annotate statistical_hypothesis_testing.xml @ 0:22ed769665b6 draft default tip

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author bgruening
date Sun, 01 Feb 2015 18:35:40 -0500
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1 <tool id="bg_statistical_hypothesis_testing" name="Statistical hypothesis testing" version="0.2">
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2 <description></description>
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3 <requirements>
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4 <requirement type="binary">@EXECUTABLE@</requirement>
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5 <requirement type="package" version="1.9">numpy</requirement>
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6 <requirement type="package" version="0.14">scipy</requirement>
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7 </requirements>
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8 <macros>
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9 <macro name="macro_sample_one_cols">
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10 <param name="sample_one_cols" multiple="True" type="data_column" data_ref="infile" label="Column for sample one"/>
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11 </macro>
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12 <macro name="macro_sample_two_cols">
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13 <param name="sample_two_cols" multiple="True" type="data_column" data_ref="infile" optional="True" label="Column for sample two"/>
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14 </macro>
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15 <macro name="macro_sample_cols_min2">
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16 <repeat name="samples" title="more samples" min='2'>
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17 <param name="sample_cols" multiple="True" type="data_column" data_ref="infile" label="Column for sample"/>
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18 </repeat>
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19 </macro>
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20 <macro name="macro_sample_cols_min3">
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21 <repeat name="samples" title="more samples" min='3'>
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22 <param name="sample_cols" multiple="True" type="data_column" data_ref="infile" label="Column for sample"/>
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23 </repeat>
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24 </macro>
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25 <macro name="macro_zero_method">
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26 <param name="zero_method" type="select" label="pratt,wilcox,zsplit">
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27 <option value="pratt">Pratt treatment: includes zero-differences in the ranking process</option>
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28 <option value="wilcox">Wilcox treatment: discards all zero-differences</option>
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29 <option value="zsplit">Zero rank split: just like Pratt, but spliting the zero rank between positive and negative ones</option>
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30 </param>
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31 </macro>
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32 <macro name="macro_center">
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33 <param name="center" type="select" label="Which function of the data to use in the test ">
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34 <option value="median">median</option>
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35 <option value="mean">mean</option>
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36 <option value="trimmed">trimmed</option>
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37 </param>
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38 </macro>
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39 <macro name="macro_interpolation">
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40 <param name="interpolation" type="select" label="this specifies the interpolation method to use, when the desired quantile lies between two data points i and j">
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41 <option value="fraction">fraction</option>
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42 <option value="lower">lower</option>
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43 <option value="higher">higher</option>
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44 </param>
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45 </macro>
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46 <macro name="macro_ties">
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47 <param name="ties" type="select" label="Determines how values equal to the grand median are classified in the contingency table">
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48 <option value="below">below</option>
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49 <option value="above">above</option>
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50 <option value="ignore">ignore</option>
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51 </param>
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52 </macro>
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53 <macro name="macro_method">
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54 <param name="method" type="select" label="Maximizes the Pearson correlation coefficient">
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55 <option value="pearsonr">pearsonr</option>
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56 <option value="mle">mle</option>
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57 <option value="all">all</option>
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58 </param>
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59 </macro>
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60 <macro name="macro_dist">
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61 <param name="dist" type="select" label="the type of distribution to test against. The default is ‘norm’ and ‘extreme1’ is a synonym for ‘gumbel’">
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62 <option value="norm">norm</option>
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63 <option value="expon">expon</option>
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64 <option value="logistic">logistic</option>
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65 <option value="gumbel">gumbel</option>
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66 <option value="extreme1">extreme1</option>
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67 </param>
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68 </macro>
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69 <macro name="macro_tail">
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70 <param name="tail" type="select" label="From which tail">
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71 <option value="right">right</option>
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72 <option value="left">left</option>
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73 </param>
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74 </macro>
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75 <macro name="macro_kind">
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76 <param name="kind" type="select" label="This optional parameter specifies the interpretation of the resulting score">
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77 <option value="rank">rank</option>
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78 <option value="weak">weak</option>
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79 <option value="strict">strict</option>
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80 <option value="mean">mean</option>
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81 </param>
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82 </macro>
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83 <macro name="macro_md">
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84 <param name="md" type="select" label="The method used to assign ranks to tied elements">
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85 <option value="average">average</option>
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86 <option value="min">min</option>
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87 <option value="max">max</option>
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88 <option value="dense">dense</option>
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89 <option value="ordinal">ordinal</option>
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90 </param>
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91 </macro>
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92 <macro name="macro_statistic">
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93 <param name="statistic" type="select" label="The statistic to compute ">
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94 <option value="mean">mean</option>
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95 <option value="median">median</option>
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96 <option value="count">count</option>
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97 <option value="sum">sum</option>
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98 </param>
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99 </macro>
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100 <macro name="macro_alternative">
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101 <param name="alternative" type="select" label="Defines the alternative hypothesis">
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102 <option value="two-sided">two-sided</option>
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103 <option value="less">less</option>
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104 <option value="greater">greater</option>
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105 </param>
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106 </macro>
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107 <macro name="macro_mode">
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108 <param name="mode" type="select" label="Defines the distribution used for calculating the p-value">
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109 <option value="approx">approx</option>
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110 <option value="asymp">asymp</option>
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111 </param>
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112 </macro>
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113 <macro name="macro_interpolation">
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114 <param name="interpolation" type="select" label="this specifies the interpolation method to use, when the desired quantile lies between two data points i and j">
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115 <option value="fraction">fraction</option>
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116 <option value="lower">lower</option>
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117 <option value="higher">higher</option>
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118 </param>
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119 </macro>
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120 <macro name="macro_correction">
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121 <param name="correction" type="boolean" truevalue="--correction" falsevalue="" checked="True" label="If True, and the degrees of freedom is 1, apply Yates’ correction for continuity."/>
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122 </macro>
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123 <macro name="macro_printextras">
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124 <param name="printextras" type="boolean" truevalue="--printextras" falsevalue="" checked="False" label="printextras" help="If True, if there are extra points a warning is raised saying how many of those points there are" />
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125 </macro>
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126 <macro name="macro_initial_lexsort">
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127 <param name="initial_lexsort" type="boolean" truevalue="--initial_lexsort" falsevalue="" checked="True" label="Whether to use lexsort or quicksort as the sorting method for the initial sort of the inputs"/>
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128 </macro>
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129 <macro name="macro_cdf">
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130 <param name="cdf" size="16" type="text" value="norm" label="If a string, it should be the name of a distribution in scipy.stats"/>
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131 </macro>
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132 <macro name="macro_ni">
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133 <param name="ni" size="5" type="integer" value="20" label="N" optional="True" help="Sample size if rvs is string or callable."/>
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134 </macro>
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135 <macro name="macro_mwu_use_continuity">
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136 <param name="mwu_use_continuity" type="boolean" label="Enable continuity correction" help="Whether a continuity correction (1/2.) should be taken into account." truevalue="--mwu_use_continuity" falsevalue="" checked="true" />
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137 </macro>
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138 <macro name="macro_equal_var">
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139 <param name="equal_var" type="boolean" label="assume equal population" help="If set perform a standard independent 2 sample test that assumes equal population variances. If not set, perform Welch’s t-test, which does not assume equal population variance." truevalue="--equal_var" falsevalue="" checked="true" />
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140 </macro>
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141 <macro name="macro_base">
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142 <param name="base" size="5" type="float" value="1.6" label="base" help="The logarithmic base to use, defaults to e"/>
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143 </macro>
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144 <macro name="macro_med">
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145 <param name="med" size="16" type="text" value="fisher" label="Name of method to use to combine p-values"/>
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146 </macro>
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147 <macro name="macro_reta">
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148 <param name="reta" type="boolean" truevalue="--reta" falsevalue="" checked="False" label="Whether or not to return the internally computed a values." help="Whether or not to return the internally computed a values"/>
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149 </macro>
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150 <macro name="macro_n_in">
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151 <param name="n" size="5" type="integer" value="1" label="the number of trials" help="This is ignored if x gives both the number of successes and failures"/>
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152 </macro>
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153 <macro name="macro_n_moment">
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154 <param name="n" size="5" type="integer" value="1" label="moment" help="order of central moment that is returned"/>
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155 </macro>
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156 <macro name="macro_equal_var">
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157 <param name="equal_var" type="boolean" label="assume equal population" help="If set perform a standard independent 2 sample test that assumes equal population variances. If not set, perform Welch’s t-test, which does not assume equal population variance." truevalue="--equal_var" falsevalue="" checked="true" />
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158 </macro>
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159 <macro name="macro_imbda">
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160 <param name="imbda" size="5" type="float" value="" label="imbda" optional="True" help="do the transformation for that value"/>
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161 </macro>
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162 <macro name="macro_ddof">
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163 <param name="ddof" size="5" type="integer" value="0" label="ddof" optional="True" help="Degrees of freedom correction for standard deviation. "/>
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164 </macro>
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165 <macro name="macro_dtype">
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166 <param name="dtype" size="16" type="text" value="" optional="True" label="Type of the returned array and of the accumulator in which the elements are summed"/>
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167 </macro>
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168 <macro name="macro_m">
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169 <param name="m" size="5" type="float" value="4" label="low" help="Lower bound factor of sigma clipping. Default is 4."/>
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170 </macro>
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171 <macro name="macro_mf">
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172 <param name="mf" size="5" type="float" value="" label="lower_limit" optional="True" help="lower values for the range of the histogram"/>
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173 </macro>
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174 <macro name="macro_nf">
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175 <param name="nf" size="5" type="float" value="" label="upper_limit" optional="True" help="higher values for the range of the histogram"/>
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176 </macro>
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177 <macro name="macro_b">
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178 <param name="b" size="5" type="integer" value="10" label="numbins" help="The number of bins to use for the histogram"/>
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179 </macro>
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180 <macro name="macro_proportiontocut">
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181 <param name="proportiontocut" size="5" type="float" value="0.05" label="proportiontocut" optional="True" help="Proportion (in range 0-1) of total data set to trim of each end"/>
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182 </macro>
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183 <macro name="macro_alpha">
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184 <param name="alpha" size="5" type="float" value="0.9" label="alpha" optional="True" help="Probability that the returned confidence interval contains the true parameter"/>
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185 </macro>
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186 <macro name="macro_score">
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187 <param name="score" size="5" type="integer" value="0" label="score" optional="True" help="Score that is compared to the elements in a"/>
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188 </macro>
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parents:
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189 <macro name="macro_axis">
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parents:
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190 <param name="axis" size="5" type="integer" value="0" label="0 means one-dimensional array" help="Axis along which the kurtosis is calculated"/>
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parents:
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191 </macro>
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parents:
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192 <macro name="macro_new">
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parents:
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193 <param name="new" size="5" type="float" value="0" label="newval" help="Value to put in place of values in a outside of bounds"/>
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parents:
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194 </macro>
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parents:
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195 <macro name="macro_fisher">
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parents:
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196 <param name="fisher" type="boolean" truevalue="--fisher" falsevalue="" checked="true" label="Fisher’s definition is used" help="If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0)." />
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parents:
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197 </macro>
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parents:
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198 <macro name="macro_b">
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parents:
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199 <param name="b" size="5" type="integer" value="10" label="numbins" help="The number of bins to use for the histogram"/>
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parents:
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200 </macro>
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parents:
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201 <macro name="macro_proportiontocut">
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parents:
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202 <param name="proportiontocut" size="5" type="float" value="0.05" label="proportiontocut" optional="True" help="Proportion (in range 0-1) of total data set to trim of each end"/>
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203 </macro>
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parents:
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204 <macro name="macro_bias">
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parents:
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205 <param name="bias" type="boolean" truevalue="--bias" falsevalue="" checked="true" label="bias" help="If False, then the calculations are corrected for statistical bias." />
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parents:
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206 </macro>
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parents:
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207 <macro name="macro_lambda_">
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parents:
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208 <param name="lambda_" size="5" type="float" value="1" label="lambda_" optional="True" help="lambda_ gives the power in the Cressie-Read power divergence statistic."/>
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209 </macro>
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parents:
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210 <macro name="macro_inclusive">
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parents:
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211 <param name="inclusive" type="boolean" truevalue="--inclusive" falsevalue="" checked="true" label="flag" help="These flags determine whether values exactly equal to the lower or upper limits are included" />
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212 </macro>
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parents:
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213 <macro name="macro_p">
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parents:
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214 <param name="p" size="5" type="float" value="0.5" />
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parents:
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215 </macro>
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parents:
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216 <macro name="macro_inclusive1">
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parents:
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217 <param name="inclusive1" type="boolean" truevalue="--inclusive1" falsevalue="" checked="true" label="lower flag" help="These flags determine whether values exactly equal to the lower or upper limits are included" />
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218 </macro>
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219 <macro name="macro_inclusive2">
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parents:
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220 <param name="inclusive2" type="boolean" truevalue="--inclusive2" falsevalue="" checked="true" label="upper flag" help="These flags determine whether values exactly equal to the lower or upper limits are included" />
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221 </macro>
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222 <macro name="macro_inclusive">
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parents:
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223 <param name="inclusive" type="boolean" truevalue="--inclusive" falsevalue="" checked="true" label="flag" help="These flags determine whether values exactly equal to the lower or upper limits are included" />
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224 </macro>
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225 </macros>
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parents:
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226 <command interpreter="python">
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227 statistical_hypothesis_testing.py
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parents:
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228 --infile "${infile}"
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parents:
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229 --outfile "${outfile}"
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parents:
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230 --test_id "${test_methods.test_methods_opts}"
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parents:
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231 #if str($test_methods.test_methods_opts) == "describe" or str($test_methods.test_methods_opts) == "mode" or str($test_methods.test_methods_opts) == "normaltest" or str($test_methods.test_methods_opts) == "kurtosistest" or str($test_methods.test_methods_opts) == "skewtest" or str($test_methods.test_methods_opts) == "nanmean" or str($test_methods.test_methods_opts) == "nanmedian" or str($test_methods.test_methods_opts) == "variation" or str($test_methods.test_methods_opts) == "itemfreq" or str($test_methods.test_methods_opts) == "kurtosistest" or str($test_methods.test_methods_opts) == "skewtest" or str($test_methods.test_methods_opts) == "nanmean" or str($test_methods.test_methods_opts) == "nanmedian" or str($test_methods.test_methods_opts) == "variation" or str($test_methods.test_methods_opts) == "tiecorrect":
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232 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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233 #elif str($test_methods.test_methods_opts) == "gmean" or str($test_methods.test_methods_opts) == "hmean":
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parents:
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234 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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235 --dtype "${test_methods.dtype}"
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parents:
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236 #elif str($test_methods.test_methods_opts) == "anderson":
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parents:
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237 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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238 --dist "${test_methods.dist}"
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parents:
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239 #elif str($test_methods.test_methods_opts) == "binom_test":
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parents:
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240 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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241 --n "${test_methods.n}"
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parents:
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242 --p "${test_methods.p}"
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parents:
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243 #elif str($test_methods.test_methods_opts) == "kurtosis":
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parents:
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244 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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245 --axis "${test_methods.axis}"
