source: trunk/yat/regression/Linear.cc @ 726

Last change on this file since 726 was 726, checked in by Peter, 16 years ago

fixes #165 added test checking Linear Regression is equivalent to Polynomial regression of degree one.

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1// $Id: Linear.cc 726 2007-01-04 14:38:56Z 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 "Linear.h"
25#include "yat/statistics/AveragerPair.h"
26#include "yat/utility/vector.h"
27
28namespace theplu {
29namespace yat {
30namespace regression {
31
32  Linear::Linear(void)
33    : OneDimensional(), alpha_(0), alpha_var_(0), beta_(0), beta_var_(0),
34      chisq_(0)
35  {
36  }
37
38  Linear::~Linear(void)
39  {
40  }
41
42  double Linear::alpha(void) const
43  {
44    return alpha_;
45  }
46
47  double Linear::alpha_var(void) const
48  {
49    return alpha_var_;
50  }
51
52  double Linear::beta(void) const
53  {
54    return beta_;
55  }
56
57  double Linear::beta_var(void) const
58  {
59    return beta_var_;
60  }
61
62  double Linear::chisq(void) const 
63  { 
64    return chisq_; 
65  }
66
67  void Linear::fit(const utility::vector& x, const utility::vector& y) 
68  {
69    ap_.reset();
70    for (size_t i=0; i<x.size(); i++)
71      ap_.add(x(i),y(i));
72
73    alpha_ = ap_.y_averager().mean();
74    beta_ = ap_.covariance() / ap_.x_averager().variance();
75
76    // calculating deviation between data and model
77    chisq_ = (ap_.y_averager().sum_xx_centered() - ap_.sum_xy_centered()*
78              ap_.sum_xy_centered()/ap_.x_averager().sum_xx_centered() );
79    r2_= 1-chisq_/ap_.x_averager().sum_xx_centered();
80    alpha_var_ = s2() / x.size();
81    beta_var_ = s2() / ap_.x_averager().sum_xx_centered();
82  }
83
84  double Linear::predict(const double x) const
85  { 
86    return alpha_ + beta_ * (x - ap_.x_averager().mean()); 
87  }
88
89  double Linear::r2(void) const
90  {
91    return r2_;
92  }
93
94  double Linear::s2(void) const
95  {
96    return chisq()/(ap_.n()-2);
97  }
98
99  double Linear::standard_error(const double x) const
100  {
101    return sqrt( alpha_var_+beta_var_*(x-ap_.x_averager().mean())*
102                 (x-ap_.x_averager().mean()) ); 
103  }
104
105}}} // of namespaces regression, yat, and theplu
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