source: trunk/yat/regression/LinearWeighted.cc @ 730

Last change on this file since 730 was 730, checked in by Peter, 15 years ago

fixes #167 and #160

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 2.8 KB
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1// $Id: LinearWeighted.cc 730 2007-01-06 11:02:21Z peter $
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 <cassert>
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)
37  {
38  }
39
40  LinearWeighted::~LinearWeighted(void)
41  {
42  }
43
44  double LinearWeighted::alpha(void) const
45  {
46    return alpha_;
47  }
48
49  double LinearWeighted::alpha_var(void) const
50  {
51    return alpha_var_;
52  }
53
54  double LinearWeighted::beta(void) const
55  {
56    return beta_;
57  }
58
59  double LinearWeighted::beta_var(void) const
60  {
61    return beta_var_;
62  }
63
64  void LinearWeighted::fit(const utility::vector& x,
65                           const utility::vector& y,
66                           const utility::vector& w)
67  {
68    assert(x.size()==y.size());
69    assert(x.size()==w.size());
70
71    // AveragerPairWeighted requires 2 weights but works only on the
72    // product wx*wy, so we can send in w and a dummie to get what we
73    // want.
74    ap_.reset();
75    ap_.add_values(x,y,utility::vector(x.size(),1),w);
76
77    alpha_ = m_y();
78    beta_ = sxy()/sxx();
79
80    chisq_=0;
81    for (size_t i=0; i<x.size(); ++i){
82      double res = predict(x(i))-y(i);
83      chisq_ += w(i)*res*res;
84    }
85
86    alpha_var_ = s2()/ap_.y_averager().sum_w();
87    beta_var_ = s2()/sxx(); 
88  }
89
90  double LinearWeighted::m_x(void) const
91  {
92    return ap_.x_averager().mean();
93  }
94
95  double LinearWeighted::m_y(void) const
96  {
97    return ap_.y_averager().mean();
98  }
99
100  double LinearWeighted::predict(const double x) const
101  { 
102    return alpha_ + beta_ * (x-m_x()); 
103  }
104
105 
106  double LinearWeighted::s2(double w) const
107  {
108    return chisq_/(w*(ap_.y_averager().n()-2));
109  }
110
111  double LinearWeighted::standard_error2(const double x) const
112  {
113    return alpha_var_ + beta_var_*(x-m_x())*(x-m_x());
114  }
115
116
117  double LinearWeighted::sxx(void) const
118  {
119    return ap_.x_averager().sum_xx_centered();
120  }
121
122
123  double LinearWeighted::sxy(void) const
124  {
125    return ap_.sum_xy_centered();
126  }
127
128
129  double LinearWeighted::syy(void) const
130  {
131    return ap_.y_averager().sum_xx_centered();
132  }
133
134}}} // of namespaces regression, yat, and theplu
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