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parents:
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246 $test_methods.fisher
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parents:
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247 $test_methods.bias
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parents:
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248 #elif str($test_methods.test_methods_opts) == "moment":
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parents:
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249 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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250 --n "${test_methods.n}"
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parents:
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251 #elif str($test_methods.test_methods_opts) == "bayes_mvs":
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parents:
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252 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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253 --alpha "${test_methods.alpha}"
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parents:
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254 #elif str($test_methods.test_methods_opts) == "percentileofscore":
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parents:
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255 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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256 --score "${test_methods.score}"
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parents:
diff changeset
257 --kind "${test_methods.kind}"
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parents:
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258 #elif str($test_methods.test_methods_opts) == "sigmaclip":
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parents:
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259 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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260 --n "${test_methods.n}"
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parents:
diff changeset
261 --m "${test_methods.m}"
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parents:
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262 #elif str($test_methods.test_methods_opts) == "chi2_contingency":
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parents:
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263 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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264 $test_methods.correction
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parents:
diff changeset
265 #if str($test_methods.lambda_).strip():
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parents:
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266 --lambda_ "${test_methods.lambda_}"
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parents:
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267 #end if
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268 #elif str($test_methods.test_methods_opts) == "skew" or str($test_methods.test_methods_opts) == "nanstd" :
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269 --sample_one_cols "${test_methods.sample_one_cols}"
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270 $test_methods.bias
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parents:
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271 #elif str($test_methods.test_methods_opts) == "rankdata":
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parents:
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272 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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273 --md "${test_methods.md}"
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parents:
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274 #elif str($test_methods.test_methods_opts) == "sem" or str($test_methods.test_methods_opts) == "zscore" or str($test_methods.test_methods_opts) == "signaltonoise":
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parents:
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275 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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276 #if str($test_methods.ddof).strip():
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parents:
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277 --ddof "${test_methods.ddof}"
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parents:
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278 #end if
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parents:
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279 #elif str($test_methods.test_methods_opts) == "trimboth":
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parents:
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280 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
281 #if str($test_methods.proportiontocut).strip():
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parents:
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282 --proportiontocut "${test_methods.proportiontocut}"
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parents:
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283 #end if
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parents:
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284 #elif str($test_methods.test_methods_opts) == "trim1":
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parents:
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285 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
286 #if str($test_methods.proportiontocut).strip():
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parents:
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287 --proportiontocut "${test_methods.proportiontocut}"
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parents:
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288 #end if
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parents:
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289 --tail "${test_methods.tail}"
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parents:
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290 #elif str($test_methods.test_methods_opts) == "boxcox":
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parents:
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291 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
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292 --alpha "${test_methods.alpha}"
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parents:
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293 #if str($test_methods.imbda).strip():
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parents:
diff changeset
294 --imbda "${test_methods.imbda}"
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parents:
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295 #end if
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parents:
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296 #elif str($test_methods.test_methods_opts) == "boxcox_llf":
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parents:
diff changeset
297 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
298 --imbda "${test_methods.imbda}"
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parents:
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299 #elif str($test_methods.test_methods_opts) == "kstest":
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parents:
diff changeset
300 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
301 #if str($test_methods.ni).strip():
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parents:
diff changeset
302 --ni "${test_methods.ni}"
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parents:
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303 #end if
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parents:
diff changeset
304 --cdf "${test_methods.cdf}"
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parents:
diff changeset
305 --alternative "${test_methods.alternative}"
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parents:
diff changeset
306 --mode "${test_methods.mode}"
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parents:
diff changeset
307
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parents:
diff changeset
308 #elif str($test_methods.test_methods_opts) == "boxcox_normmax":
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parents:
diff changeset
309 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
310 #if str($test_methods.mf).strip():
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parents:
diff changeset
311 --mf "${test_methods.mf}"
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parents:
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312 #end if
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parents:
diff changeset
313 #if str($test_methods.nf).strip():
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parents:
diff changeset
314 --nf "${test_methods.nf}"
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parents:
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315 #end if
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parents:
diff changeset
316 --method "${test_methods.method}"
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parents:
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317 #elif str($test_methods.test_methods_opts) == "tmean" or str($test_methods.test_methods_opts) == "tvar" or str($test_methods.test_methods_opts) == "tstd" or str($test_methods.test_methods_opts) == "tsem":
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parents:
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318 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
319 #if str($test_methods.mf).strip():
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parents:
diff changeset
320 --mf "${test_methods.mf}"
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parents:
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321 #end if
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parents:
diff changeset
322 #if str($test_methods.nf).strip():
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parents:
diff changeset
323 --nf "${test_methods.nf}"
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parents:
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324 #end if
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parents:
diff changeset
325 $test_methods.inclusive1
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parents:
diff changeset
326 $test_methods.inclusive2
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parents:
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327 #elif str($test_methods.test_methods_opts) == "tmin":
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parents:
diff changeset
328 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
329 #if str($test_methods.mf).strip():
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parents:
diff changeset
330 --mf "${test_methods.mf}"
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parents:
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331 #end if
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parents:
diff changeset
332 $test_methods.inclusive
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parents:
diff changeset
333 #elif str($test_methods.test_methods_opts) == "tmax":
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parents:
diff changeset
334 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
335 #if str($test_methods.nf).strip():
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parents:
diff changeset
336 --nf "${test_methods.nf}"
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parents:
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337 #end if
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parents:
diff changeset
338 $test_methods.inclusive
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parents:
diff changeset
339 #elif str($test_methods.test_methods_opts) == "histogram":
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parents:
diff changeset
340 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
341 #if str($test_methods.mf).strip():
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parents:
diff changeset
342 --mf "${test_methods.mf}"
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parents:
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343 #end if
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parents:
diff changeset
344 #if str($test_methods.nf).strip():
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parents:
diff changeset
345 --nf "${test_methods.nf}"
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parents:
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346 #end if
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parents:
diff changeset
347 --b "${test_methods.b}"
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parents:
diff changeset
348 $test_methods.printextras
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parents:
diff changeset
349 #elif str($test_methods.test_methods_opts) == "cumfreq":
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parents:
diff changeset
350 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
351 #if str($test_methods.mf).strip():
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parents:
diff changeset
352 --mf "${test_methods.mf}"
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parents:
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353 #end if
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parents:
diff changeset
354 #if str($test_methods.nf).strip():
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parents:
diff changeset
355 --nf "${test_methods.nf}"
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parents:
diff changeset
356 #end if
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parents:
diff changeset
357 --b "${test_methods.b}"
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parents:
diff changeset
358 #elif str($test_methods.test_methods_opts) == "threshold":
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parents:
diff changeset
359 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
360 #if str($test_methods.mf).strip():
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parents:
diff changeset
361 --mf "${test_methods.mf}"
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parents:
diff changeset
362 #end if
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parents:
diff changeset
363 #if str($test_methods.nf).strip():
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parents:
diff changeset
364 --nf "${test_methods.nf}"
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parents:
diff changeset
365 #end if
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parents:
diff changeset
366 --new "${test_methods.new}"
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parents:
diff changeset
367 #elif str($test_methods.test_methods_opts) == "relfreq":
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parents:
diff changeset
368 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
369 #if str($test_methods.mf).strip():
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parents:
diff changeset
370 --mf "${test_methods.mf}"
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parents:
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371 #end if
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parents:
diff changeset
372 #if str($test_methods.nf).strip():
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parents:
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373 --nf "${test_methods.nf}"
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parents:
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374 #end if
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parents:
diff changeset
375 --b "${test_methods.b}"
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parents:
diff changeset
376 #elif str($test_methods.test_methods_opts) == "spearmanr":
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parents:
diff changeset
377 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
378 #if str($test_methods.sample_two_cols).strip():
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parents:
diff changeset
379 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
380 #end if
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parents:
diff changeset
381 #elif str($test_methods.test_methods_opts) == "theilslopes":
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parents:
diff changeset
382 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
383 #if str($test_methods.sample_two_cols).strip():
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parents:
diff changeset
384 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
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385 #end if
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parents:
diff changeset
386 --alpha "${test_methods.alpha}"
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parents:
diff changeset
387 #elif str($test_methods.test_methods_opts) == "chisquare":
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parents:
diff changeset
388 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
389 #if str($test_methods.sample_two_cols).strip():
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parents:
diff changeset
390 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
391 #end if
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parents:
diff changeset
392 #if str($test_methods.ddof).strip():
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parents:
diff changeset
393 --ddof "${test_methods.ddof}"
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parents:
diff changeset
394 #end if
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parents:
diff changeset
395 #elif str($test_methods.test_methods_opts) == "power_divergence":
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parents:
diff changeset
396 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
397 #if str($test_methods.sample_two_cols).strip():
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parents:
diff changeset
398 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
399 #end if
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parents:
diff changeset
400 #if str($test_methods.ddof).strip():
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parents:
diff changeset
401 --ddof "${test_methods.ddof}"
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parents:
diff changeset
402 #end if
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parents:
diff changeset
403 #if str($test_methods.lambda_).strip():
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parents:
diff changeset
404 --lambda_ "${test_methods.lambda_}"
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parents:
diff changeset
405 #end if
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parents:
diff changeset
406 #elif str($test_methods.test_methods_opts) == "combine_pvalues":
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parents:
diff changeset
407 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
408 #if str($test_methods.sample_two_cols).strip() and $test_methods.sample_two_cols:
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parents:
diff changeset
409 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
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410 #end if
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parents:
diff changeset
411 --med "${test_methods.med}"
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parents:
diff changeset
412 #elif str($test_methods.test_methods_opts) == "wilcoxon":
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parents:
diff changeset
413 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
414 #if str($test_methods.sample_two_cols).strip() and $test_methods.sample_two_cols:
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parents:
diff changeset
415 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
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416 #end if
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parents:
diff changeset
417 --zero_method "${test_methods.zero_method}"
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parents:
diff changeset
418 $test_methods.correction
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parents:
diff changeset
419 #elif str($test_methods.test_methods_opts) == "ranksums" or str($test_methods.test_methods_opts) == "ansari" or str($test_methods.test_methods_opts) == "linregress" or str($test_methods.test_methods_opts) == "pearsonr" or str($test_methods.test_methods_opts) == "pointbiserialr" or str($test_methods.test_methods_opts) == "ks_2samp" or str($test_methods.test_methods_opts) == "ttest_1samp" or str($test_methods.test_methods_opts) == "histogram2":
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parents:
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420 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
421 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
422 #elif str($test_methods.test_methods_opts) == "entropy":
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parents:
diff changeset
423 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
424 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
425 --base "${test_methods.base}"
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parents:
diff changeset
426 #elif str($test_methods.test_methods_opts) == "kendalltau":
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parents:
diff changeset
427 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
428 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
429 $test_methods.initial_lexsort
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parents:
diff changeset
430 #elif str($test_methods.test_methods_opts) == "kendalltau":
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parents:
diff changeset
431 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
432 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
433 $test_methods.initial_lexsort
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parents:
diff changeset
434 #elif str($test_methods.test_methods_opts) == "mannwhitneyu":
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parents:
diff changeset
435 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
436 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
437 $test_methods.mwu_use_continuity
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parents:
diff changeset
438 #elif str($test_methods.test_methods_opts) == "ttest_ind":
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parents:
diff changeset
439 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
440 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
441 $test_methods.equal_var
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parents:
diff changeset
442 #elif str($test_methods.test_methods_opts) == "ttest_rel":
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parents:
diff changeset
443 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
444 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
445 --axis "${test_methods.axis}"
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parents:
diff changeset
446 #elif str($test_methods.test_methods_opts) == "zmap":
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parents:
diff changeset
447 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
448 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
449 #if str($test_methods.ddof).strip():
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parents:
diff changeset
450 --ddof "${test_methods.ddof}"
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parents:
diff changeset
451 #end if
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parents:
diff changeset
452 #elif str($test_methods.test_methods_opts) == "binned_statistic":
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parents:
diff changeset
453 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
454 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
455 #if str($test_methods.mf).strip():
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parents:
diff changeset
456 --mf "${test_methods.mf}"
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parents:
diff changeset
457 #end if
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parents:
diff changeset
458 #if str($test_methods.nf).strip():
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parents:
diff changeset
459 --nf "${test_methods.nf}"
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parents:
diff changeset
460 #end if
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parents:
diff changeset
461 --statistic "${test_methods.statistic}"
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parents:
diff changeset
462 --b "${test_methods.b}"
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parents:
diff changeset
463 #elif str($test_methods.test_methods_opts) == "scoreatpercentile":
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parents:
diff changeset
464 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
465 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
466 #if str($test_methods.mf).strip():
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parents:
diff changeset
467 --mf "${test_methods.mf}"
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parents:
diff changeset
468 #end if
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parents:
diff changeset
469 #if str($test_methods.nf).strip():
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parents:
diff changeset
470 --nf "${test_methods.nf}"
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parents:
diff changeset
471 #end if
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parents:
diff changeset
472 --interpolation "${test_methods.interpolation}"
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parents:
diff changeset
473 #elif str($test_methods.test_methods_opts) == "mood":
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parents:
diff changeset
474 --axis "${test_methods.axis}"
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parents:
diff changeset
475 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
476 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
477 #elif str($test_methods.test_methods_opts) == "shapiro":
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parents:
diff changeset
478 $test_methods.reta
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parents:
diff changeset
479 --sample_one_cols "${test_methods.sample_one_cols}"
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parents:
diff changeset
480 --sample_two_cols "${test_methods.sample_two_cols}"
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parents:
diff changeset
481 #elif str($test_methods.test_methods_opts) == "bartlett" or str($test_methods.test_methods_opts) == "f_oneway" or str($test_methods.test_methods_opts) == "kruskal" or str($test_methods.test_methods_opts) == "friedmanchisquare" or str($test_methods.test_methods_opts) == "obrientransform":
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parents:
diff changeset
482 --sample_cols "#echo ';'.join( [str($list.sample_cols) for $list in $test_methods.samples] )#"
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parents:
diff changeset
483 #elif str($test_methods.test_methods_opts) == "levene":
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parents:
diff changeset
484 --sample_cols "#echo ';'.join( [str($list.sample_cols) for $list in $test_methods.samples] )#"
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parents:
diff changeset
485 --center "${test_methods.center}"
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parents:
diff changeset
486 #if str($test_methods.proportiontocut).strip():
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parents:
diff changeset
487 --proportiontocut "${test_methods.proportiontocut}"
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parents:
diff changeset
488 #end if
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parents:
diff changeset
489 #elif str($test_methods.test_methods_opts) == "fligner":
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parents:
diff changeset
490 --sample_cols "#echo ';'.join( [str($list.sample_cols) for $list in $test_methods.samples] )#"
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parents:
diff changeset
491 --center "${test_methods.center}"
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parents:
diff changeset
492 #if str($test_methods.proportiontocut).strip():
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parents:
diff changeset
493 --proportiontocut "${test_methods.proportiontocut}"
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parents:
diff changeset
494 #end if
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parents:
diff changeset
495 #elif str($test_methods.test_methods_opts) == "median_test":
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parents:
diff changeset
496 --sample_cols "#echo ';'.join( [str($list.sample_cols) for $list in $test_methods.samples] )#"
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parents:
diff changeset
497 $test_methods.correction
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parents:
diff changeset
498 #if str($test_methods.lambda_).strip():
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parents:
diff changeset
499 --lambda_ "${test_methods.lambda_}"
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parents:
diff changeset
500 #end if
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parents:
diff changeset
501 --ties "${test_methods.ties}"
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parents:
diff changeset
502 #end if
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parents:
