source: branches/peters_vector/lib/statistics/tScore.h @ 469

// $Id: tScore.h 469 2005-12-19 14:58:29Z peter $

#ifndef _theplu_statistics_t_score_
#define _theplu_statistics_t_score_ 

// C++ tools include
/////////////////////
#include <c++_tools/statistics/Score.h>
#include <c++_tools/gslapi/vector.h>

#include <gsl/gsl_cdf.h>


namespace theplu {
namespace statistics {  

  ///
  /// Class for Fisher's t-test.
  ///   
  class tScore : public Score
  {
  
  public:
    ///
    /// Default Constructor.
    ///
    tScore(bool absolute=true);

    ///
    /// Calculates the value of t-score, i.e. the ratio between
    /// difference in mean and standard deviation of this
    /// difference. \f$ \frac{ \vert \frac{1}{n_x}\sum x_i -
    /// \frac{1}{n_y}\sum y_i \vert } {\frac{\sum x_i^2 + \sum
    /// y_i^2}{n_x-1+n_y-1}} \f$ 
    ///
    /// @return t-score if absolute=true
    /// absolute value of t-score is returned
    ///
    double score(const classifier::Target& target, 
                 const classifier::VectorAbstract& value); 

    ///
    /// Weighted version of t-Score @return t-score if absolute=true
    /// absolute value of t-score is returned
    ///
    double score(const classifier::Target& target, 
                 const classifier::VectorAbstract& value, 
                 const classifier::VectorAbstract& weight); 

    ///
    ///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 ...  @return the one-sided p-value( if
    ///absolute=true is used the two-sided p-value)
    ///
    double p_value() const;

         
         
  private:
    double t_;
    int dof_;
        
  };

}} // of namespace statistics and namespace theplu

#endif