man gbnlprobit (1): 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 <[email protected]>

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

COPYRIGHT

Copyright © 2001-2015 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;