diff changeset
503 </command>
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parents:
diff changeset
504 <inputs>
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parents:
diff changeset
505 <param name="infile" type="data" format="tabular" label="Sample file" help="tabular file containing the observations"/>
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parents:
diff changeset
506 <conditional name="test_methods">
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parents:
diff changeset
507 <param name="test_methods_opts" type="select" label="Select a statistical test method">
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parents:
diff changeset
508 <option value="describe">Computes several descriptive statistics of the passed array</option>
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parents:
diff changeset
509 <option value="gmean">Compute the geometric mean along the specified axis</option>
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parents:
diff changeset
510 <option value="hmean">Calculates the harmonic mean along the specified axis</option>
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parents:
diff changeset
511 <option value="kurtosis">Computes the kurtosis (Fisher or Pearson) of a dataset</option>
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parents:
diff changeset
512 <option value="kurtosistest">Tests whether a dataset has normal kurtosis</option>
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parents:
diff changeset
513 <option value="mode">show the most common value in the passed array</option>
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parents:
diff changeset
514 <option value="moment">Calculates the nth moment about the mean for a sample</option>
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parents:
diff changeset
515 <option value="normaltest">Tests whether a sample differs from a normal distribution</option>
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parents:
diff changeset
516 <option value="skew">Computes the skewness of a data set.</option>
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parents:
diff changeset
517 <option value="skewtest">Tests whether the skew is different from the normal distribution.</option>
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parents:
diff changeset
518 <option value="tmean">Compute the trimmed mean</option>
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parents:
diff changeset
519 <option value="tvar">Compute the trimmed variance</option>
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parents:
diff changeset
520 <option value="tmin">Compute the trimmed minimum</option>
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parents:
diff changeset
521 <option value="tmax">Compute the trimmed maximum</option>
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parents:
diff changeset
522 <option value="tstd">Compute the trimmed sample standard deviation</option>
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parents:
diff changeset
523 <option value="tsem">Compute the trimmed standard error of the mean</option>
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parents:
diff changeset
524 <option value="nanmean">Compute the mean ignoring nans</option>
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parents:
diff changeset
525 <option value="nanstd">Compute the standard deviation ignoring nans</option>
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parents:
diff changeset
526 <option value="nanmedian">Compute the median ignoring nan values.</option>
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parents:
diff changeset
527 <option value="variation">Computes the coefficient of variation, the ratio of the biased standard deviation to the mean.</option>
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parents:
diff changeset
528 <option value="cumfreq">Returns a cumulative frequency histogram, using the histogram function</option>
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parents:
diff changeset
529 <option value="histogram2">Compute histogram using divisions in bins</option>
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parents:
diff changeset
530 <option value="histogram">Separates the range into several bins</option>
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parents:
diff changeset
531 <option value="itemfreq">Compute frequencies for each number</option>
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parents:
diff changeset
532 <option value="percentileofscore">The percentile rank of a score relative to a list of scores</option>
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parents:
diff changeset
533 <option value="scoreatpercentile">Calculate the score at a given percentile of the input sequence</option>
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parents:
diff changeset
534 <option value="relfreq">Returns a relative frequency histogram, using the histogram function</option>
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parents:
diff changeset
535 <option value="binned_statistic">Compute a binned statistic for a set of data</option>
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parents:
diff changeset
536 <option value="obrientransform">Computes the O’Brien transform on input data</option>
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parents:
diff changeset
537 <option value="signaltonoise">The signal-to-noise ratio of the input data</option>
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parents:
diff changeset
538 <option value="bayes_mvs">Bayesian confidence intervals for the mean, var, and std</option>
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parents:
diff changeset
539 <option value="sem">Calculates the standard error of the mean of the value</option>
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parents:
diff changeset
540 <option value="zmap">Calculates the relative z-scores</option>
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parents:
diff changeset
541 <option value="zscore">Calculates the z score of each value in the sample, relative to the sample mean and standard deviation</option>
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parents:
diff changeset
542 <option value="sigmaclip">Iterative sigma-clipping of array elements</option>
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parents:
diff changeset
543 <option value="threshold">Clip array to a given value</option>
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parents:
diff changeset
544 <option value="trimboth">Slices off a proportion of items from both ends of an array</option>
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parents:
diff changeset
545 <option value="trim1">Slices off a proportion of items from ONE end of the passed array distribution</option>
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parents:
diff changeset
546 <option value="f_oneway">Performs a 1-way ANOVA</option>
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parents:
diff changeset
547 <option value="pearsonr">Calculates a Pearson correlation coefficient and the p-value for testing non-correlation.</option>
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parents:
diff changeset
548 <option value="spearmanr">Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation</option>
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parents:
diff changeset
549 <option value="pointbiserialr">Calculates a point biserial correlation coefficient and the associated p-value</option>
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parents:
diff changeset
550 <option value="kendalltau">Calculates Kendall’s tau, a correlation measure for ordinal data</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
551 <option value="linregress">This computes a least-squares regression for two sets of measurements</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
552 <option value="theilslopes">Computes the Theil-Sen estimator for a set of points (x, y)</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
553 <option value="ttest_1samp">Calculates the T-test for the mean of ONE group of scores</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
554 <option value="ttest_ind">T-test for the means of TWO INDEPENDENT samples of scores</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
555 <option value="ttest_rel">T-test for the means of TWO RELATED samples of scores</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
556 <option value="kstest">Perform the Kolmogorov-Smirnov test for goodness of fit.</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
557 <option value="chisquare">Calculates a one-way chi square test</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
558 <option value="power_divergence">Cressie-Read power divergence statistic and goodness of fit test</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
559 <option value="ks_2samp">Computes the Kolmogorov-Smirnov statistic on 2 samples</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
560 <option value="mannwhitneyu">Computes the Mann-Whitney rank test on samples x and y</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
561 <option value="tiecorrect">Tie correction factor for ties in the Mann-Whitney U and Kruskal-Wallis H tests</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
562 <option value="rankdata">Assign ranks to data, dealing with ties appropriately</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
563 <option value="ranksums">Compute the Wilcoxon rank-sum statistic for two samples</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
564 <option value="wilcoxon">Calculate the Wilcoxon signed-rank test</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
565 <option value="kruskal">Compute the Kruskal-Wallis H-test for independent samples</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
566 <option value="friedmanchisquare">Computes the Friedman test for repeated measurements</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
567 <option value="combine_pvalues">Methods for combining the p-values of independent tests bearing upon the same hypothesis</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
568 <option value="ansari">Perform the Ansari-Bradley test for equal scale parameters</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
569 <option value="bartlett">Perform Bartlett’s test for equal variances</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
570 <option value="levene">Perform Levene test for equal variances.</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
571 <option value="shapiro">Perform the Shapiro-Wilk test for normality</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
572 <option value="anderson">Anderson-Darling test for data coming from a particular distribution</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
573 <option value="binom_test">Perform a test that the probability of success is p</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
574 <option value="fligner">Perform Fligner’s test for equal variances</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
575 <option value="median_test">Mood’s median test</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
576 <option value="mood">Perform Mood’s test for equal scale parameters</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
577 <option value="boxcox">Return a positive dataset transformed by a Box-Cox power transformation</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
578 <option value="boxcox_normmax">Compute optimal Box-Cox transform parameter for input data</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
579 <option value="boxcox_llf">The boxcox log-likelihood function</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
580 <option value="boxcox">Return a positive dataset transformed by a Box-Cox power transformation</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
581 <option value="entropy">Calculate the entropy of a distribution for given probability values</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
582 <option value="chi2_contingency">Chi-square test of independence of variables in a contingency table</option>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
583 </param>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
584 <when value="itemfreq">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
585 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
586 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
587 <when value="sem">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
588 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
589 <expand macro="macro_ddof"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
590 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
591 <when value="zscore">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
592 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
593 <expand macro="macro_ddof"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
594 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
595 <when value="relfreq">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
596 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
597 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
598 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
599 <expand macro="macro_b"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
600 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
601 <when value="signaltonoise">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
602 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
603 <expand macro="macro_ddof"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
604 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
605 <when value="bayes_mvs">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
606 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
607 <expand macro="macro_alpha"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
608 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
609 <when value="threshold">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
610 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
611 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
612 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
613 <expand macro="macro_new"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
614 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
615 <when value="trimboth">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
616 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
617 <expand macro="macro_proportiontocut"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
618 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
619 <when value="trim1">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
620 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
621 <expand macro="macro_proportiontocut"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
622 <expand macro="macro_tail"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
623 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
624 <when value="percentileofscore">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
625 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
626 <expand macro="macro_score"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
627 <expand macro="macro_kind"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
628 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
629 <when value="normaltest">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
630 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
631 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
632 <when value="kurtosistest">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
633 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
634 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
635 <when value="describe">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
636 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
637 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
638 <when value="mode">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
639 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
640 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
641 <when value="normaltest">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
642 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
643 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
644 <when value="kurtosistest">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
645 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
646 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
647 <when value="skewtest">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
648 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
649 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
650 <when value="nanmean">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
651 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
652 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
653 <when value="nanmedian">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
654 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
655 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
656 <when value="variation">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
657 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
658 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
659 <when value="tiecorrect">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
660 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
661 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
662 <when value="gmean">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
663 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
664 <expand macro="macro_dtype"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
665 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
666 <when value="hmean">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
667 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
668 <expand macro="macro_dtype"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
669 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
670 <when value="sigmaclip">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
671 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
672 <expand macro="macro_m"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
673 <expand macro="macro_n_in"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
674 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
675 <when value="kurtosis">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
676 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
677 <expand macro="macro_axis"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
678 <expand macro="macro_fisher"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
679 <expand macro="macro_bias"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
680 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
681 <when value="chi2_contingency">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
682 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
683 <expand macro="macro_correction"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
684 <expand macro="macro_lambda_"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
685 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
686 <when value="binom_test">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
687 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
688 <expand macro="macro_n_in"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
689 <expand macro="macro_p"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
690 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
691 <when value="moment">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
692 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
693 <expand macro="macro_n_moment"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
694 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
695 <when value="skew">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
696 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
697 <expand macro="macro_bias"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
698 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
699 <when value="tmean">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
700 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
701 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
702 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
703 <expand macro="macro_inclusive1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
704 <expand macro="macro_inclusive2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
705 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
706 <when value="tmin">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
707 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
708 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
709 <expand macro="macro_inclusive"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
710 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
711 <when value="tmax">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
712 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
713 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
714 <expand macro="macro_inclusive"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
715 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
716 <when value="tvar">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
717 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
718 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
719 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
720 <expand macro="macro_inclusive1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
721 <expand macro="macro_inclusive2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
722 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
723 <when value="tstd">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
724 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
725 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
726 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
727 <expand macro="macro_inclusive1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
728 <expand macro="macro_inclusive2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
729 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
730 <when value="tsem">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
731 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
732 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
733 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
734 <expand macro="macro_inclusive1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
735 <expand macro="macro_inclusive2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
736 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
737 <when value="nanstd">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
738 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
739 <expand macro="macro_bias"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
740 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
741 <when value="histogram">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
742 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
743 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
744 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
745 <expand macro="macro_b"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
746 <expand macro="macro_printextras"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
747
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
748 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
749 <when value="cumfreq">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
750 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
751 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
752 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
753 <expand macro="macro_b"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
754 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
755 <when value="boxcox">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
756 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
757 <expand macro="macro_imbda"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
758 <expand macro="macro_alpha"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
759 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
760 <when value="boxcox_llf">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
761 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
762 <expand macro="macro_imbda"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
763 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
764 <when value="boxcox_normmax">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
765 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
766 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
767 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
768 <expand macro="macro_method"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
769 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
770 <when value="anderson">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
771 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
772 <expand macro="macro_dist"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
773 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
774 <when value="rankdata">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
775 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
776 <expand macro="macro_md"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
777 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
778 <when value="kstest">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
779 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
780 <expand macro="macro_cdf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
781 <expand macro="macro_ni"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
782 <expand macro="macro_alternative"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
783 <expand macro="macro_mode"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
784 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
785
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
786 <when value="spearmanr">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
787 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
788 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
789 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
790 <when value="ranksums">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
791 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
792 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
793 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
794 <when value="ansari">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
795 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
796 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
797 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
798 <when value="linregress">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
799 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
800 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
801 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
802 <when value="histogram2">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
803 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
804 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
805 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
806 <when value="pearsonr">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
807 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
808 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
809 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
810 <when value="pointbiserialr">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
811 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
812 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
813 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
814 <when value="ttest_1samp">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
815 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
816 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
817 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
818 <when value="ks_2samp">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
819 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
820 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
821 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
822 <when value="kendalltau">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
823 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
824 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
825 <expand macro="macro_initial_lexsort"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
826
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
827 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
828 <when value="mannwhitneyu">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
829 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
830 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
831 <expand macro="macro_mwu_use_continuity"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
832 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
833 <when value="ttest_ind">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
834 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
835 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
836 <expand macro="macro_equal_var"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
837 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
838 <when value="ttest_rel">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
839 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
840 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
841 <expand macro="macro_axis"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
842 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
843 <when value="entropy">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
