source: trunk/yat/regression/LinearWeighted.cc @ 1020

Last change on this file since 1020 was 1020, checked in by Peter, 14 years ago

passing VectorBase? in regression::OneDimesionalWeighted? - refs #256

  • Property svn:eol-style set to native
  • Property svn:keywords set to Id
File size: 3.0 KB
Line 
1// $Id: LinearWeighted.cc 1020 2008-02-01 17:17:26Z peter $
2
3/*
4  Copyright (C) 2005 Peter Johansson
5  Copyright (C) 2006 Jari Häkkinen, Markus Ringnér, Peter Johansson
6  Copyright (C) 2007 Jari Häkkinen, Peter Johansson
7
8  This file is part of the yat library, http://trac.thep.lu.se/yat
9
10  The yat library is free software; you can redistribute it and/or
11  modify it under the terms of the GNU General Public License as
12  published by the Free Software Foundation; either version 2 of the
13  License, or (at your option) any later version.
14
15  The yat library is distributed in the hope that it will be useful,
16  but WITHOUT ANY WARRANTY; without even the implied warranty of
17  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
18  General Public License for more details.
19
20  You should have received a copy of the GNU General Public License
21  along with this program; if not, write to the Free Software
22  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
23  02111-1307, USA.
24*/
25
26#include "LinearWeighted.h"
27#include "yat/statistics/AveragerPairWeighted.h"
28#include "yat/utility/vector.h"
29
30#include <cassert>
31
32namespace theplu {
33namespace yat {
34namespace regression {
35
36  LinearWeighted::LinearWeighted(void)
37    : OneDimensionalWeighted(), alpha_(0), alpha_var_(0), beta_(0),
38      beta_var_(0)
39  {
40  }
41
42  LinearWeighted::~LinearWeighted(void)
43  {
44  }
45
46  double LinearWeighted::alpha(void) const
47  {
48    return alpha_;
49  }
50
51  double LinearWeighted::alpha_var(void) const
52  {
53    return alpha_var_;
54  }
55
56  double LinearWeighted::beta(void) const
57  {
58    return beta_;
59  }
60
61  double LinearWeighted::beta_var(void) const
62  {
63    return beta_var_;
64  }
65
66  void LinearWeighted::fit(const utility::VectorBase& x,
67                           const utility::VectorBase& y,
68                           const utility::VectorBase& w)
69  {
70    assert(x.size()==y.size());
71    assert(x.size()==w.size());
72
73    // AveragerPairWeighted requires 2 weights but works only on the
74    // product wx*wy, so we can send in w and a dummie to get what we
75    // want.
76    ap_.reset();
77    ap_.add_values(x,y,utility::vector(x.size(),1),w);
78
79    alpha_ = m_y();
80    beta_ = sxy()/sxx();
81
82    chisq_=0;
83    for (size_t i=0; i<x.size(); ++i){
84      double res = predict(x(i))-y(i);
85      chisq_ += w(i)*res*res;
86    }
87
88    alpha_var_ = s2()/ap_.y_averager().sum_w();
89    beta_var_ = s2()/sxx(); 
90  }
91
92  double LinearWeighted::m_x(void) const
93  {
94    return ap_.x_averager().mean();
95  }
96
97  double LinearWeighted::m_y(void) const
98  {
99    return ap_.y_averager().mean();
100  }
101
102  double LinearWeighted::predict(const double x) const
103  { 
104    return alpha_ + beta_ * (x-m_x()); 
105  }
106
107 
108  double LinearWeighted::s2(double w) const
109  {
110    return chisq_/(w*(ap_.y_averager().n()-2));
111  }
112
113  double LinearWeighted::standard_error2(const double x) const
114  {
115    return alpha_var_ + beta_var_*(x-m_x())*(x-m_x());
116  }
117
118
119  double LinearWeighted::sxx(void) const
120  {
121    return ap_.x_averager().sum_xx_centered();
122  }
123
124
125  double LinearWeighted::sxy(void) const
126  {
127    return ap_.sum_xy_centered();
128  }
129
130
131  double LinearWeighted::syy(void) const
132  {
133    return ap_.y_averager().sum_xx_centered();
134  }
135
136}}} // of namespaces regression, yat, and theplu
Note: See TracBrowser for help on using the repository browser.