Pattern Analysis Across Contingency Tables

Statistical hypothesis testing of pattern heterogeneity via differences in underlying distributions across multiple contingency tables. Five tests are included: the comparative chi-squared test (Song et al. 2014) (Zhang et al. 2015) , the Sharma-Song test, the heterogeneity test, the marginal-change test (Sharma et al. 2020), and the strength test (Sharma et al. 2020). Under the null hypothesis that row and column variables are statistically independent and joint distributions are equal, their test statistics all follow an asymptotically chi-squared distribution. A comprehensive type analysis categorizes the relation among the contingency tables into type null, 0, 1, and 2 (Sharma et al. 2020). They can identify heterogeneous patterns that differ in either the first order (marginal) or the second order (joint distribution deviation from product of marginals). Second-order differences may reveal more fundamental changes than first-order differences across heterogeneous patterns.


Reference manual

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0.1.1 by Joe Song, 7 months ago

Browse source code at

Authors: Ruby Sharma [aut] , Joe Song [aut, cre]

Documentation:   PDF Manual  

LGPL (>= 3) license

Imports Rdpack

Depends on pander

Suggests Ckmeans.1d.dp, FunChisq, knitr, rmarkdown, testthat

Suggested by FunChisq.

See at CRAN