844 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
845 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
846 <expand macro="macro_base"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
847 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
848 <when value="theilslopes">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
849 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
850 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
851 <expand macro="macro_alpha"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
852 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
853 <when value="zmap">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
854 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
855 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
856 <expand macro="macro_ddof"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
857 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
858 <when value="chisquare">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
859 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
860 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
861 <expand macro="macro_ddof"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
862 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
863 <when value="power_divergence">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
864 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
865 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
866 <expand macro="macro_lambda_"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
867 <expand macro="macro_ddof"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
868 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
869 <when value="combine_pvalues">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
870 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
871 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
872 <expand macro="macro_med"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
873 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
874 <when value="mood">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
875 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
876 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
877 <expand macro="macro_axis"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
878 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
879 <when value="shapiro">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
880 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
881 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
882 <expand macro="macro_reta"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
883 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
884 <when value="wilcoxon">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
885 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
886 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
887 <expand macro="macro_zero_method"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
888 <expand macro="macro_correction"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
889 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
890 <when value="scoreatpercentile">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
891 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
892 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
893 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
894 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
895 <expand macro="macro_interpolation"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
896 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
897 <when value="binned_statistic">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
898 <expand macro="macro_sample_one_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
899 <expand macro="macro_sample_two_cols"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
900 <expand macro="macro_mf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
901 <expand macro="macro_nf"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
902 <expand macro="macro_b"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
903 <expand macro="macro_statistic"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
904 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
905 <when value="fligner">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
906 <expand macro="macro_proportiontocut"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
907 <expand macro="macro_center"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
908 <expand macro="macro_sample_cols_min2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
909 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
910 <when value="f_oneway">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
911 <expand macro="macro_sample_cols_min2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
912 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
913 <when value="kruskal">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
914 <expand macro="macro_sample_cols_min2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
915 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
916 <when value="friedmanchisquare">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
917 <expand macro="macro_sample_cols_min3"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
918 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
919 <when value="bartlett">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
920 <expand macro="macro_sample_cols_min2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
921 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
922 <when value="levene">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
923 <expand macro="macro_proportiontocut"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
924 <expand macro="macro_center"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
925 <expand macro="macro_sample_cols_min2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
926 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
927 <when value="obrientransform">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
928 <expand macro="macro_sample_cols_min2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
929 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
930 <when value="median_test">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
931 <expand macro="macro_ties"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
932 <expand macro="macro_correction"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
933 <expand macro="macro_lambda_"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
934 <expand macro="macro_sample_cols_min2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
935 </when>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
936 </conditional>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
937 </inputs>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
938 <outputs>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
939 <data format="tabular" name="outfile" label="${tool.name} on ${on_string}" />
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
940 </outputs>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
941 <tests>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
942 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
943 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
944 <output name="outfile" file="boxcox_normmax2.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
945 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
946 <param name="test_methods_opts" value="boxcox_normmax"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
947 <param name="method" value="pearsonr"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
948 <param name="mf" value="-2.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
949 <param name="nf" value="2.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
950 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
951 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
952 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
953 <output name="outfile" file="normaltest.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
954 <param name="sample_one_cols" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
955 <param name="test_methods_opts" value="normaltest"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
956 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
957 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
958 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
959 <output name="outfile" file="tmin.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
960 <param name="sample_one_cols" value="1,2,3,4,5,6"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
961 <param name="test_methods_opts" value="tmin"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
962 <param name="mf" value="10.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
963 <param name="inclusive" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
964 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
965 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
966 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
967 <output name="outfile" file="shapiro2.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
968 <param name="sample_one_cols" value="1,2,3,4,8,9"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
969 <param name="sample_two_cols" value="5,6,7"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
970 <param name="test_methods_opts" value="shapiro"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
971 <param name="reta" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
972 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
973 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
974 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
975 <output name="outfile" file="obrientransform.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
976 <repeat name="samples">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
977 <param name="sample_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
978 </repeat>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
979 <repeat name="samples">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
980 <param name="sample_cols" value="5,6,7,8"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
981 </repeat>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
982 <param name="test_methods_opts" value="obrientransform"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
983 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
984 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
985 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
986 <output name="outfile" file="median_test_result1.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
987 <repeat name="samples">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
988 <param name="sample_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
989 </repeat>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
990 <repeat name="samples">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
991 <param name="sample_cols" value="5,6,7,8"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
992 </repeat>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
993 <repeat name="samples">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
994 <param name="sample_cols" value="9,10,11,12"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
995 </repeat>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
996 <param name="test_methods_opts" value="median_test"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
997 <param name="ties" value="above"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
998 <param name="correction" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
999 <param name="lambda_" value="1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1000 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1001 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1002 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1003 <output name="outfile" file="wilcoxon_result1.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1004 <param name="sample_one_cols" value="1,2,3,4,5,6,7,8,9,10"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1005 <param name="sample_two_cols" value="11,12,13,14,15,16,17,18,19,20"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1006 <param name="test_methods_opts" value="wilcoxon"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1007 <param name="zero_method" value="pratt"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1008 <param name="correction" value="False"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1009 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1010 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1011 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1012 <output name="outfile" file="percentileofscore1.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1013 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1014 <param name="sample_two_cols" value="5,6,7,8"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1015 <param name="test_methods_opts" value="percentileofscore"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1016 <param name="score" value="1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1017 <param name="kind" value="rank"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1018 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1019 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1020 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1021 <output name="outfile" file="percentileofscore2.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1022 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1023 <param name="sample_two_cols" value="5,6,7,8"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1024 <param name="test_methods_opts" value="percentileofscore"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1025 <param name="score" value="2"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1026 <param name="kind" value="mean"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1027 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1028 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1029 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1030 <output name="outfile" file="trim1.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1031 <param name="sample_one_cols" value="1,2,3,4,5,6"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1032 <param name="test_methods_opts" value="trim1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1033 <param name="tail" value="left"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1034 <param name="proportiontocut" value="1.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1035 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1036 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1037 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1038 <output name="outfile" file="scoreatpercentile.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1039 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1040 <param name="sample_two_cols" value="11,12,13,14"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1041 <param name="test_methods_opts" value="scoreatpercentile"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1042 <param name="mf" value="5.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1043 <param name="nf" value="50.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1044 <param name="interpolation" value="lower"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1045 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1046 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1047 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1048 <output name="outfile" file="anderson.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1049 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1050 <param name="test_methods_opts" value="anderson"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1051 <param name="dist" value="expon"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1052 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1053 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1054 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1055 <output name="outfile" file="boxcox_normmax.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1056 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1057 <param name="test_methods_opts" value="boxcox_normmax"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1058 <param name="method" value="mle"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1059 <param name="mf" value="-3.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1060 <param name="nf" value="3.0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1061 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1062 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1063 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1064 <output name="outfile" file="f_oneway.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1065 <repeat name="samples">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1066 <param name="sample_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1067 </repeat>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1068 <repeat name="samples">
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1069 <param name="sample_cols" value="5,6,7,8"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1070 </repeat>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1071 <param name="test_methods_opts" value="f_oneway"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1072 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1073 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1074 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1075 <output name="outfile" file="shapiro.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1076 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1077 <param name="sample_two_cols" value="5,6"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1078 <param name="test_methods_opts" value="shapiro"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1079 <param name="reta" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1080 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1081 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1082 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1083 <output name="outfile" file="power_divergence.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1084 <param name="sample_one_cols" value="1,2,3,4"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1085 <param name="sample_two_cols" value="5,6,7,8"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1086 <param name="test_methods_opts" value="power_divergence"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1087 <param name="ddof" value="1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1088 <param name="lambda_" value="1"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1089 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1090 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1091 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1092 <output name="outfile" file="itemfreq.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1093 <param name="sample_one_cols" value="1,2,3,4,5,6,7,8,9,10"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1094 <param name="test_methods_opts" value="itemfreq"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1095 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1096 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1097 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1098 <output name="outfile" file="trimboth.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1099 <param name="sample_one_cols" value="1,2,3,4,5,6,7,8,9,10"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1100 <param name="proportiontocut" value="0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1101 <param name="test_methods_opts" value="trimboth"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1102 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1103 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1104 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1105 <output name="outfile" file="tmean.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1106 <param name="sample_one_cols" value="1,2,3,4,5,6"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1107 <param name="test_methods_opts" value="tmean"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1108 <param name="mf" value="0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1109 <param name="nf" value="50"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1110 <param name="inclusive1" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1111 <param name="inclusive2" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1112 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1113 <test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1114 <param name="infile" value="input.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1115 <output name="outfile" file="tvar.tabular"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1116 <param name="sample_one_cols" value="1,2,3,4,5,6"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1117 <param name="test_methods_opts" value="tvar"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1118 <param name="mf" value="0"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1119 <param name="nf" value="50"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1120 <param name="inclusive1" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1121 <param name="inclusive2" value="True"/>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1122 </test>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1123 </tests>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1124 <help>
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1125
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1126 .. class:: warningmark
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1127
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1128
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1129 Computes a large number of probability distributions as well as a statistical functions of any kind.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1130 For more informations have a look at the `SciPy site`_.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1131
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1132 .. _`SciPy site`: http://docs.scipy.org/doc/scipy/reference/stats.html
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1133
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1134
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1135 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1136
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1137 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1138 Describe
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1139 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1140
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1141 Computes several descriptive statistics for samples x
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1142
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1143 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1144
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1145 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1146
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1147 size of the data : int
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1148
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1149 length of data along axis
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1150
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1151 (min, max): tuple of ndarrays or floats
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1152
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1153 minimum and maximum value of data array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1154
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1155 arithmetic mean : ndarray or float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1156
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1157 mean of data along axis
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1158
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1159 unbiased variance : ndarray or float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1160
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1161 variance of the data along axis, denominator is number of observations minus one.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1162
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1163 biased skewness : ndarray or float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1164
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1165 skewness, based on moment calculations with denominator equal to the number of observations, i.e. no degrees of freedom correction
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1166
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1167 biased kurtosis : ndarray or float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1168
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1169 kurtosis (Fisher), the kurtosis is normalized so that it is zero for the normal distribution. No degrees of freedom or bias correction is used.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1170
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1171 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1172
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1173 describe([4,417,8,3]) the result is (4,(3.0, 417.0),108.0,42440.6666667 ,1.15432044278, -0.666961688151)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1174
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1175
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1176 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1177 Gmean
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1178 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1179
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1180 Compute the geometric mean along the specified axis.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1181
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1182 Returns the geometric average of the array elements. That is: n-th root of (x1 * x2 * ... * xn)
22ed769665b6 Uploaded
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parents:
diff changeset
1183
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1184 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1185
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1186 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1187
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1188 gmean : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1189
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1190 see dtype parameter above
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1191
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1192 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1193
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1194 stats.gmean([4,17,8,3],dtype='float64') the result is (6.35594365562)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1195
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1196 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1197 Hmean
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1198 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1199
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1200 py.stats.hmean(a, axis=0, dtype=None)[source]
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1201 Calculates the harmonic mean along the specified axis.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1202
22ed769665b6 Uploaded
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parents:
diff changeset
1203 That is: n / (1/x1 + 1/x2 + ... + 1/xn)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1204
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1205 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1206
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1207 hmean : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1208
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1209 see dtype parameter above
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1210
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1211
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1212 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1213
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1214 stats.hmean([4,17,8,3],dtype='float64')the result is (5.21405750799)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1215
22ed769665b6 Uploaded
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parents:
diff changeset
1216 ========
22ed769665b6 Uploaded
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parents:
diff changeset
1217 Kurtosis
22ed769665b6 Uploaded
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parents:
diff changeset
1218 ========
22ed769665b6 Uploaded
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parents:
diff changeset
1219
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1220 Computes the kurtosis (Fisher or Pearson) of a dataset.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1221
22ed769665b6 Uploaded
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parents:
diff changeset
1222 Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1223
22ed769665b6 Uploaded
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parents:
diff changeset
1224 If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators
22ed769665b6 Uploaded
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parents:
diff changeset
1225
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1226 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1227
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1228 Computes the kurtosis for samples x .
