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        "author": "Christoph Moehl",
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        "description": "================\npermutation_test\n================\n\n\nWhat is it\n----------\n\nImplementation of Fisher's permutation test.\n\nThe test is described in following publications:\n\n- Fisher, R. A. (1935). The design of experiments. 1935. Oliver and Boyd, Edinburgh.\n\n- Ernst, M. D. (2004). Permutation methods: a basis for exact inference. Statistical Science, 19(4), 676-685\n\n\nHow to install it\n-----------------\n\nInstall with pip::\n\n    $ pip install permutation_test\n\n\n\nCommand Line Script Usage\n-------------------------\n\nExample::\n  \n    permtest [path/to/data.csv] [groups_colname] [reference_group_name] -t [test_group_name]\n\n\nUse help to get info about parameters::\n\n    $ permtest -h\n\n    usage: permtest [-h] [-t TESTGROUP]\n                input_filepath treatment_column_name referencegroup\n\n    positional arguments:\n      input_filepath        e.g. path/to/my/data.csv, path to csv file with data\n      treatment_column_name\n                            name of column in the csv table that specifies the\n                            group\n      referencegroup        name of the reference group as named in the csv table\n\n    optional arguments:\n      -h, --help            show this help message and exit\n      -t TESTGROUP, --testgroup TESTGROUP\n                            name of the test group as named in th csv table. If\n                            not defined, test group is determined automatically.\n       -a ALPHA, --alpha ALPHA\n                             significance level alpha (between 0 and 1) If not\n                             defined, alpha is set to 0.05.\n       -m MULTI_COMP_CORR, --multi_comp_corr MULTI_COMP_CORR\n                             perform multiple comparison correction with benjamini\n                             hochberg procedure yes/no, If not defined, correction\n                             is performed.\n                      \n\n\nSpecifications of data structure in csv file\n--------------------------------------------\n\n- The csv should contain comma separated values. One ore more columns should contain measurement data.\n\n- All columns need to have a name, specified in the first row.\n\n- One column contains names for the groups\n\nExample *my_data.csv*:\n\n============ ============ ============ ===========\nexperiment_1 experiment_2 experiment_3 group_names\n============ ============ ============ ===========\n1.4          3            2.5          condition_2  \n2            5            2            condition_1\n5.6          3            17           condition_2\n9            6.5          2            condition_1\n17           5            13.0         condition_1\n17           2            13.0         condition_3\n12           8            18.7         condition_3\n============ ============ ============ ===========\n\n\nTo perform tests for all experiments, where *condition_1* is the reference and *condition_2* is\nthe test data, run follwoing command::\n\n    $ permtest my_data.csv group_names condition_1 -t condition_2\n\nOften, it is convenient to save the output in a textfile::\n\n    $ permtest my_data.csv group_names condition_1 -t condition_2 > my_test_result.txt\n\n\n\nPython Library Use Example\n--------------------------\n::\n\n    >>> import permutation_test as p\n    >>> data = [1,2,2,3,3,3,4,4,5]\n    >>> ref_data = [3,4,4,5,5,5,6,6,7]\n\n    >>> p_value = p.permutation_test(data, ref_data)\n    taking random subsample of size 20000 from 48620 possible permutations\n    nr of mean diffs: 20000\n\n\n     Distribution of mean differences\n                                           \u2502                                        \n                                        *  \u253c+1.73038                                \n                                           \u2502  *                                     \n                                           \u2502                                        \n                                     *     \u2502      *                                 \n                                           \u2502                                        \n                                  *        \u2502         *                              \n                                           \u2502                                        \n                                           \u2502                                        \n                              *            \u2502             *                          \n                                           \u2502                                        \n                                           \u2502                                        \n                           *               \u2502                *                       \n                                           \u2502                                        \n                        *                  \u2502                   *                    \n                                           \u2502                                        \n                    *                      \u2502                       *                \n             *   *                         \u253c+0.037                    *   *         \n    \u2500\u2500\u2500\u253c*****\u2500***\u2500**\u2500***\u2500**\u2500**\u2500***\u2500**\u2500**\u2500**\u253c**\u2500***\u2500**\u2500***\u2500**\u2500**\u2500***\u2500**\u2500***\u2500*****\u253c\u2500\u2500\u2500\n        -2.38713                           \u2502                            +2.39919    \n    mean difference of tested dataset: -2.0\n    p_value: 0.00345\n    p_lower_than (probability that mean of test data is not lower than mean of ref data): 0.00345\n    p_value_greater_than (probability that mean of test data is not greater than mean of ref data): 0.9998\n    0.0034500000000000121\n\n\nThe asccii art plot shows the ditribution of mean differences for the permutations. \nThe ascii art plot is done with [AP](https://github.com/mfouesneau/asciiplot), a plotting package by Morgan Fouesneau.\n\n\nIf the number of possible combinations is grater than n_combinations_max,\na random subsample of size n_combinations_max is taken for histogram calculation.\n\nIf detailed is False, only (two-sided) p_value is returned,\ni.e. the probability that data is not different from ref_data \n\nIf detailed is True, one-sided p values and histogram data of \nmean differences is returned in a dict:\n\nhist_data: distribution of mean differences for all permutations\np_value: two sided p_value (the probability that data is not\ndifferent from ref_data )\np_value_lower_than: the probability that mean of data is not \nlower than mean of ref_data\np_value_greater_than: the probability that mean of data is \nnot grater than mean of ref_data \n\n\nChristoph M\u00f6hl,\nImage and Data Analysis Facililty/Core Faciliies,\nDeutsches Zentrum f\u00fcr Neurodegenerative Erkrankungen e. V. (DZNE) in der Helmholtz-Gemeinschaft\nGerman Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association",
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