Chi-square tests 1.0

Chi-square tests of homogeneity and independence.Computes the P-value for I x J - table row/col independence.Ref.: DeltaProt toolbox at http://services.cbu.uib.no/software/deltaprot/Input:X: data matrix (I x J -table) of the observed frequency cells.method: 'RC': Read-Cressie power divergence statistics (default), lambda= 2/3 'Pe': Standard Pearson chi2-distance, lambda= 1 'LL': Log Likelihood ratio distance, lambda= 0Output:P-valueUse: P = chi2Tests(Observed,'RC')The P-value is computed through approximation with chi-2 distributionunder the null hypothesis for all methods.The 'RC'-method is sligtly better than the 'Pe'-method in small tables with unbalanced column marginsPlease, use the following reference:Thorvaldsen, S. , FldoDE, T. and Willassen, N.P. (2010) DeltaProt: a software toolbox for comparative genomics. BMC Bioinformatics 2010, Vol 11:573.See http://www.biomedcentral.com/1471-2105/11/573Other reference:Rudas, T. (1986): A Monte Carlo Comparision of Small Sample Behaviourof The Pearson, the Likelihood Ratio and the Cressie-Read Statistics. J.Statist. Comput. Simul, vol 24, pp 107-120.Read, TRC and Cressie, NAC (1988): Goodness of Fit Statistics for Discrete Multivariate Data. Springer Verlag.Ewens, WJ and Grant, GR (2001): Statistical Methods in Bioinformatics. Springer Verlag.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Bioinformatics,  Carlo,  Comparative,  Genomics,  Monte,  Rudas,  Software,  Willassen
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