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1229
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1230 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1231
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1232 kurtosis : array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1233
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1234 The kurtosis of values along an axis. If all values are equal, return -3 for Fisher’s definition and 0 for Pearson’s definition.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1235
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1236 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1237
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1238 kurtosis([4,417,8,3],0,true,true) the result is (-0.666961688151)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1239
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1240 =============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1241 Kurtosis Test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1242 =============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1243
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1244 Tests whether a dataset has normal kurtosis
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1245
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1246 This function tests the null hypothesis that the kurtosis of the population from which the sample was drawn is that of the normal distribution: kurtosis = 3(n-1)/(n+1).
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1247
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1248 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1249
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1250 Computes the Z-value and p-value about samples x.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1251
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1252 kurtosistest only valid for n>=20.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1253
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1254 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1255
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1256 z-score : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1257
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1258 The computed z-score for this test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1259
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1260 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1261
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1262 The 2-sided p-value for the hypothesis test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1263
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1264
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1265 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1266
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1267 kurtosistest([4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3]) the result is (0.29775013081425117, 0.7658938788569033)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1268
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1269 ====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1270 Mode
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1271 ====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1272
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1273 Returns an array of the modal value in the passed array.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1274
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1275 If there is more than one such value, only the first is returned. The bin-count for the modal bins is also returned.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1276
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1277 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1278
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1279 Computes the most common value for samples x .
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1280
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1281 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1282
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1283 vals : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1284
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1285 Array of modal values.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1286
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1287 counts : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1288
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1289 Array of counts for each mode.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1290
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1291
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1292 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1293
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1294 mode([4,417,8,3]) the result is ([ 3.], [ 1.])
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1295
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1296 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1297 Moment
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1298 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1299
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1300 Calculates the nth moment about the mean for a sample.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1301
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1302 Generally used to calculate coefficients of skewness and kurtosis.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1303
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1304 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1305
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1306 Computes the nth moment about the mean for samples x .
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1307
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1308 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1309
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1310 n-th central moment : ndarray or float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1311
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1312 The appropriate moment along the given axis or over all values if axis is None. The denominator for the moment calculation is the number of observations, no degrees of freedom correction is done.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1313
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1314
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1315 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1316
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1317 mode([4,417,8,3],moment=2) the result is (31830.5)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1318
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1319
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1320 ===========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1321 Normal Test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1322 ===========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1323
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1324 Tests whether a sample differs from a normal distribution.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1325
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1326 This function tests the null hypothesis that a sample comes from a normal distribution. It is based on D’Agostino and Pearson’s test that combines skew and kurtosis to produce an omnibus test of normality.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1327
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1328 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1329
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1330 Computes the k2 and p-value for samples x.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1331
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1332 skewtest is not valid with less than 8 samples.kurtosistest only valid for n>=20.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1333
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1334 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1335
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1336 k2 : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1337
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1338 s^2 + k^2, where s is the z-score returned by skewtest and k is the z-score returned by kurtosistest.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1339
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1340 p-value : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1341
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1342 A 2-sided chi squared probability for the hypothesis test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1343
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1344
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1345 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1346
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1347 normaltest([4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3]) the result is (5.8877986151838, 0.052659990380181286)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1348
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1349 ====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1350 Skew
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1351 ====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1352
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1353 Computes the skewness of a data set.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1354
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1355 For normally distributed data, the skewness should be about 0. A skewness value > 0 means that there is more weight in the left tail of the distribution. The function skewtest can be used to determine if the skewness value is close enough to 0, statistically speaking.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1356
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1357 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1358
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1359 Computes the skewness from samples x.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1360
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1361
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1362 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1363
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1364 skewness : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1365
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1366 The skewness of values along an axis, returning 0 where all values are equal.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1367
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1368
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1369 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1370
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1371 kurtosistest([4,417,8,3]) the result is (1.1543204427775307)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1372
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1373
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1374 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1375 Skew Test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1376 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1377
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1378 Tests whether the skew is different from the normal distribution.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1379
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1380 This function tests the null hypothesis that the skewness of the population that the sample was drawn from is the same as that of a corresponding normal distribution.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1381
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1382 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1383
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1384 Computes the z-value and p-value from samples x.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1385
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1386 skewtest is not valid with less than 8 samples
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1387
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1388 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1389
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1390 z-score : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1391
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1392 The computed z-score for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1393
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1394 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1395
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1396 a 2-sided p-value for the hypothesis test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1397
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1398 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1399
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1400 skewtest([4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3,4,17,8,3,30,45,5,3]) the result is (2.40814108282,0.0160339834731)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1401
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1402 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1403 tmean
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1404 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1405
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1406 Compute the trimmed mean.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1407
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1408 This function finds the arithmetic mean of given values, ignoring values outside the given limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1409
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1410 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1411
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1412 Computes the mean of samples x,considering the lower and higher limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1413
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1414 Values in the input array less than the lower limit or greater than the upper limit will be ignored
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1415
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1416 for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1417
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1418 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1419
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1420 tmean : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1421
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1422 The computed mean for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1423
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1424
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1425 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1426
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1427 tmean([4,17,8,3],(0,20),(true,true)) the result is (8.0)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1428
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1429 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1430 tvar
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1431 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1432
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1433 Compute the trimmed variance
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1434
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1435 This function computes the sample variance of an array of values, while ignoring values which are outside of given limits
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1436
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1437 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1438
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1439 Computes the variance of samples x,considering the lower and higher limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1440
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1441 Values in the input array less than the lower limit or greater than the upper limit will be ignored
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1442
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1443 for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1444
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1445 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1446
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1447 tvar : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1448
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1449 The computed variance for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1450
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1451
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1452 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1453
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1454 tvar([4,17,8,3],(0,99999),(true,true)) the result is (40.6666666667)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1455
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1456 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1457 tmin
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1458 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1459
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1460 Compute the trimmed minimum.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1461
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1462 This function finds the arithmetic minimum of given values, ignoring values outside the given limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1463
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1464 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1465
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1466 Compute the trimmed minimum
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1467
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1468 This function finds the miminum value of an array a along the specified axis, but only considering values greater than a specified lower limit.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1469
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1470 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1471
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1472 tmin : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1473
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1474 The computed min for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1475
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1476
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1477 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1478
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1479 stats.tmin([4,17,8,3],2,0,'true') the result is (3.0)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1480
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1481 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1482 tmax
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1483 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1484
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1485 Compute the trimmed maximum.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1486
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1487 This function finds the arithmetic maximum of given values, ignoring values outside the given limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1488
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1489 This function computes the maximum value of an array along a given axis, while ignoring values larger than a specified upper limit.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1490
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1491 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1492
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1493 tmax : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1494
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1495 The computed max for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1496
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1497
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1498 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1499
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1500 stats.tmax([4,17,8,3],50,0,'true') the result is (17.0)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1501
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1502 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1503 tstd
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1504 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1505
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1506 Compute the trimmed sample standard deviation
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1507
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1508 This function finds the sample standard deviation of given values, ignoring values outside the given limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1509
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1510 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1511
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1512 Computes the deviation of samples x,considering the lower and higher limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1513
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1514 Values in the input array less than the lower limit or greater than the upper limit will be ignored
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1515
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1516 for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1517
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1518 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1519
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1520 tstd : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1521
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1522 The computed deviation for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1523
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1524
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1525 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1526
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1527 tstd([4,17,8,3],(0,99999),(true,true)) the result is (6.37704215657)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1528
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1529
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1530 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1531 tsem
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1532 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1533
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1534 Compute the trimmed standard error of the mean.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1535
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1536 This function finds the standard error of the mean for given values, ignoring values outside the given limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1537
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1538 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1539
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1540 Computes the standard error of mean for samples x,considering the lower and higher limits.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1541
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1542 Values in the input array less than the lower limit or greater than the upper limit will be ignored
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1543
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1544 for inclusive,These flags determine whether values exactly equal to the lower or upper limits are included. The default value is (True, True)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1545
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1546 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1547
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1548 tsem : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1549
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1550 The computed the standard error of mean for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1551
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1552
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1553 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1554
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1555 tsem([4,17,8,3],(0,99999),(true,true)) the result is (3.18852107828)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1556
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1557 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1558 nanmean
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1559 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1560
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1561 Compute the mean over the given axis ignoring nans
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1562
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1563 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1564
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1565 Computes the mean for samples x without considering nans
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1566
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1567 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1568
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1569 m : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1570
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1571 The computed the mean for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1572
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1573
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1574 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1575
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1576 tsem([4,17,8,3]) the result is (8.0)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1577
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1578 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1579 nanstd
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1580 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1581
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1582 Compute the standard deviation over the given axis, ignoring nans.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1583
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1584 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1585
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1586 Computes the deviation for samples x without considering nans
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1587
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1588 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1589
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1590 s : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1591
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1592 The computed the standard deviation for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1593
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1594
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1595 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1596
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1597 nanstd([4,17,8,3],0,'false') the result is (5.52268050859)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1598
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1599
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1600 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1601 nanmedian
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1602 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1603
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1604 Computes the median for samples x without considering nans
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1605
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1606 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1607
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1608 m : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1609
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1610 The computed the median for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1611
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1612
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1613 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1614
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1615 nanmedian([4,17,8,3]) the result is (6.0)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1616
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1617
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1618 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1619 variation
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1620 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1621
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1622 Computes the coefficient of variation, the ratio of the biased standard deviation to the mean for samples x
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1623
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1624 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1625
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1626 ratio: float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1627
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1628 The ratio of the biased standard deviation to the mean for this test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1629
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1630
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1631 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1632
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1633 variation([4,17,8,3]) the result is (0.690335063574)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1634
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1635 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1636 cumfreq
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1637 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1638
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1639 Returns a cumulative frequency histogram, using the histogram function.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1640
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1641 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1642
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1643 cumfreq : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1644
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1645 Binned values of cumulative frequency.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1646
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1647 lowerreallimit : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1648
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1649 Lower real limit
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1650
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1651 binsize : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1652
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1653 Width of each bin.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1654
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1655 extrapoints : int
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1656
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1657 Extra points.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1658
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1659
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1660 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1661
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1662 cumfreq([4,17,8,3],defaultreallimits=(2.0,3.5)) the result is ([ 0. 0. 0. 0. 0. 0. 1. 1. 1. 1.],2.0,0.15,3)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1663
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1664 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1665 histogram2
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1666 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1667
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1668 Compute histogram using divisions in bins.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1669
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1670 Count the number of times values from array a fall into numerical ranges defined by bins.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1671
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1672 samples should at least have two numbers.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1673
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1674 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1675
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1676 histogram2 : ndarray of rank 1
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1677
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1678 Each value represents the occurrences for a given bin (range) of values.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1679
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1680
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1681 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1682
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1683 stats.histogram2([4,17,8,3], [30,45,5,3]) the result is (array([ 0, -2, -2, 4]))
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1684
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1685 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1686 histogram
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1687 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1688
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1689 Separates the range into several bins and returns the number of instances in each bin
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1690
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1691 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1692
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1693 histogram : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1694
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1695 Number of points (or sum of weights) in each bin.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1696
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1697 low_range : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1698
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1699 Lowest value of histogram, the lower limit of the first bin.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1700
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1701 binsize : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1702
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1703 The size of the bins (all bins have the same size).
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1704
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1705 extrapoints : int
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1706
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1707 The number of points outside the range of the histogram.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1708
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1709
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1710 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1711
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1712 histogram([4,17,8,3],defaultlimits=(2.0,3.4)) the result is ([ 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.],2.0,0.14,3)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1713
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1714
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1715 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1716 itemfreq
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1717 ============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1718
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1719 Computes the frequencies for numbers
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1720
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1721 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1722
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1723 temfreq : (K, 2) ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1724 A 2-D frequency table. Column 1 contains sorted, unique values from a, column 2 contains their respective counts.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1725
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1726
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1727 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1728
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1729 variation([4,17,8,3]) the result is array([[ 3, 1], [ 4, 1],[ 8, 1],[17, 1]])
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1730
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1731 ===
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1732 Sem
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1733 ===
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1734
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1735 Calculates the standard error of the mean (or standard error of measurement) of the values in the input array.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1736
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1737
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1738 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1739
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1740 s : ndarray or float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1741 The standard error of the mean in the sample(s), along the input axis.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1742
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1743
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1744 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1745
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1746 variation([4,17,8,3],ddof=1) the result is(3.18852107828)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1747
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1748 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1749 Z Map
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1750 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1751
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1752 Calculates the relative z-scores.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1753
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1754 Returns an array of z-scores, i.e., scores that are standardized to zero mean and unit variance, where mean and variance are calculated from the comparison array.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1755
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1756
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1757 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1758
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1759 zscore : array_like
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1760
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1761 Z-scores, in the same shape as scores.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1762
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1763 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1764
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1765 stats.zmap([4,17,8,3],[30,45,5,3],ddof=1)the result is[-0.82496302 -0.18469321 -0.62795692 -0.87421454]
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1766
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1767 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1768 Z Score
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1769 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1770
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1771 Calculates the z score of each value in the sample, relative to the sample mean and standard deviation
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1772
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1773
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1774 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1775
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1776 zscore : array_like
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1777 The z-scores, standardized by mean and standard deviation of input array a.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1778
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1779
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1780 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1781
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1782 variation([4,17,8,3],ddof=0) the result is ([-0.72428597 1.62964343 0. -0.90535746])
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1783
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1784 ===============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1785 Signal to noise
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1786 ===============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1787
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1788 The signal-to-noise ratio of the input data.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1789
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1790 Returns the signal-to-noise ratio of a, here defined as the mean divided by the standard deviation.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1791
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1792
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1793 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1794
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1795 s2n : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1796 The mean to standard deviation ratio(s) along axis, or 0 where the standard deviation is 0.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1797
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1798
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1799 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1800
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1801 variation([4,17,8,3],ddof=0) the result is (1.44857193668)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1802
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1803 ===================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1804 Percentile of score
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1805 ===================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1806
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1807 The percentile rank of a score relative to a list of scores.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1808
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1809 A percentileofscore of, for example, 80% means that 80% of the scores in a are below the given score. In the case of gaps or ties, the exact definition depends on the optional keyword, kind.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1810
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1811 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1812
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1813 pcos : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1814 Percentile-position of score (0-100) relative to a.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1815
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1816
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1817 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1818
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1819 percentileofscore([4,17,8,3],score=3,kind='rank') the result is(25.0)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1820
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1821 ===================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1822 Score at percentile
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1823 ===================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1824
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1825 Calculate the score at a given percentile of the input sequence.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1826
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1827 For example, the score at per=50 is the median. If the desired quantile lies between two data points, we interpolate between them, according to the value of interpolation. If the parameter limit is provided, it should be a tuple (lower, upper) of two values.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1828
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1829 The second simple should be in range [0,100].
