1 | #ifndef _theplu_yat_statistics_sam_score_ |
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2 | #define _theplu_yat_statistics_sam_score_ |
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3 | |
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4 | // $Id: SAMScore.h 1486 2008-09-09 21:17:19Z jari $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2006, 2007 Jari Häkkinen, Peter Johansson |
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8 | Copyright (C) 2008 Peter Johansson |
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9 | |
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10 | This file is part of the yat library, http://dev.thep.lu.se/yat |
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11 | |
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12 | The yat library is free software; you can redistribute it and/or |
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13 | modify it under the terms of the GNU General Public License as |
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14 | published by the Free Software Foundation; either version 3 of the |
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15 | License, or (at your option) any later version. |
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16 | |
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17 | The yat library is distributed in the hope that it will be useful, |
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18 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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19 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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20 | General Public License for more details. |
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21 | |
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22 | You should have received a copy of the GNU General Public License |
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23 | along with this program; if not, write to the Free Software |
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24 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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25 | 02111-1307, USA. |
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26 | */ |
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27 | |
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28 | #include "Score.h" |
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29 | |
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30 | #include <cmath> |
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31 | |
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32 | namespace theplu { |
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33 | namespace yat { |
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34 | namespace utility { |
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35 | class VectorBase; |
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36 | } |
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37 | namespace classifier { |
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38 | class DataLookWeighted1D; |
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39 | } |
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40 | namespace statistics { |
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41 | |
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42 | /** |
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43 | @brief Class for score used in Significance Analysis of |
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44 | Microarrays (SAM). |
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45 | |
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46 | The score is similar to the Student t-test but with an added |
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47 | fudge factor in denominator to avoid groups with small variance |
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48 | getting a large score. \f$ \frac{m_x-m_y}{s+s_0} \f$ |
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49 | |
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50 | see http://www.pnas.org/cgi/content/abstract/98/9/5116 for |
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51 | details |
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52 | */ |
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53 | class SAMScore : public Score |
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54 | { |
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55 | |
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56 | public: |
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57 | /// |
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58 | /// @param s0 \f$ s_0 \f$ is a fudge factor |
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59 | /// @param absolute if true max(score, -score) is used |
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60 | /// |
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61 | SAMScore(const double s0, bool absolute=true); |
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62 | |
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63 | /** |
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64 | \f$ \frac{m_x-m_y}{s+s_0} \f$ where \f$ m = \frac{1}{n_x}\sum |
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65 | x_i \f$, \f$ s^2 = \left(\frac{1}{n_x}+\frac{1}{n_y} \right) |
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66 | \frac{\sum (x_i-m_x)^2 + \sum(y_i-m_y)^2}{n_x+n_y-2} \f$, and |
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67 | \f$ s_0 \f$ is the fudge factor. |
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68 | |
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69 | @return SAM score. If absolute=true absolute value of t-score |
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70 | is returned |
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71 | */ |
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72 | double score(const classifier::Target& target, |
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73 | const utility::VectorBase& value) const; |
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74 | |
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75 | /** |
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76 | \f$ \frac{m_x-m_y}{s+s_0} \f$ where \f$ m = \frac{\sum |
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77 | w_ix_i}{w_i} \f$, \f$ s_0 \f$ is the fudge factor, and \f$ s^2 |
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78 | = \left(\frac{1}{n_x}+\frac{1}{n_y} \right) \frac{\sum |
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79 | w_i(x_i-m_x)^2 + \sum w_i(y_i-m_y)^2}{n_x+n_y-2} \f$ where \f$ |
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80 | n \f$ is weighted version of number of data points \f$ |
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81 | \frac{\left(\sum w_i\right)^2}{\sum w_i^2} \f$. |
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82 | |
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83 | @return weighted version of SAM score. If absolute=true |
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84 | absolute value is returned |
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85 | */ |
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86 | double score(const classifier::Target& target, |
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87 | const classifier::DataLookupWeighted1D& value) const; |
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88 | |
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89 | /** |
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90 | \f$ \frac{m_x-m_y}{s+s_0} \f$ where \f$ m = \frac{\sum |
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91 | w_ix_i}{w_i} \f$, \f$ s_0 \f$ is the fudge factor, and \f$ s^2 |
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92 | = \left(\frac{1}{n_x}+\frac{1}{n_y} \right) \frac{\sum |
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93 | w_i(x_i-m_x)^2 + \sum w_i(y_i-m_y)^2}{n_x+n_y-2} \f$ where \f$ |
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94 | n \f$ is weighted version of number of data points \f$ |
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95 | \frac{\left(\sum w_i\right)^2}{\sum w_i^2} \f$. |
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96 | |
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97 | @return weighted version of SAM score. If absolute=true |
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98 | absolute value is returned |
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99 | */ |
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100 | double score(const classifier::Target& target, |
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101 | const utility::VectorBase& value, |
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102 | const utility::VectorBase& weight) const; |
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103 | private: |
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104 | double s0_; |
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105 | |
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106 | template<class T> |
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107 | double score(const T& positive, const T& negative) const |
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108 | { |
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109 | if(positive.n()+negative.n()<=2) |
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110 | return 0; |
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111 | double diff = positive.mean() - negative.mean(); |
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112 | double s2 = ( (1.0/positive.n()+1.0/negative.n()) * |
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113 | (positive.sum_xx_centered()+negative.sum_xx_centered()) / |
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114 | (positive.n()+negative.n()-2) ); |
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115 | if (diff<0 && absolute_) |
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116 | return -diff/(sqrt(s2)+s0_); |
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117 | return diff/(sqrt(s2)+s0_); |
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118 | } |
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119 | }; |
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120 | |
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121 | }}} // of namespace statistics, yat, and theplu |
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122 | |
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123 | #endif |
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