source: trunk/yat/statistics/tTest.h @ 1451

#ifndef _theplu_yat_statistics_ttest_
#define _theplu_yat_statistics_ttest_ 

// $Id: tTest.h 1451 2008-08-28 20:46:10Z peter $

/*
  Copyright (C) 2004, 2005 Peter Johansson
  Copyright (C) 2006 Jari Häkkinen, Peter Johansson, Markus Ringnér
  Copyright (C) 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 "AveragerWeighted.h"

#include <gsl/gsl_cdf.h>

namespace theplu {
namespace yat {
namespace statistics {  

  ///
  /// @brief Class for Student's t-test.
  ///   
  /// See <a href="http://en.wikipedia.org/wiki/Student's_t-test">
  /// http://en.wikipedia.org/wiki/Student's_t-test</a> for more
  /// details on the t-test.
  /// 
  class tTest
  {
  
  public:
    ///
    /// @brief Default Constructor.
    ///
    tTest(void);

    
    /**
       Adding a data value to tTest.
    */
    void add(double value, bool target, double weight=1.0);

    /**
       \brief Set everything to zero

       \since New in yat 0.5
     */
    void reset(void);

    /**
       Calculates the t-score, i.e. the ratio between difference in
       mean and standard deviation of this difference. The t-score is
       calculated as 
       \f$ t = \frac{ m_x - m_y }{
       s\sqrt{\frac{1}{n_x}+\frac{1}{n_y}}} \f$ where \f$ m \f$ is the
       weighted mean, n is the weighted version of number of data
       points \f$ \frac{\left(\sum w_i\right)^2}{\sum w_i^2} \f$, and
       \f$ s^2 \f$ is an estimation of the variance \f$ s^2 = \frac{
       \sum_i w_i(x_i-m_x)^2 + \sum_i w_i(y_i-m_y)^2 }{ n_x + n_y - 2
       } \f$

       \see AveragerWeighted

       If all weights are equal to unity this boils down to 
       \f$ t = \frac{ m_x - m_y }
       {s\sqrt{\frac{1}{n_x}+\frac{1}{n_y}}} \f$ where \f$ m \f$ is
       the mean, \f$ n \f$ is the number of data points and \f$ s^2 =
       \frac{ \sum_i (x_i-m_x)^2 + \sum_i (y_i-m_y)^2 }{ n_x + n_y - 2
       } \f$

       \see Averager
       
       \return t-score.
    */
    double score(void); 

    ///
    /// Calculates the p-value, i.e. the probability of observing a
    /// t-score equally or larger if the null hypothesis is true. If P
    /// is near zero, this casts doubt on this hypothesis. The null
    /// hypothesis is that the means of the two distributions are
    /// equal. Assumtions for this test is that the two distributions
    /// are normal distributions with equal variance. The latter
    /// assumtion is dropped in Welch's t-test. 
    ///
    /// @return the two-sided p-value
    ///
    double p_value() const;

    ///
    /// @return One-sided P-value
    ///
    double p_value_one_sided(void) const;

  private:

    double dof_;
    bool updated_;
    double t_;
    AveragerWeighted pos_;
    AveragerWeighted neg_;

  };

}}} // of namespace statistics, yat, and theplu

#endif