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1830
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1831 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1832
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1833 score : float or ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1834 Score at percentile(s).
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1835
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1836
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1837 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1838
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1839 stats.scoreatpercentile([4,17,8,3],[8,3],(0,100),'fraction') the result is array([ 3.24, 3.09])
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1840
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1841 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1842 relfreq
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1843 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1844
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1845 Returns a relative frequency histogram, using the histogram function
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1846
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1847 numbins are the number of bins to use for the histogram.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1848
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1849 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1850
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1851 relfreq : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1852
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1853 Binned values of relative frequency.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1854
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1855 lowerreallimit : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1856
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1857 Lower real limit
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1858
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1859 binsize : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1860
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1861 Width of each bin.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1862
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1863 extrapoints : int
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1864
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1865 Extra points.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
1866
22ed769665b6 Uploaded
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parents:
diff changeset
1867
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parents:
diff changeset
1868 **example**:
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parents:
diff changeset
1869
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parents:
diff changeset
1870 stats.relfreq([4,17,8,3],10,(0,100)) the result is (array([ 0.75, 0.25, 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 , 0.0 ]), 0, 10.0, 0)
22ed769665b6 Uploaded
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parents:
diff changeset
1871
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parents:
diff changeset
1872 ================
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parents:
diff changeset
1873 Binned statistic
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parents:
diff changeset
1874 ================
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parents:
diff changeset
1875
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parents:
diff changeset
1876 Compute a binned statistic for a set of data.
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parents:
diff changeset
1877
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parents:
diff changeset
1878 This is a generalization of a histogram function. A histogram divides the space into bins, and returns the count of the number of points in each bin. This function allows the computation of the sum, mean, median, or other statistic of the values within each bin.
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parents:
diff changeset
1879
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parents:
diff changeset
1880 Y must be the same shape as X
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parents:
diff changeset
1881
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parents:
diff changeset
1882 **The output are:**
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parents:
diff changeset
1883
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parents:
diff changeset
1884 statistic : array
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parents:
diff changeset
1885
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parents:
diff changeset
1886 The values of the selected statistic in each bin.
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parents:
diff changeset
1887
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parents:
diff changeset
1888 bin_edges : array of dtype float
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parents:
diff changeset
1889
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parents:
diff changeset
1890 Return the bin edges (length(statistic)+1).
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parents:
diff changeset
1891
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parents:
diff changeset
1892 binnumber : 1-D ndarray of ints
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parents:
diff changeset
1893
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parents:
diff changeset
1894 This assigns to each observation an integer that represents the bin in which this observation falls. Array has the same length as values.
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parents:
diff changeset
1895
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parents:
diff changeset
1896
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parents:
diff changeset
1897 **example**:
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parents:
diff changeset
1898
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parents:
diff changeset
1899 stats.binned_statistic([4,17,8,3],[30,45,5,3],'sum',10,(0,100)) the result is ([ 38. 45. 0. 0. 0. 0. 0. 0. 0. 0.],[ 0. 10. 20. 30. 40. 50. 60. 70. 80. 90. 100.],[1 2 1 1])
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parents:
diff changeset
1900
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parents:
diff changeset
1901 ================
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parents:
diff changeset
1902 obrientransform
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parents:
diff changeset
1903 ================
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parents:
diff changeset
1904
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parents:
diff changeset
1905 Computes the O’Brien transform on input data (any number of arrays).
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parents:
diff changeset
1906
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parents:
diff changeset
1907 Used to test for homogeneity of variance prior to running one-way stats.
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parents:
diff changeset
1908
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parents:
diff changeset
1909 It has to have at least two samples.
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parents:
diff changeset
1910
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parents:
diff changeset
1911 **The output are:**
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parents:
diff changeset
1912
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parents:
diff changeset
1913 obrientransform : ndarray
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parents:
diff changeset
1914
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parents:
diff changeset
1915 Transformed data for use in an ANOVA. The first dimension of the result corresponds to the sequence of transformed arrays. If the arrays given are all 1-D of the same length, the return value is a 2-D array; otherwise it is a 1-D array of type object, with each element being an ndarray.
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parents:
diff changeset
1916
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parents:
diff changeset
1917
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parents:
diff changeset
1918 **example**:
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parents:
diff changeset
1919
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parents:
diff changeset
1920 stats.obrientransformcenter([4,17,8,3], [30,45,5,3]) the result is (array([[ 16.5 , 124.83333333, -10.16666667, 31.5 ],[ 39.54166667, 877.04166667, 310.375 , 422.04166667]]))
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parents:
diff changeset
1921
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parents:
diff changeset
1922 =========
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parents:
diff changeset
1923 bayes mvs
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parents:
diff changeset
1924 =========
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parents:
diff changeset
1925
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parents:
diff changeset
1926 Bayesian confidence intervals for the mean, var, and std.alpha should be larger than 0,smaller than 1.
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parents:
diff changeset
1927
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parents:
diff changeset
1928
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parents:
diff changeset
1929 **The output are:**
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parents:
diff changeset
1930
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parents:
diff changeset
1931 mean_cntr, var_cntr, std_cntr : tuple
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parents:
diff changeset
1932
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parents:
diff changeset
1933 The three results are for the mean, variance and standard deviation, respectively. Each result is a tuple of the form:
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parents:
diff changeset
1934
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parents:
diff changeset
1935 (center, (lower, upper))
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parents:
diff changeset
1936
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parents:
diff changeset
1937 with center the mean of the conditional pdf of the value given the data, and (lower, upper) a confidence interval, centered on the median, containing the estimate to a probability alpha.
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parents:
diff changeset
1938
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parents:
diff changeset
1939 **example**:
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parents:
diff changeset
1940
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parents:
diff changeset
1941 stats.bayes_mvs([4,17,8,3],0.8) the result is (8.0, (0.49625108326958145, 15.503748916730416));(122.0, (15.611548029617781, 346.74229584218108));(8.8129230241075476, (3.9511451542075475, 18.621017583423871))
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parents:
diff changeset
1942
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parents:
diff changeset
1943 =========
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parents:
diff changeset
1944 sigmaclip
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parents:
diff changeset
1945 =========
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parents:
diff changeset
1946
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parents:
diff changeset
1947 Iterative sigma-clipping of array elements.
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parents:
diff changeset
1948
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parents:
diff changeset
1949 The output array contains only those elements of the input array c that satisfy the conditions
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parents:
diff changeset
1950
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parents:
diff changeset
1951 **The output are:**
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parents:
diff changeset
1952
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parents:
diff changeset
1953 c : ndarray
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parents:
diff changeset
1954 Input array with clipped elements removed.
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parents:
diff changeset
1955 critlower : float
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parents:
diff changeset
1956 Lower threshold value use for clipping.
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parents:
diff changeset
1957 critlupper : float
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parents:
diff changeset
1958 Upper threshold value use for clipping.
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parents:
diff changeset
1959
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parents:
diff changeset
1960
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parents:
diff changeset
1961 **example**:
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parents:
diff changeset
1962
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parents:
diff changeset
1963 sigmaclip([4,17,8,3]) the result is [ 4. 17. 8. 3.],-14.0907220344,30.0907220344)
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parents:
diff changeset
1964
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parents:
diff changeset
1965 =========
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parents:
diff changeset
1966 threshold
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parents:
diff changeset
1967 =========
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parents:
diff changeset
1968
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parents:
diff changeset
1969 Clip array to a given value.
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parents:
diff changeset
1970
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parents:
diff changeset
1971 Similar to numpy.clip(), except that values less than threshmin or greater than threshmax are replaced by newval, instead of by threshmin and threshmax respectively.
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parents:
diff changeset
1972
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parents:
diff changeset
1973
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parents:
diff changeset
1974 **The output are:**
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parents:
diff changeset
1975
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parents:
diff changeset
1976 out : ndarray
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parents:
diff changeset
1977 The clipped input array, with values less than threshmin or greater than threshmax replaced with newval.
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parents:
diff changeset
1978
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parents:
diff changeset
1979 **example**:
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parents:
diff changeset
1980
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parents:
diff changeset
1981 stats.threshold([4,17,8,3],2,8,0)the result is array([4, 17, 8, 3])
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parents:
diff changeset
1982
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parents:
diff changeset
1983 ========
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parents:
diff changeset
1984 trimboth
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parents:
diff changeset
1985 ========
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parents:
diff changeset
1986
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parents:
diff changeset
1987 Slices off a proportion of items from both ends of an array.
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parents:
diff changeset
1988
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parents:
diff changeset
1989 Slices off the passed proportion of items from both ends of the passed array (i.e., with proportiontocut = 0.1, slices leftmost 10% and rightmost 10% of scores). You must pre-sort the array if you want ‘proper’ trimming. Slices off less if proportion results in a non-integer slice index (i.e., conservatively slices off proportiontocut).
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parents:
diff changeset
1990
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parents:
diff changeset
1991
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parents:
diff changeset
1992 **The output are:**
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parents:
diff changeset
1993
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parents:
diff changeset
1994 out : ndarray
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parents:
diff changeset
1995 Trimmed version of array a.
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parents:
diff changeset
1996
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parents:
diff changeset
1997 **example**:
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parents:
diff changeset
1998
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parents:
diff changeset
1999 stats.trimboth([4,17,8,3],0.1)the result is array([ 4, 17, 8, 3])
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parents:
diff changeset
2000
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parents:
diff changeset
2001 =====
22ed769665b6 Uploaded
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parents:
diff changeset
2002 trim1
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parents:
diff changeset
2003 =====
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parents:
diff changeset
2004
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parents:
diff changeset
2005 Slices off a proportion of items from ONE end of the passed array distribution.
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parents:
diff changeset
2006
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parents:
diff changeset
2007 If proportiontocut = 0.1, slices off ‘leftmost’ or ‘rightmost’ 10% of scores. Slices off LESS if proportion results in a non-integer slice index (i.e., conservatively slices off proportiontocut ).
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parents:
diff changeset
2008
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2009 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2010
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parents:
diff changeset
2011 trim1 : ndarray
22ed769665b6 Uploaded
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parents:
diff changeset
2012
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bgruening
parents:
diff changeset
2013 Trimmed version of array a
22ed769665b6 Uploaded
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parents:
diff changeset
2014
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2015 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2016
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parents:
diff changeset
2017 stats.trim1([4,17,8,3],0.5,'left')the result is array([8, 3])
22ed769665b6 Uploaded
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parents:
diff changeset
2018
22ed769665b6 Uploaded
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parents:
diff changeset
2019 =========
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parents:
diff changeset
2020 spearmanr
22ed769665b6 Uploaded
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parents:
diff changeset
2021 =========
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parents:
diff changeset
2022
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parents:
diff changeset
2023 Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation.
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parents:
diff changeset
2024
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parents:
diff changeset
2025 The Spearman correlation is a nonparametric measure of the monotonicity of the relationship between two datasets. Unlike the Pearson correlation, the Spearman correlation does not assume that both datasets are normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact monotonic relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
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parents:
diff changeset
2026
22ed769665b6 Uploaded
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parents:
diff changeset
2027 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2028
22ed769665b6 Uploaded
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parents:
diff changeset
2029 rho : float or ndarray (2-D square)
22ed769665b6 Uploaded
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parents:
diff changeset
2030
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parents:
diff changeset
2031 Spearman correlation matrix or correlation coefficient (if only 2 variables are given as parameters. Correlation matrix is square with length equal to total number of variables (columns or rows) in a and b combined.
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parents:
diff changeset
2032
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parents:
diff changeset
2033 p-value : float
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parents:
diff changeset
2034
22ed769665b6 Uploaded
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parents:
diff changeset
2035 The two-sided p-value for a hypothesis test whose null hypothesis is that two sets of data are uncorrelated, has same dimension as rho.
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parents:
diff changeset
2036
22ed769665b6 Uploaded
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parents:
diff changeset
2037 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2038
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parents:
diff changeset
2039 stats.spearmanr([4,17,8,3,30,45,5,3],[5,3,4,17,8,3,30,45])the result is (-0.722891566265, 0.0427539458876)
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parents:
diff changeset
2040
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parents:
diff changeset
2041 ========
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parents:
diff changeset
2042 f oneway
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parents:
diff changeset
2043 ========
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parents:
diff changeset
2044
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parents:
diff changeset
2045 Performs a 1-way ANOVA.
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parents:
diff changeset
2046
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parents:
diff changeset
2047 The one-way ANOVA tests the null hypothesis that two or more groups have the same population mean. The test is applied to samples from two or more groups, possibly with differing sizes.
