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FisherExact 1.4.2

[![PyPI version](https://badge.fury.io/py/FisherExact.svg)](https://badge.fury.io/py/FisherExact)# FisherExactFisher exact test for mxn contingency table## Installation FisherExact should be python2/3 compatible. You can install it with pip : `pip install FisherExact`If you get an error about builtins module, install "future" with `pip install future`This package use fortran sources, so you need to have a fortran compiler (`gfortran`) installed. See here ==> https://gcc.gnu.org/wiki/GFortranBinaries.The source code was tested on Linux and Mac (thanks to [@fomightez](https://github.com/fomightez))## Binary UsageA binary is provided to use FisherExact from the terminal usage: fexact [-h] [--simulate [SIMULATE]] [--hybrid] [--midP] [--retry ATTEMPT] [--workspace WORKSPACE] [--version] table Fisher's Exact test for mxn contingency table positional arguments: table Contingency table in a file, without header optional arguments: -h, --help show this help message and exit --simulate [SIMULATE] Simulate p-values with n replicates --hybrid Use hybrid mode --midP Use midP correction --retry ATTEMPT Number of attempt to made if execution fail --workspace WORKSPACE Workspace size to use, Increase this if the program crash --version show program's version number and exit## Contingency table format if fexact is used as binaryThe accepted format is space/tab or comma (or both) separated values with an optionnal first line starting with a "#" that specified the number of rows and column:For example, the following format are accepted```# 2 38 2 121 5 2``````8 2 121 5 2``````#2, 38 2 121 5 2``````8,2,121,5,2```## Use as a module fisher_exact(table, alternative='two-sided', hybrid=False, midP=False, simulate_pval=False, replicate=2000, workspace=300, attempt=2, seed=None) Performs a Fisher exact test on a mxn contingency table. Parameters ---------- table : array_like of ints A 2x2 contingency table. Elements should be non-negative integers. alternative : {'two-sided', 'less', 'greater'}, optional Which alternative hypothesis to the null hypothesis the test uses. Default is 'two-sided'. Only used in the 2 x 2 case (with the scipy function). In every other case, the two-sided pval is returned. mult : int Specify the size of the workspace used in the network algorithm. Only used for non-simulated p-values larger than 2 x 2 table. You might want to increase this if the p-value failed hybrid : bool Only used for larger than 2 x 2 tables, in which cases it indicates whether the exact probabilities (default) or a hybrid approximation thereof should be computed. midP : bool Use this to enable mid-P correction. Could lead to slow computation. This is not applicable for simulation p-values. `alternative` cannot be used if you enable midpoint correction. simulate_pval : bool Indicate whether to compute p-values by Monte Carlo simulation, in larger than 2 x 2 tables. replicate : int An integer specifying the number of replicates used in the Monte Carlo test. workspace : int An integer specifying the workspace size. Default value is 300. attempt : int Number of attempts to try, if the workspace size is not enough. On each attempt, the workspace size is doubled. seed : int Random number to use as seed. If a seed isn't provided. 4 bytes will be read from os.urandom. If this fail, getrandbits of the random module (with 32 random bits) will be used. In the particular case where both failed, the current time will be used Returns ------- p_value : float The probability of a more extreme table, where 'extreme' is in a probabilistic sense.