source: trunk/c++_tools/statistics/LinearWeighted.cc @ 675

Last change on this file since 675 was 675, checked in by Jari Häkkinen, 15 years ago

References #83. Changing project name to yat. Compilation will fail in this revision.

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1// $Id: LinearWeighted.cc 675 2006-10-10 12:08:45Z 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 "yat/statistics/LinearWeighted.h"
25
26#include "yat/statistics/AveragerPairWeighted.h"
27#include "yat/utility/vector.h"
28
29#include <gsl/gsl_fit.h>
30
31
32namespace theplu {
33namespace statistics {
34namespace regression {
35
36
37  void LinearWeighted::fit(const utility::vector& x,
38                           const utility::vector& y,
39                           const utility::vector& w)
40  {
41    // AveragerPairWeighted requires 2 weights but works only on the
42    // product wx*wy, so we can send in w and a dummie to get what we
43    // want.
44    utility::vector dummie(w.size(),1);
45    AveragerPairWeighted ap;
46    ap.add_values(x,y,w,dummie);
47
48    double m_x = ap.x_averager().mean();
49    double m_y = ap.y_averager().mean();
50   
51    double sxy = ap.sum_xy_centered();
52
53    double sxx = ap.x_averager().sum_xx_centered();
54    double syy = ap.y_averager().sum_xx_centered();
55
56    // estimating the noise level. see attached document for motivation
57    // of the expression.
58    s2_= (syy-sxy*sxy/sxx)/(w.sum()-2*(w*w)/w.sum()) ;
59   
60    alpha_ = m_y;
61    beta_ = sxy/sxx;
62    alpha_var_ = ap.y_averager().standard_error() * 
63      ap.y_averager().standard_error();
64    beta_var_ = s2_/sxx; 
65    m_x_=m_x;
66  }
67
68
69}}} // of namespaces regression, statisitcs and thep
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