Polak-Ribiere,     2
              Broyden-Fletcher-Goldfarb-Shanno, 3 Steepest descent, 4 simplex

       -B     score optimization options (default 0.1,100,1e-6) fields are step,iter,msize. Empty
              fields for default

       step   initial step size of the searching algorithm

       iter   maximum number of iterations

       msize  max size, stopping criteria simplex dim. <max size optimization method is simplex

AUTHOR

       Written by Giulio Bottazzi

REPORTING BUGS

       Report bugs to <gbutils@googlegroups.com>

       Package home page <http://cafim.sssup.it/~giulio/software/gbutils/index.html>

COPYRIGHT

       Copyright  © 2001-2018 Giulio Bottazzi This program is free software; you can redistribute
       it and/or modify it under the terms of the GNU  General  Public  License  (version  2)  as
       published by the Free Software Foundation;

       This  program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
       without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR  PURPOSE.
       See the GNU General Public License for more details.