Chi-square tests

Three Chi-square tests of homogeneity and independence (Read-Cressie, Pearson or Log Likelihood)

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= 0

Output:
P-value

Use: P = chi2Tests(Observed,'RC')

The P-value is computed through approximation with chi-2 distribution
under the null hypothesis for all methods.
The 'RC'-method is sligtly better than the 'Pe'-method in small tables with unbalanced column margins

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.
See http://www.biomedcentral.com/1471-2105/11/573

Other reference:
Rudas, T. (1986): A Monte Carlo Comparision of Small Sample Behaviour
of 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.