source: trunk/yat/regression/LinearWeighted.cc @ 682

// $Id: LinearWeighted.cc 682 2006-10-11 22:06:38Z jari $

/*
  Copyright (C) The authors contributing to this file.

  This file is part of the yat library, http://lev.thep.lu.se/trac/yat

  The yat library is free software; you can redistribute it and/or
  modify it under the terms of the GNU General Public License as
  published by the Free Software Foundation; either version 2 of the
  License, or (at your option) any later version.

  The yat library is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
  General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program; if not, write to the Free Software
  Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
  02111-1307, USA.
*/

#include "LinearWeighted.h"
#include "yat/statistics/AveragerPairWeighted.h"
#include "yat/utility/vector.h"

#include <gsl/gsl_fit.h>

namespace theplu {
namespace yat {
namespace regression {

  void LinearWeighted::fit(const utility::vector& x,
                           const utility::vector& y,
                           const utility::vector& w)
  {
    // AveragerPairWeighted requires 2 weights but works only on the
    // product wx*wy, so we can send in w and a dummie to get what we
    // want.
    utility::vector dummie(w.size(),1);
    statistics::AveragerPairWeighted ap;
    ap.add_values(x,y,w,dummie);

    double m_x = ap.x_averager().mean();
    double m_y = ap.y_averager().mean();
    
    double sxy = ap.sum_xy_centered();

    double sxx = ap.x_averager().sum_xx_centered();
    double syy = ap.y_averager().sum_xx_centered();

    // estimating the noise level. see attached document for motivation
    // of the expression.
    s2_= (syy-sxy*sxy/sxx)/(w.sum()-2*(w*w)/w.sum()) ;
    
    alpha_ = m_y;
    beta_ = sxy/sxx;
    alpha_var_ = ap.y_averager().standard_error() * 
      ap.y_averager().standard_error();
    beta_var_ = s2_/sxx;  
    m_x_=m_x;
  }

}}} // of namespaces regression, yat, and theplu