1 | #ifndef _theplu_yat_statistics_fisher_ |
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2 | #define _theplu_yat_statistics_fisher_ |
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3 | |
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4 | // $Id: Fisher.h 777 2007-03-04 12:34:17Z peter $ |
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5 | |
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6 | /* |
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7 | Copyright (C) The authors contributing to this file. |
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8 | |
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9 | This file is part of the yat library, http://lev.thep.lu.se/trac/yat |
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10 | |
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11 | The yat library is free software; you can redistribute it and/or |
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12 | modify it under the terms of the GNU General Public License as |
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13 | published by the Free Software Foundation; either version 2 of the |
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14 | License, or (at your option) any later version. |
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15 | |
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16 | The yat library is distributed in the hope that it will be useful, |
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17 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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18 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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19 | General Public License for more details. |
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20 | |
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21 | You should have received a copy of the GNU General Public License |
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22 | along with this program; if not, write to the Free Software |
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23 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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24 | 02111-1307, USA. |
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25 | */ |
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26 | |
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27 | #include "Score.h" |
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28 | |
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29 | #include <sys/types.h> |
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30 | |
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31 | #include <cmath> |
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32 | |
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33 | namespace theplu { |
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34 | namespace yat { |
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35 | namespace utility { |
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36 | class vector; |
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37 | } |
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38 | namespace statistics { |
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39 | /** |
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40 | @brief Fisher's exact test. |
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41 | |
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42 | Fisher's Exact test is a procedure that you can use for data |
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43 | in a two by two contingency table: \f[ \begin{tabular}{|c|c|} |
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44 | \hline a&b \tabularnewline \hline c&d \tabularnewline \hline |
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45 | \end{tabular} \f] Fisher's Exact Test is based on exact |
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46 | probabilities from a specific distribution (the hypergeometric |
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47 | distribution). There's really no lower bound on the amount of |
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48 | data that is needed for Fisher's Exact Test. You do have to |
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49 | have at least one data value in each row and one data value in |
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50 | each column. If an entire row or column is zero, then you |
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51 | don't really have a 2 by 2 table. But you can use Fisher's |
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52 | Exact Test when one of the cells in your table has a zero in |
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53 | it. Fisher's Exact Test is also very useful for highly |
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54 | imbalanced tables. If one or two of the cells in a two by two |
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55 | table have numbers in the thousands and one or two of the |
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56 | other cells has numbers less than 5, you can still use |
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57 | Fisher's Exact Test. For very large tables (where all four |
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58 | entries in the two by two table are large), your computer may |
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59 | take too much time to compute Fisher's Exact Test. In these |
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60 | situations, though, you might as well use the Chi-square test |
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61 | because a large sample approximation (that the Chi-square test |
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62 | relies on) is very reasonable. If all elements are larger than |
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63 | 10 a Chi-square test is reasonable to use. |
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64 | |
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65 | @note The statistica assumes that each column and row sum, |
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66 | respectively, are fixed. Just because you have a 2x2 table, this |
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67 | assumtion does not necessarily match you experimental upset. See |
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68 | e.g. Barnard's test for alternative. |
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69 | */ |
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70 | |
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71 | class Fisher |
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72 | { |
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73 | |
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74 | public: |
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75 | /// |
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76 | /// Default Constructor. |
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77 | /// |
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78 | Fisher(void); |
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79 | |
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80 | /// |
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81 | /// Destructor |
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82 | /// |
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83 | virtual ~Fisher(void); |
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84 | |
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85 | |
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86 | /// |
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87 | /// @return Chi2 score |
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88 | /// |
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89 | double Chi2(void) const; |
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90 | |
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91 | /** |
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92 | Calculates the expected values under the null hypothesis. |
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93 | \f$ a' = \frac{(a+c)(a+b)}{a+b+c+d} \f$, |
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94 | \f$ b' = \frac{(a+b)(b+d)}{a+b+c+d} \f$, |
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95 | \f$ c' = \frac{(a+c)(c+d)}{a+b+c+d} \f$, |
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96 | \f$ d' = \frac{(b+d)(c+d)}{a+b+c+d} \f$, |
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97 | */ |
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98 | void expected(double& a, double& b, double& c, double& d) const; |
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99 | |
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100 | /// |
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101 | /// If all elements in table is at least minimum_size(), a Chi2 |
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102 | /// approximation is used for p-value calculation. |
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103 | /// |
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104 | /// @return reference to minimum_size |
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105 | /// |
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106 | u_int& minimum_size(void); |
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107 | |
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108 | /// |
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109 | /// If all elements in table is at least minimum_size(), a Chi2 |
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110 | /// approximation is used for p-value calculation. |
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111 | /// |
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112 | /// @return const reference to minimum_size |
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113 | /// |
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114 | const u_int& minimum_size(void) const; |
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115 | |
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116 | /// |
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117 | /// If oddsratio is larger than unity, two-sided p-value is equal |
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118 | /// to 2*p_value_one_sided(). If oddsratio is smaller than unity |
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119 | /// two-sided p-value is equal to 2*(1-p_value_one_sided()). If |
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120 | /// oddsratio is unity two-sided p-value is equal to unity. |
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121 | /// |
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122 | /// If all elements in table is at least minimum_size(), a Chi2 |
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123 | /// approximation is used. |
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124 | /// |
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125 | /// @return 2-sided p-value |
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126 | /// |
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127 | double p_value() const; |
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128 | |
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129 | /// |
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130 | /// One-sided p-value is probability to get larger (or equal) oddsratio. |
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131 | /// |
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132 | /// If all elements in table is at least minimum_size(), a Chi2 |
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133 | /// approximation is used. |
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134 | /// |
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135 | /// @return One-sided p-value |
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136 | /// |
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137 | double p_value_one_sided() const; |
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138 | |
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139 | /** |
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140 | Function calculating odds ratio from 2x2 table |
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141 | \f[ \begin{tabular}{|c|c|} |
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142 | \hline a&b \tabularnewline \hline c&d \tabularnewline \hline |
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143 | \end{tabular} \f] as \f$ \frac{ad}{bc} \f$ |
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144 | |
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145 | @return odds ratio. |
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146 | |
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147 | @throw If table is invalid a runtime_error is thrown. A table |
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148 | is invalid if a row or column sum is zero. |
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149 | */ |
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150 | double oddsratio(const u_int a, const u_int b, |
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151 | const u_int c, const u_int d); |
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152 | |
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153 | private: |
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154 | bool calculate_p_exact() const; |
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155 | |
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156 | // two-sided |
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157 | double p_value_approximative(void) const; |
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158 | //two-sided |
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159 | double p_value_exact(void) const; |
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160 | |
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161 | u_int a_; |
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162 | u_int b_; |
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163 | u_int c_; |
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164 | u_int d_; |
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165 | u_int minimum_size_; |
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166 | double oddsratio_; |
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167 | }; |
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168 | |
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169 | }}} // of namespace statistics, yat, and theplu |
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170 | |
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171 | #endif |
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