Given an input contingency table,
fun.chisq.test() offers three quantities to evaluate non-parametric functional dependency of the column variable \(Y\) on the row variable \(X\). They include the functional chi-squared test statistic (\(\chi^2_f\)), statistical significance (\(p\)-value), and effect size (function index \(\xi_f\)).
We explain their differences in analogy to those statistics returned from
cor.test(), the R function for the test of correlation, and the \(t\)-test. We chose both tests because they are widely used and well understood. Another choice could be the Pearson’s chi-squared test plus a statistic called Cramer’s V, analogous to correlation coefficient, but not as popularly used. The table below summarizes the differences among the quantities and their analogous counterparts in correlation and \(t\) tests.
|Quantity||Measure functional dependency?||Affected by sample size?||Affected by table size?||Measure statistical significance?||Counterpart in correlation test||Counterpart in two-sample \(t\)-test|
|\(\xi_f\)||Yes||No||No||No||correlation coefficient||mean difference|
The test statistic \(\chi^2_f\) measures deviation of \(Y\) from a uniform distribution contributed by \(X\). It is maximized when there is a functional relationship from \(X\) to \(Y\). This statistic is also affected by sample size and the size of the contingency table. It summarizes the strength of both functional dependency and support from the sample. A strong function supported by few samples may have equal \(\chi^2_f\) to a weak function supported by many samples. It is analogous to the test statistic (not to be confused with correlation coefficient) in
cor.test(), or the \(t\) statistic from the \(t\)-test.
The \(p\)-value of \(\chi^2_f\) overcomes the table size factor and making tables of different sizes or sample sizes comparable. However, its null distribution (chi-squared or normalized) is only asymptotically true. It is analogous to the role of the \(p\)-value of
The function index \(\xi_f\) measures only the strength of functional dependency normalized by sample and table sizes without considering statistical significance. When the sample size is small, the index can be unreliable; when the sample size is large, it is a direct measure of functional dependency and is comparable across tables. It is analogous to the role of correlation coefficient in
cor.test(), or fold change in \(t\)-test for differential gene expression analysis.