1 | #ifndef _theplu_yat_statistics_ttest_ |
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2 | #define _theplu_yat_statistics_ttest_ |
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3 | |
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4 | // $Id: tTest.h 1437 2008-08-25 17:55:00Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2004, 2005 Peter Johansson |
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8 | Copyright (C) 2006 Jari Häkkinen, Peter Johansson, Markus Ringnér |
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9 | Copyright (C) 2007 Jari Häkkinen, Peter Johansson |
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10 | Copyright (C) 2008 Peter Johansson |
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11 | |
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12 | This file is part of the yat library, http://dev.thep.lu.se/yat |
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13 | |
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14 | The yat library is free software; you can redistribute it and/or |
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15 | modify it under the terms of the GNU General Public License as |
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16 | published by the Free Software Foundation; either version 2 of the |
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17 | License, or (at your option) any later version. |
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18 | |
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19 | The yat library is distributed in the hope that it will be useful, |
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20 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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21 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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22 | General Public License for more details. |
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23 | |
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24 | You should have received a copy of the GNU General Public License |
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25 | along with this program; if not, write to the Free Software |
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26 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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27 | 02111-1307, USA. |
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28 | */ |
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29 | |
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30 | #include "AveragerWeighted.h" |
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31 | |
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32 | #include <gsl/gsl_cdf.h> |
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33 | |
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34 | namespace theplu { |
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35 | namespace yat { |
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36 | namespace statistics { |
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37 | |
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38 | /// |
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39 | /// @brief Class for Student's t-test. |
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40 | /// |
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41 | /// See <a href="http://en.wikipedia.org/wiki/Student's_t-test"> |
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42 | /// http://en.wikipedia.org/wiki/Student's_t-test</a> for more |
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43 | /// details on the t-test. |
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44 | /// |
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45 | class tTest |
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46 | { |
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47 | |
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48 | public: |
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49 | /// |
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50 | /// @brief Default Constructor. |
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51 | /// |
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52 | tTest(void); |
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53 | |
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54 | |
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55 | /** |
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56 | Adding a data value to tTest. |
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57 | */ |
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58 | void add(double value, bool target, double weight=1.0); |
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59 | |
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60 | /** |
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61 | Calculates the t-score, i.e. the ratio between difference in |
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62 | mean and standard deviation of this difference. The t-score is |
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63 | calculated as |
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64 | \f$ t = \frac{ m_x - m_y }{ |
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65 | s\sqrt{\frac{1}{n_x}+\frac{1}{n_y}}} \f$ where \f$ m \f$ is the |
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66 | weighted mean, n is the weighted version of number of data |
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67 | points \f$ \frac{\left(\sum w_i\right)^2}{\sum w_i^2} \f$, and |
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68 | \f$ s^2 \f$ is an estimation of the variance \f$ s^2 = \frac{ |
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69 | \sum_i w_i(x_i-m_x)^2 + \sum_i w_i(y_i-m_y)^2 }{ n_x + n_y - 2 |
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70 | } \f$ |
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71 | |
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72 | \see AveragerWeighted |
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73 | |
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74 | If all weights are equal to unity this boils down to |
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75 | \f$ t = \frac{ m_x - m_y } |
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76 | {s\sqrt{\frac{1}{n_x}+\frac{1}{n_y}}} \f$ where \f$ m \f$ is |
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77 | the mean, \f$ n \f$ is the number of data points and \f$ s^2 = |
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78 | \frac{ \sum_i (x_i-m_x)^2 + \sum_i (y_i-m_y)^2 }{ n_x + n_y - 2 |
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79 | } \f$ |
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80 | |
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81 | \see Averager |
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82 | |
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83 | \return t-score. |
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84 | */ |
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85 | double score(void); |
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86 | |
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87 | /// |
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88 | /// Calculates the p-value, i.e. the probability of observing a |
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89 | /// t-score equally or larger if the null hypothesis is true. If P |
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90 | /// is near zero, this casts doubt on this hypothesis. The null |
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91 | /// hypothesis is that the means of the two distributions are |
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92 | /// equal. Assumtions for this test is that the two distributions |
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93 | /// are normal distributions with equal variance. The latter |
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94 | /// assumtion is dropped in Welch's t-test. |
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95 | /// |
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96 | /// @return the two-sided p-value |
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97 | /// |
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98 | double p_value() const; |
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99 | |
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100 | /// |
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101 | /// @return One-sided P-value |
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102 | /// |
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103 | double p_value_one_sided(void) const; |
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104 | |
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105 | private: |
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106 | |
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107 | double dof_; |
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108 | bool updated_; |
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109 | double t_; |
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110 | AveragerWeighted pos_; |
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111 | AveragerWeighted neg_; |
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112 | |
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113 | }; |
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114 | |
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115 | }}} // of namespace statistics, yat, and theplu |
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116 | |
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117 | #endif |
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