GBNLPROBIT(1)

💖 GBNLPROBIT 💖

okay bestie, gather round, because today we're doing non linear probit regression and honestly? it's giving main character energy ✨

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🌸 NAME 🌸

gbnlprobit — non linear probit regression. that's it, that's the whole vibe. 🎀

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🦋 SYNOPSIS 🦋

gbnlprobit [options] <function definition>

no cap, that's literally the whole thing. you pass it options and a function definition and it works. 💅

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⭐ DESCRIPTION ⭐

so. we're doing non linear probit estimation, and what that means is we 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. slay. 🔥

and ALSO, plot twist, it's 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. 🫶

okay now the data tea ☕ — 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. iykyk. 💜

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🌈 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

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🎀 AUTHOR 🎀

Written by Giulio Bottazzi 💖

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💀 REPORTING BUGS 💀

found a bug? not me gatekeeping, go tell them fr fr:

Report bugs to <gbutils@googlegroups.com>

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

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👑 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. ⚠️

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and that's the whole page bestie!! this is gbnlprobit version 6.2, from June 2021, and honestly it ate. go forth and probit. periodt. 💅✨🫶