---
title: "Measuring functional dependency model-free"
author: "Joe Song"
date: "Updated September 5, 2019; October 24, 2018; Created April 21, 2016"
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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.

Table: **Comparison of the three quantities returned from `fun.chisq.test()`.**

----------------------------------------------------------------------------------
              Measure        Affected       Affected     Measure         Counterpart in                Counterpart in
              functional     by sample      by table     statistical     correlation                 two-sample
 Quantity     dependency?    size?          size?        significance?   test                         $t$-test
------------ -------------  ----------     -----------  --------------- ---------------             ---------------
$\chi^2_f$    Yes             Yes             Yes         No             $t$-statistic              $t$-statistic

$p$-value     Yes             Yes             Yes         Yes             $p$-value                   $p$-value

$\xi_f$       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 `cor.test()`.

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.