It is a non-parametric statistical test for discrete data used to
determine if there are nonrandom associations between the two variables.
Mid-P values are a reasonable compromise between the conservativeness of
the ordinary exact test and the uncertain adequacy of large-sample methods.
Mid-P values usually performs well, typically being a bit conservative,
and is currently recommended by many leading statisticians.
Ref.: DeltaProt toolbox at http://services.cbu.uib.no/software/deltaprot/
X: data matrix (2x2-table) of observed counts
tail: The alternative hypothesis against which to compute p-values.
'ne' 2-Tail (default)
'gt' Right tail: the alternative to independence is that there is positive association between the variables.
'lt' Left tail: the alternative hypothesis is that there is negative association between the variables
Use: P = FisherExtest(Observed,'ne')
Please, use the following reference:
Thorvaldsen, S. , Flå, T. and Willassen, N.P. (2010) DeltaProt: a software toolbox for comparative genomics. BMC Bioinformatics 2010, Vol 11:573.
Agresti, A. (2001), Exact inference for categorical data: recent advances and continuing controvercies. Statistics in Medicine, 20: 2709-2722.
Hirji, K.F. (2006), Exact Analysis of Discrete Data. Chapman & Hall.
Fisher, R.A. (1934), Statistical Methods for Research Workers. Chapter 12. 5th Ed., Oliver & Boyd.
Steinar Thorvaldsen (2022). Fisher's Exact with mid-P method (https://www.mathworks.com/matlabcentral/fileexchange/29819-fisher-s-exact-with-mid-p-method), MATLAB Central File Exchange. Retrieved .
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