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 | |
---|

30 | namespace theplu { |
---|

31 | namespace yat { |
---|

32 | namespace 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 |
---|