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bgruening
parents:
diff changeset
2048
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2049 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2050
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2051 F-value : float
22ed769665b6 Uploaded
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parents:
diff changeset
2052
22ed769665b6 Uploaded
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parents:
diff changeset
2053 The computed F-value of the test.
22ed769665b6 Uploaded
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parents:
diff changeset
2054
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2055 p-value : float
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parents:
diff changeset
2056
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parents:
diff changeset
2057 The associated p-value from the F-distribution.
22ed769665b6 Uploaded
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parents:
diff changeset
2058
22ed769665b6 Uploaded
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parents:
diff changeset
2059 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2060
22ed769665b6 Uploaded
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parents:
diff changeset
2061 stats. f_oneway([4,17,8,3], [30,45,5,3]) the result is (1.43569457222,0.276015080537)
22ed769665b6 Uploaded
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parents:
diff changeset
2062
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bgruening
parents:
diff changeset
2063 =================
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parents:
diff changeset
2064 Mann-Whitney rank
22ed769665b6 Uploaded
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parents:
diff changeset
2065 =================
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parents:
diff changeset
2066
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parents:
diff changeset
2067 Compute the Wilcoxon rank-sum statistic for two samples.
22ed769665b6 Uploaded
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parents:
diff changeset
2068
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parents:
diff changeset
2069 The Wilcoxon rank-sum test tests the null hypothesis that two sets of measurements are drawn from the same distribution. The alternative hypothesis is that values in one sample are more likely to be larger than the values in the other sample.
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parents:
diff changeset
2070
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parents:
diff changeset
2071 This test should be used to compare two samples from continuous distributions. It does not handle ties between measurements in x and y. For tie-handling and an optional continuity correction use mannwhitneyu.
22ed769665b6 Uploaded
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parents:
diff changeset
2072
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2073 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2074
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2075 Computes the Mann-Whitney rank test on samples x and y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2076
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2077 u : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2078
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2079 The Mann-Whitney statistics.
22ed769665b6 Uploaded
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parents:
diff changeset
2080
22ed769665b6 Uploaded
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parents:
diff changeset
2081 prob : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2082
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2083 One-sided p-value assuming a asymptotic normal distribution.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2084
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2085 ===================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2086 Ansari-Bradley test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2087 ===================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2088
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2089 Perform the Ansari-Bradley test for equal scale parameters
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2090
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2091 The Ansari-Bradley test is a non-parametric test for the equality of the scale parameter of the distributions from which two samples were drawn.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2092
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2093 The p-value given is exact when the sample sizes are both less than 55 and there are no ties, otherwise a normal approximation for the p-value is used.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2094
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2095 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2096
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2097 Computes the Ansari-Bradley test for samples x and y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2098
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2099 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2100
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2101 AB : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2102
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2103 The Ansari-Bradley test statistic
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2104
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2105 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2106
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2107 The p-value of the hypothesis test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2108
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2109 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2110
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2111 ansari([1,2,3,4],[15,5,20,8,10,12]) the result is (10.0, 0.53333333333333333)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2112
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2113 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2114 bartlett
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2115 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2116
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2117 Perform Bartlett’s test for equal variances
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2118
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2119 Bartlett’s test tests the null hypothesis that all input samples are from populations with equal variances.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2120
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2121 It has to have at least two samples.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2122
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2123 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2124
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2125 T : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2126
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2127 The test statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2128
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2129 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2130
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2131 The p-value of the test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2132
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2133
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2134 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2135
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2136 stats.bartlett([4,17,8,3], [30,45,5,3]) the result is (2.87507113948,0.0899609995242)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2137
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2138 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2139 levene
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2140 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2141
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2142 Perform Levene test for equal variances.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2143
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2144 The Levene test tests the null hypothesis that all input samples are from populations with equal variances.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2145
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2146 It has to have at least two samples.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2147
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2148 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2149
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2150 W : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2151
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2152 The test statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2153
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2154 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2155
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2156 The p-value for the test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2157
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2158
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2159 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2160
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2161 stats.levene(center='mean',proportiontocut=0.01,[4,17,8,3], [30,45,5,3]) the result is (11.5803858521,0.014442549362)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2162
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2163 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2164 fligner
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2165 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2166
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2167 Perform Fligner’s test for equal variances.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2168
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2169 Fligner’s test tests the null hypothesis that all input samples are from populations with equal variances. Fligner’s test is non-parametric in contrast to Bartlett’s test bartlett and Levene’s test levene.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2170
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2171 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2172
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2173 Xsq : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2174
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2175 The test statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2176
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2177 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2178
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2179 The p-value for the hypothesis test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2180
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2181
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2182 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2183 linregress
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2184 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2185
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2186 Calculate a regression line
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2187
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2188 This computes a least-squares regression for two sets of measurements.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2189
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2190 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2191
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2192 Computes the least-squares regression for samples x and y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2193
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2194 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2195
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2196 slope : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2197
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2198 slope of the regression line
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2199
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2200 intercept : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2201
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2202 intercept of the regression line
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2203
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2204 r-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2205
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2206 correlation coefficient
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2207
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2208 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2209
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2210 two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2211
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2212 stderr : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2213
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2214 Standard error of the estimate
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2215
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2216 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2217
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2218 linregress([4,417,8,3],[30,45,5,3]) the result is (0.0783053989099, 12.2930169177, 0.794515680443,0.205484319557,0.0423191764713)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2219
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2220 ===========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2221 ttest 1samp
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2222 ===========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2223
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2224 Calculates the T-test for the mean of ONE group of scores.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2225
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2226 This is a two-sided test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given population mean, popmean.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2227
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2228 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2229
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2230 t : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2231
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2232 The calculated t-statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2233
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2234 prob : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2235
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2236 The two-tailed p-value.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2237
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2238 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2239
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2240 stats.ttest_1samp([4,17,8,3],[30,45,5,3])the result is (array([ -6.89975053, -11.60412589, 0.94087507, 1.56812512]), array([ 0.00623831, 0.00137449, 0.41617971, 0.21485306]))
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2241
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2242 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2243 ttest ind
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2244 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2245
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2246 Calculates the T-test for the means of TWO INDEPENDENT samples of scores.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2247
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2248 This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes that the populations have identical variances.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2249
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2250 The independent samples t-test is used when two separate sets of independent and identically distributed samples are obtained, one from each of the two populations
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2251 being compared.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2252 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2253 Computes the T-test for the means of independent samples x and y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2254
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2255 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2256
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2257 t : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2258
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2259 The calculated t-statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2260
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2261 prob : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2262
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2263 The two-tailed p-value.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2264
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2265 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2266
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2267 ttest_ind([4,417,8,3],[30,45,5,3]) the result is (0.842956644207,0.431566932748)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2268
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2269 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2270 ttest rel
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2271 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2272
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2273 Calculates the T-test on TWO RELATED samples of scores, a and b.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2274
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2275 This is a two-sided test for the null hypothesis that 2 related or repeated samples have identical average (expected) values.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2276
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2277 related samples t-tests typically consist of a sample of matched pairs of similar units, or one group of units that has been tested twice (a "repeated measures" t-test)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2278
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2279 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2280
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2281 Computes the T-test for the means of related samples x and y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2282
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2283 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2284
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2285 t : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2286
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2287 t-statistic
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2288
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2289 prob : float or array
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2290
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2291 two-tailed p-value
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2292
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2293 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2294
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2295 ttest_rel([4,417,8,3],[30,45,5,3]) the result is (0.917072474241,0.426732624361)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2296
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2297 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2298 chisquare
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2299 =========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2300
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2301 Calculates a one-way chi square test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2302
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2303 The chi square test tests the null hypothesis that the categorical data has the given frequencies.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2304
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2305 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2306
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2307 chisq : float or ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2308
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2309 The chi-squared test statistic. The value is a float if axis is None or f_obs and f_exp are 1-D.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2310
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2311 p : float or ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2312
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2313 The p-value of the test. The value is a float if ddof and the return value chisq are scalars.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2314
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2315 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2316
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2317 stats.chisquare([4,17,8,3],[30,45,5,3],ddof=1)the result is (41.7555555556,8.5683326078e-10)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2318
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2319 ================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2320 power divergence
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2321 ================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2322
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2323 Cressie-Read power divergence statistic and goodness of fit test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2324
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2325 This function tests the null hypothesis that the categorical data has the given frequencies, using the Cressie-Read power divergence statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2326
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2327 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2328
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2329 stat : float or ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2330
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2331 The Cressie-Read power divergence test statistic. The value is a float if axis is None or if` f_obs and f_exp are 1-D.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2332
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2333 p : float or ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2334
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2335 The p-value of the test. The value is a float if ddof and the return value stat are scalars.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2336
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2337 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2338
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2339 stats.power_divergence([4,17,8,3],[30,45,5,3],1,lambda=1)the result is (41.7555555556, 8.5683326078e-10)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2340
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2341 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2342 tiecorrect
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2343 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2344
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2345 Tie correction factor for ties in the Mann-Whitney U and Kruskal-Wallis H tests.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2346
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2347 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2348
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2349 factor : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2350
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2351 Correction factor for U or H.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2352
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2353 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2354
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2355 stats.tiecorrect([4,17,8,3,30,45,5,3])the result is (0.988095238095)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2356
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2357 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2358 rankdata
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2359 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2360
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2361 Assign ranks to data, dealing with ties appropriately.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2362
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2363 Ranks begin at 1. The method argument controls how ranks are assigned to equal values. See [R308] for further discussion of ranking methods.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2364
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2365 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2366
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2367 ranks : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2368
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2369 An array of length equal to the size of a, containing rank scores.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2370
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2371 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2372
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2373 stats.rankdata([4,17,8,3],average)the result is ([ 2. 4. 3. 1.])
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2374
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2375 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2376 kruskal
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2377 =======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2378
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2379 Compute the Kruskal-Wallis H-test for independent samples
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2380
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2381 The Kruskal-Wallis H-test tests the null hypothesis that the population median of all of the groups are equal. It is a non-parametric version of ANOVA.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2382
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2383 The number of samples have to be more than one
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2384
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2385 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2386
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2387 H-statistic : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2388
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2389 The Kruskal-Wallis H statistic, corrected for ties
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2390
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2391 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2392
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2393 The p-value for the test using the assumption that H has a chi square distribution
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2394
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2395
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2396 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2397
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2398 stats. kruskal([4,17,8,3], [30,45,5,3]) the result is (0.527108433735,0.467825077285)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2399
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2400 ==================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2401 friedmanchisquare
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2402 ==================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2403
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2404 Computes the Friedman test for repeated measurements
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2405
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2406 The Friedman test tests the null hypothesis that repeated measurements of the same individuals have the same distribution. It is often used to test for consistency among measurements obtained in different ways.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2407
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2408 The number of samples have to be more than two.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2409
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2410 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2411
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2412 friedman chi-square statistic : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2413
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2414 the test statistic, correcting for ties
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2415
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2416 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2417
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2418 the associated p-value assuming that the test statistic has a chi squared distribution
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2419
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2420
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2421 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2422
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2423 stats.friedmanchisquare([4,17,8,3],[8,3,30,45],[30,45,5,3])the result is (0.933333333333,0.627089085273)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2424
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2425 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2426 mood
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2427 =====
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2428
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2429 Perform Mood’s test for equal scale parameters.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2430
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2431 Mood’s two-sample test for scale parameters is a non-parametric test for the null hypothesis that two samples are drawn from the same distribution with the same scale parameter.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2432
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2433 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2434
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2435 Computes the Mood’s test for equal scale samples x and y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2436
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2437 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2438
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2439 z : scalar or ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2440
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2441 The z-score for the hypothesis test. For 1-D inputs a scalar is returned;
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2442
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2443 p-value : scalar ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2444
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2445 The p-value for the hypothesis test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2446
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2447 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2448
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2449 mood([4,417,8,3],[30,45,5,3]) the result is (0.396928310068,0.691420327045)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2450
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2451 ===============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2452 combine_pvalues
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2453 ===============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2454
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2455 Methods for combining the p-values of independent tests bearing upon the same hypothesis.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2456
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2457
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2458 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2459
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2460 statistic: float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2461
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2462 The statistic calculated by the specified method: - “fisher”: The chi-squared statistic - “stouffer”: The Z-score
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2463
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2464 pval: float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2465
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2466 The combined p-value.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2467
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2468 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2469
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2470 stats.combine_pvalues([4,17,8,3],method='fisher',weights=[5,6,7,8]) the result is (-14.795123071,1.0)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2471
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2472 ===========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2473 median test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2474 ===========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2475
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2476 Mood’s median test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2477
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2478 Test that two or more samples come from populations with the same median.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2479
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2480 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2481
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2482 stat : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2483
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2484 The test statistic. The statistic that is returned is determined by lambda. The default is Pearson’s chi-squared statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2485
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2486 p : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2487
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2488 The p-value of the test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2489
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2490 m : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2491
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2492 The grand median.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2493
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2494 table : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2495
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2496 The contingency table.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2497
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2498
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2499 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2500
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2501 stats.median_test(ties='below',correction=True ,lambda=1,*a)the result is ((0.0, 1.0, 6.5, array([[2, 2],[2, 2]])))
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2502
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2503 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2504 shapiro
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2505 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2506
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2507 Perform the Shapiro-Wilk test for normality.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2508
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2509 The Shapiro-Wilk test tests the null hypothesis that the data was drawn from a normal distribution.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2510
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2511 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2512
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2513 Computes the Shapiro-Wilk test for samples x and y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2514
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2515 If x has length n, then y must have length n/2.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2516
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2517 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2518
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2519 W : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2520
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2521 The test statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2522
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2523 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2524
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2525 The p-value for the hypothesis test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2526
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2527 a : array_like, optional
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2528
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2529 If reta is True, then these are the internally computed “a” values that may be passed into this function on future calls.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2530
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2531
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2532 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2533
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2534 shapiro([4,417,8,3],[45,5]) the result is (0.66630089283, 0.00436889193952, [45,5])
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2535
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2536 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2537 anderson
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2538 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2539
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2540 Anderson-Darling test for data coming from a particular distribution
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2541
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2542 The Anderson-Darling test is a modification of the Kolmogorov- Smirnov test kstest for the null hypothesis that a sample is drawn from a population that follows a particular distribution. For the Anderson-Darling test, the critical values depend on which distribution is being tested against. This function works for normal, exponential, logistic, or Gumbel (Extreme Value Type I) distributions.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2543
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2544 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2545
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2546 Computes the Anderson-Darling test for samples x which comes from a specific distribution..
