Mercurial > hg > octave-lyh
view scripts/statistics/base/ols.m @ 10687:a8ce6bdecce5
Improve documentation strings.
author | Rik <octave@nomad.inbox5.com> |
---|---|
date | Tue, 08 Jun 2010 20:22:38 -0700 |
parents | f0c3d3fc4903 |
children | e151e23f73bc |
line wrap: on
line source
## Copyright (C) 1996, 1997, 1998, 1999, 2000, 2005, 2006, 2007, 2009 ## John W. Eaton ## ## This file is part of Octave. ## ## Octave is free software; you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation; either version 3 of the License, or (at ## your option) any later version. ## ## Octave 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. ## ## You should have received a copy of the GNU General Public License ## along with Octave; see the file COPYING. If not, see ## <http://www.gnu.org/licenses/>. ## -*- texinfo -*- ## @deftypefn {Function File} {[@var{beta}, @var{sigma}, @var{r}] =} ols (@var{y}, @var{x}) ## Ordinary least squares estimation for the multivariate model ## @tex ## $y = x b + e$ ## with ## $\bar{e} = 0$, and cov(vec($e$)) = kron ($s, I$) ## @end tex ## @ifnottex ## @w{@math{y = x*b + e}} with ## @math{mean (e) = 0} and @math{cov (vec (e)) = kron (s, I)}. ## @end ifnottex ## where ## @tex ## $y$ is a $t \times p$ matrix, $x$ is a $t \times k$ matrix, ## $b$ is a $k \times p$ matrix, and $e$ is a $t \times p$ matrix. ## @end tex ## @ifnottex ## @math{y} is a @math{t} by @math{p} matrix, @math{x} is a @math{t} by ## @math{k} matrix, @math{b} is a @math{k} by @math{p} matrix, and ## @math{e} is a @math{t} by @math{p} matrix. ## @end ifnottex ## ## Each row of @var{y} and @var{x} is an observation and each column a ## variable. ## ## The return values @var{beta}, @var{sigma}, and @var{r} are defined as ## follows. ## ## @table @var ## @item beta ## The OLS estimator for @var{b}, @code{@var{beta} = pinv (@var{x}) * ## @var{y}}, where @code{pinv (@var{x})} denotes the pseudoinverse of ## @var{x}. ## ## @item sigma ## The OLS estimator for the matrix @var{s}, ## ## @example ## @group ## @var{sigma} = (@var{y}-@var{x}*@var{beta})' ## * (@var{y}-@var{x}*@var{beta}) ## / (@var{t}-rank(@var{x})) ## @end group ## @end example ## ## @item r ## The matrix of OLS residuals, @code{@var{r} = @var{y} - @var{x}*@var{beta}}. ## @end table ## @end deftypefn ## Author: Teresa Twaroch <twaroch@ci.tuwien.ac.at> ## Created: May 1993 ## Adapted-By: jwe function [BETA, SIGMA, R] = ols (Y, X) if (nargin != 2) print_usage (); endif [nr, nc] = size (X); [ry, cy] = size (Y); if (nr != ry) error ("ols: incorrect matrix dimensions"); endif Z = X' * X; r = rank (Z); if (r == nc) BETA = inv (Z) * X' * Y; else BETA = pinv (X) * Y; endif R = Y - X * BETA; SIGMA = R' * R / (nr - r); endfunction