source: trunk/yat/regression/LinearWeighted.cc @ 682

Last change on this file since 682 was 682, checked in by Jari Häkkinen, 15 years ago

Addresses #153. Moved regression files to .../yat/regression.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.0 KB
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1// $Id: LinearWeighted.cc 682 2006-10-11 22:06:38Z jari $
2
3/*
4  Copyright (C) The authors contributing to this file.
5
6  This file is part of the yat library, http://lev.thep.lu.se/trac/yat
7
8  The yat library is free software; you can redistribute it and/or
9  modify it under the terms of the GNU General Public License as
10  published by the Free Software Foundation; either version 2 of the
11  License, or (at your option) any later version.
12
13  The yat library is distributed in the hope that it will be useful,
14  but WITHOUT ANY WARRANTY; without even the implied warranty of
15  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
16  General Public License for more details.
17
18  You should have received a copy of the GNU General Public License
19  along with this program; if not, write to the Free Software
20  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
21  02111-1307, USA.
22*/
23
24#include "LinearWeighted.h"
25#include "yat/statistics/AveragerPairWeighted.h"
26#include "yat/utility/vector.h"
27
28#include <gsl/gsl_fit.h>
29
30namespace theplu {
31namespace yat {
32namespace regression {
33
34  void LinearWeighted::fit(const utility::vector& x,
35                           const utility::vector& y,
36                           const utility::vector& w)
37  {
38    // AveragerPairWeighted requires 2 weights but works only on the
39    // product wx*wy, so we can send in w and a dummie to get what we
40    // want.
41    utility::vector dummie(w.size(),1);
42    statistics::AveragerPairWeighted ap;
43    ap.add_values(x,y,w,dummie);
44
45    double m_x = ap.x_averager().mean();
46    double m_y = ap.y_averager().mean();
47   
48    double sxy = ap.sum_xy_centered();
49
50    double sxx = ap.x_averager().sum_xx_centered();
51    double syy = ap.y_averager().sum_xx_centered();
52
53    // estimating the noise level. see attached document for motivation
54    // of the expression.
55    s2_= (syy-sxy*sxy/sxx)/(w.sum()-2*(w*w)/w.sum()) ;
56   
57    alpha_ = m_y;
58    beta_ = sxy/sxx;
59    alpha_var_ = ap.y_averager().standard_error() * 
60      ap.y_averager().standard_error();
61    beta_var_ = s2_/sxx; 
62    m_x_=m_x;
63  }
64
65}}} // of namespaces regression, yat, and theplu
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