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2547
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2548 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2549
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2550
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2551 A2 : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2552
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2553 The Anderson-Darling test statistic
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2554
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2555 critical : list
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2556
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2557 The critical values for this distribution
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2558
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2559 sig : list
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2560
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2561 The significance levels for the corresponding critical values in percents. The function returns critical values for a differing set of significance levels depending on the distribution that is being tested against.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2562
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2563 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2564
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2565 anderson([4,417,8,3],norm) the result is (0.806976419634,[ 1.317 1.499 1.799 2.098 2.496] ,[ 15. 10. 5. 2.5 1. ])
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2566
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2567 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2568 binom_test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2569 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2570
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2571 Perform a test that the probability of success is p.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2572
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2573 This is an exact, two-sided test of the null hypothesis that the probability of success in a Bernoulli experiment is p.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2574
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2575 he binomial test is an exact test of the statistical significance of deviations from a theoretically expected distribution of observations into two categories.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2576
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2577 -----
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2578
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2579 Computes the test for the probability of success is p .
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2580
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2581 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2582
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2583 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2584
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2585 The p-value of the hypothesis test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2586
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2587 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2588
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2589 binom_test([417,8],1,0.5) the result is (5.81382734132e-112)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2590
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2591 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2592 pearsonr
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2593 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2594
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2595 Calculates a Pearson correlation coefficient and the p-value for testing non-correlation.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2596
22ed769665b6 Uploaded
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parents:
diff changeset
2597 The Pearson correlation coefficient measures the linear relationship between two datasets.The value of the correlation (i.e., correlation coefficient) does not depend on the specific measurement units used.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2598
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2599 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2600
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2601 Pearson’s correlation coefficient: float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2602
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2603 2-tailed p-value: float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2604
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2605
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2606 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2607
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2608 pearsonr([4,17,8,3],[30,45,5,3]) the result is (0.695092958988,0.304907041012)
22ed769665b6 Uploaded
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parents:
diff changeset
2609
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2610 ========
22ed769665b6 Uploaded
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parents:
diff changeset
2611 wilcoxon
22ed769665b6 Uploaded
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parents:
diff changeset
2612 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2613
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2614 Calculate the Wilcoxon signed-rank test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2615
22ed769665b6 Uploaded
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parents:
diff changeset
2616 The Wilcoxon signed-rank test tests the null hypothesis that two related paired samples come from the same distribution. In particular, it tests whether the distribution of the differences x - y is symmetric about zero. It is a non-parametric version of the paired T-test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2617
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2618 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2619
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2620 T : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2621
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2622 The sum of the ranks of the differences above or below zero, whichever is smaller.
22ed769665b6 Uploaded
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parents:
diff changeset
2623
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2624 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2625
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2626 The two-sided p-value for the test.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2627
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2628
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2629 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2630
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2631 stats.wilcoxon([3,6,23,70,20,55,4,19,3,6],
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2632 [23,70,20,55,4,19,3,6,23,70],zero_method='pratt',correction=True) the result is (23.0, 0.68309139830960874)
22ed769665b6 Uploaded
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parents:
diff changeset
2633
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2634 ==============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2635 pointbiserialr
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2636 ==============
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2637
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2638 Calculates a Pearson correlation coefficient and the p-value for testing non-correlation.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2639
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2640 The Pearson correlation coefficient measures the linear relationship between two datasets.The value of the correlation (i.e., correlation coefficient) does not depend on the specific measurement units used.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2641 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2642
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2643 r : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2644
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2645 R value
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2646
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2647 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2648
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2649 2-tailed p-value
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2650
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2651
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2652 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2653
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2654 pointbiserialr([0,0,0,1,1,1,1],[1,0,1,2,3,4,5]) the result is (0.84162541153017323, 0.017570710081214368)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2655
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2656 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2657 ks_2samp
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2658 ========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2659
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2660 Computes the Kolmogorov-Smirnov statistic on 2 samples.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2661
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2662 This is a two-sided test for the null hypothesis that 2 independent samples are drawn from the same continuous distribution.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2663
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2664 If the K-S statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions of the two samples are the same.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2665
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2666 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2667
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2668 D : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2669
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2670 KS statistic
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2671
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2672 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2673
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2674 two-tailed p-value
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2675
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2676
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2677 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2678
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2679 ks_2samp([4,17,8,3],[30,45,5,3]) the result is (0.5,0.534415719217)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2680
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2681 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2682 kendalltau
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2683 ==========
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2684
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2685 Calculates Kendall’s tau, a correlation measure for sample x and sample y.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2686
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2687 sample x and sample y should be in the same size.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2688
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2689 Kendall’s tau is a measure of the correspondence between two rankings. Values close to 1 indicate strong agreement, values close to -1 indicate strong disagreement. This is the tau-b version of Kendall’s tau which accounts for ties.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2690
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2691
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2692 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2693
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2694 Kendall’s tau : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2695
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2696 The tau statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2697
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2698 p-value : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2699
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2700 The two-sided p-value for a hypothesis test whose null hypothesis is an absence of association, tau = 0.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2701
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2702
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2703 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2704
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2705 kendalltau([4,17,8,3],[30,45,5,3]),the result is (0.666666666667,0.174231399708)
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2706
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2707 ================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2708 chi2_contingency
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2709 ================
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2710
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2711 Chi-square test of independence of variables in a contingency table.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2712
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2713 This function computes the chi-square statistic and p-value for the hypothesis test of independence of the observed frequencies in the contingency table observed.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2714
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2715 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2716
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2717 chi2 : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2718
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2719 The test statistic.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2720
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2721 p : float
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2722
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2723 The p-value of the test
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2724
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2725 dof : int
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2726
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2727 Degrees of freedom
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2728
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2729 expected : ndarray, same shape as observed
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2730
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2731 The expected frequencies, based on the marginal sums of the table.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2732
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2733 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2734
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2735 stats.chi2_contingency([4,17,8,3],1)the result is (0.0, 1.0, 0, array([ 4., 17., 8., 3.]))
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2736
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2737 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2738 boxcox
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2739 ======
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2740
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2741 Return a positive dataset transformed by a Box-Cox power transformation
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2742
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2743 **The output are:**
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2744
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2745 boxcox : ndarray
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2746
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2747 Box-Cox power transformed array.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2748
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2749 maxlog : float, optional
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2750
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2751 If the lmbda parameter is None, the second returned argument is the lambda that maximizes the log-likelihood function.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2752
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2753 (min_ci, max_ci) : tuple of float, optional
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2754
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2755 If lmbda parameter is None and alpha is not None, this returned tuple of floats represents the minimum and maximum confidence limits given alpha.
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2756
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2757
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2758 **example**:
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2759
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2760 stats.boxcox([4,17,8,3],0.9) the result is ([ 1.03301717 1.60587825 1.35353026 0.8679017 ],-0.447422166194,(-0.5699221654511225, -0.3259515659400082))
22ed769665b6 Uploaded
bgruening
parents:
diff changeset
2761
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parents:
diff changeset
2762 ==============
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parents:
diff changeset
2763 boxcox normmax
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parents:
diff changeset
2764 ==============
22ed769665b6 Uploaded
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parents:
diff changeset
2765
22ed769665b6 Uploaded
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parents:
diff changeset
2766 Compute optimal Box-Cox transform parameter for input data
22ed769665b6 Uploaded
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parents:
diff changeset
2767
22ed769665b6 Uploaded
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parents:
diff changeset
2768 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2769
22ed769665b6 Uploaded
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parents:
diff changeset
2770 maxlog : float or ndarray
22ed769665b6 Uploaded
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parents:
diff changeset
2771
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parents:
diff changeset
2772 The optimal transform parameter found. An array instead of a scalar for method='all'.
22ed769665b6 Uploaded
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parents:
diff changeset
2773
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parents:
diff changeset
2774
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parents:
diff changeset
2775 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2776
22ed769665b6 Uploaded
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parents:
diff changeset
2777 stats.boxcox_normmax([4,17,8,3],(-2,2),'pearsonr')the result is (-0.702386238971)
22ed769665b6 Uploaded
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parents:
diff changeset
2778
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parents:
diff changeset
2779 ==========
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parents:
diff changeset
2780 boxcox llf
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parents:
diff changeset
2781 ==========
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parents:
diff changeset
2782
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parents:
diff changeset
2783 The boxcox log-likelihood function
22ed769665b6 Uploaded
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parents:
diff changeset
2784
22ed769665b6 Uploaded
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parents:
diff changeset
2785 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2786
22ed769665b6 Uploaded
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parents:
diff changeset
2787 llf : float or ndarray
22ed769665b6 Uploaded
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parents:
diff changeset
2788
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parents:
diff changeset
2789 Box-Cox log-likelihood of data given lmb. A float for 1-D data, an array otherwise.
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parents:
diff changeset
2790
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parents:
diff changeset
2791 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2792
22ed769665b6 Uploaded
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parents:
diff changeset
2793 stats.boxcox_llf(1,[4,17,8,3]) the result is (-6.83545336723)
22ed769665b6 Uploaded
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parents:
diff changeset
2794
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parents:
diff changeset
2795 =======
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parents:
diff changeset
2796 entropy
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parents:
diff changeset
2797 =======
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parents:
diff changeset
2798
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parents:
diff changeset
2799 Calculate the entropy of a distribution for given probability values.
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parents:
diff changeset
2800
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parents:
diff changeset
2801 If only probabilities pk are given, the entropy is calculated as S = -sum(pk * log(pk), axis=0).
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parents:
diff changeset
2802
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parents:
diff changeset
2803 If qk is not None, then compute the Kullback-Leibler divergence S = sum(pk * log(pk / qk), axis=0).
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parents:
diff changeset
2804
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parents:
diff changeset
2805 This routine will normalize pk and qk if they don’t sum to 1.
22ed769665b6 Uploaded
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parents:
diff changeset
2806
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parents:
diff changeset
2807 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2808
22ed769665b6 Uploaded
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parents:
diff changeset
2809 S : float
22ed769665b6 Uploaded
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parents:
diff changeset
2810
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parents:
diff changeset
2811 The calculated entropy.
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parents:
diff changeset
2812
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parents:
diff changeset
2813
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parents:
diff changeset
2814 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2815
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parents:
diff changeset
2816 stats.entropy([4,17,8,3],[30,45,5,3],1.6)the result is (0.641692653659)
22ed769665b6 Uploaded
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parents:
diff changeset
2817
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parents:
diff changeset
2818 ======
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parents:
diff changeset
2819 kstest
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parents:
diff changeset
2820 ======
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parents:
diff changeset
2821
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parents:
diff changeset
2822 Perform the Kolmogorov-Smirnov test for goodness of fit.
22ed769665b6 Uploaded
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parents:
diff changeset
2823
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parents:
diff changeset
2824 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2825
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parents:
diff changeset
2826 D : float
22ed769665b6 Uploaded
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parents:
diff changeset
2827
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parents:
diff changeset
2828 KS test statistic, either D, D+ or D-.
22ed769665b6 Uploaded
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parents:
diff changeset
2829
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parents:
diff changeset
2830 p-value : float
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parents:
diff changeset
2831
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parents:
diff changeset
2832 One-tailed or two-tailed p-value.
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parents:
diff changeset
2833
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parents:
diff changeset
2834 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2835
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parents:
diff changeset
2836 stats.kstest([4,17,8,3],'norm',N=20,alternative='two-sided',mode='approx')the result is (0.998650101968,6.6409100441e-12)
22ed769665b6 Uploaded
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parents:
diff changeset
2837
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parents:
diff changeset
2838 ===========
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parents:
diff changeset
2839 theilslopes
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parents:
diff changeset
2840 ===========
22ed769665b6 Uploaded
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parents:
diff changeset
2841
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parents:
diff changeset
2842 Computes the Theil-Sen estimator for a set of points (x, y).
22ed769665b6 Uploaded
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parents:
diff changeset
2843
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parents:
diff changeset
2844 theilslopes implements a method for robust linear regression. It computes the slope as the median of all slopes between paired values.
22ed769665b6 Uploaded
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parents:
diff changeset
2845
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parents:
diff changeset
2846 **The output are:**
22ed769665b6 Uploaded
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parents:
diff changeset
2847
22ed769665b6 Uploaded
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parents:
diff changeset
2848 medslope : float
22ed769665b6 Uploaded
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parents:
diff changeset
2849
22ed769665b6 Uploaded
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parents:
diff changeset
2850 Theil slope.
22ed769665b6 Uploaded
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parents:
diff changeset
2851
22ed769665b6 Uploaded
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parents:
diff changeset
2852 medintercept : float
22ed769665b6 Uploaded
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parents:
diff changeset
2853
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parents:
diff changeset
2854 Intercept of the Theil line, as median(y) - medslope*median(x).
22ed769665b6 Uploaded
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parents:
diff changeset
2855
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parents:
diff changeset
2856 lo_slope : float
22ed769665b6 Uploaded
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parents:
diff changeset
2857
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parents:
diff changeset
2858 Lower bound of the confidence interval on medslope.
22ed769665b6 Uploaded
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parents:
diff changeset
2859
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parents:
diff changeset
2860 up_slope : float
22ed769665b6 Uploaded
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parents:
diff changeset
2861
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parents:
diff changeset
2862 Upper bound of the confidence interval on medslope.
22ed769665b6 Uploaded
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parents:
diff changeset
2863
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parents:
diff changeset
2864 **example**:
22ed769665b6 Uploaded
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parents:
diff changeset
2865
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parents:
diff changeset
2866 stats.theilslopes([4,17,8,3],[30,45,5,3],0.95)the result is (0.279166666667,1.11458333333,-0.16,2.5)
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parents:
diff changeset
2867
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parents:
diff changeset
2868 </help>
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parents:
diff changeset
2869 </tool>