---
name: "gbnlprobit"
section: "1"
platform: "linux"
package: "gbutils"
version: "6.3-1+b1"
release: "Debian 13 (Trixie)"
platforms: ["linux"]
url: "https://new.carta.tech/gbnlprobit"
---

# gbnlprobit(1)

Non linear probit regression

## 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.