source: trunk/yat/regression/Linear.cc @ 1437

// $Id: Linear.cc 1437 2008-08-25 17:55:00Z peter $

/*
  Copyright (C) 2004, 2005, 2006, 2007 Jari Häkkinen, Peter Johansson
  Copyright (C) 2008 Peter Johansson

  This file is part of the yat library, http://dev.thep.lu.se/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 "Linear.h"
#include "yat/statistics/AveragerPair.h"
#include "yat/utility/VectorBase.h"

namespace theplu {
namespace yat {
namespace regression {

  Linear::Linear(void)
    : OneDimensional(), alpha_(0), alpha_var_(0), beta_(0), beta_var_(0)
  {
  }

  Linear::~Linear(void)
  {
  }

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

  double Linear::alpha_var(void) const
  {
    return alpha_var_;
  }

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

  double Linear::beta_var(void) const
  {
    return beta_var_;
  }

  void Linear::fit(const utility::VectorBase& x, const utility::VectorBase& y) 
  {
    ap_.reset();
    for (size_t i=0; i<x.size(); i++)
      ap_.add(x(i),y(i));

    alpha_ = ap_.y_averager().mean();
    beta_ = ap_.sum_xy_centered() / ap_.x_averager().sum_xx_centered();

    // calculating deviation between data and model
    chisq_ = (ap_.y_averager().sum_xx_centered() - ap_.sum_xy_centered()*
              ap_.sum_xy_centered()/ap_.x_averager().sum_xx_centered() );
    alpha_var_ = s2() / x.size();
    beta_var_ = s2() / ap_.x_averager().sum_xx_centered();
  }

  double Linear::predict(const double x) const
  { 
    return alpha_ + beta_ * (x - ap_.x_averager().mean()); 
  }

  double Linear::s2(void) const
  {
    return chisq()/(ap_.n()-2);
  }

  double Linear::standard_error2(const double x) const
  {
    return alpha_var_+beta_var_*(x-ap_.x_averager().mean())*
      (x-ap_.x_averager().mean()); 
  }

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