source: trunk/yat/regression/LinearWeighted.cc @ 703

Last change on this file since 703 was 703, checked in by Jari Häkkinen, 15 years ago

Addresses #65 and #170.

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 1.9 KB
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1// $Id: LinearWeighted.cc 703 2006-12-18 00:47:44Z 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  LinearWeighted::LinearWeighted(void)
35    : OneDimensionalWeighted(), alpha_(0), alpha_var_(0), beta_(0),
36      beta_var_(0), m_x_(0), s2_(0)
37  {
38  }
39
40  LinearWeighted::~LinearWeighted(void)
41  {
42  }
43
44  void LinearWeighted::fit(const utility::vector& x,
45                           const utility::vector& y,
46                           const utility::vector& w)
47  {
48    // AveragerPairWeighted requires 2 weights but works only on the
49    // product wx*wy, so we can send in w and a dummie to get what we
50    // want.
51    ap_.reset();
52    ap_.add_values(x,y,utility::vector(x.size(),1),w);
53
54    // estimating the noise level. see attached document for motivation
55    // of the expression.
56    s2_= (syy()-sxy()*sxy()/sxx())/(w.sum()-2*(w*w)/w.sum()) ;
57   
58    alpha_ = m_y();
59    beta_ = sxy()/sxx();
60    alpha_var_ = ap_.y_averager().standard_error() * 
61      ap_.y_averager().standard_error();
62    beta_var_ = s2_/sxx(); 
63    m_x_=m_x();
64  }
65
66}}} // of namespaces regression, yat, and theplu
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