source: trunk/yat/regression/LinearWeighted.cc @ 718

// $Id: LinearWeighted.cc 718 2006-12-26 09:56:26Z 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 {

  LinearWeighted::LinearWeighted(void)
    : OneDimensionalWeighted(), alpha_(0), alpha_var_(0), beta_(0),
      beta_var_(0), m_x_(0), s2_(0)
  {
  }

  LinearWeighted::~LinearWeighted(void)
  {
  }

  double LinearWeighted::alpha(void) const
  {
    return alpha_;
  }

  double LinearWeighted::alpha_err(void) const
  {
    return sqrt(alpha_var_);
  }

  double LinearWeighted::beta(void) const
  {
    return beta_;
  }

  double LinearWeighted::beta_err(void) const
  {
    return sqrt(beta_var_);
  }

  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.
    ap_.reset();
    ap_.add_values(x,y,utility::vector(x.size(),1),w);

    // 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();
  }

  double LinearWeighted::m_x(void) const
  {
    return ap_.x_averager().mean();
  }

  double LinearWeighted::m_y(void) const
  {
    return ap_.y_averager().mean();
  }

  double LinearWeighted::mse(void) const
  {
    return mse_;
  }

  double LinearWeighted::prediction_error(const double x, const double w) const
  {
    return sqrt(alpha_var_ + beta_var_*(x-m_x_)*(x-m_x_)+s2(w));
  }

  double LinearWeighted::s2(double w) const
  {
    return s2_/w;
  }

  double LinearWeighted::standard_error(const double x) const
  {
    return sqrt(alpha_var_ + beta_var_*(x-m_x_)*(x-m_x_) );
  }

  double LinearWeighted::sxx(void) const
  {
    return ap_.x_averager().sum_xx_centered();
  }

  double LinearWeighted::sxy(void) const
  {
    return ap_.sum_xy_centered();
  }

  double LinearWeighted::syy(void) const
  {
    return ap_.y_averager().sum_xx_centered();
  }

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