# Making sense of the $\phi_k$ correlation coefficient

I am trying to put my head around the Phi_k ($$\phi_k$$) correlation parameter presented by Baak et al..

Is it possible to define $$\phi_k$$ in a closed-form? I believe it's impossible since it's the outcome of an algorithm rather than a formula. Besides, it is not clear if the results of $$\phi_k$$ are not sensitive to noise in the data.

They let $$x$$ and $$y$$ be two random variables where $$\bar{x}, \bar{y}, \sigma_{x}, \sigma_{y}$$ are their means and standard deviations, respectively. They denote the well-known Pearson correlation as $$\rho$$.

They state that the following relationship between $$\rho$$ and a bi-variate normal distribution $$f_{\text {b.n.}}$$ holds:

$$f_{\text {b.n. }}\left(x, y \mid \bar{x}, \bar{y}, \sigma_{x}, \sigma_{y}, \rho\right)=$$ $$\frac{1}{2 \pi \sigma_{x} \sigma_{y} \sqrt{1-\rho^{2}}} \exp \left(-\frac{1}{2\left(1-\rho^{2}\right)}\left[\frac{(x-\bar{x})^{2}}{\sigma_{x}^{2}}+\frac{(y-\bar{y})^{2}}{\sigma_{y}^{2}}-\frac{2 \rho(x-\bar{x})(y-\bar{y})}{\sigma_{x} \sigma_{y}}\right]\right)$$

They let $$F_{i j}$$ be the integral of the bi-variate normal distribution over an area: $$F_{i j}(\rho)=\int_{\text {area }_{i j}} f_{\text {b.n. }}(x, y \mid \rho) \mathrm{d} x \mathrm{~d} y$$

The maximum possible value of the contingency test is: $$\chi_{\max }^{2}(N, r, k)=N \min (r-1, k-1)$$, which depends only on the number of records $$N$$, rows $$r$$, and columns $$k$$, and is reached when there is a one-on-one dependency between the two variables.

To account for statistical noise, they introduce a sample-specific pedestal (what's pedestal) $$n_{\text {sdof }}$$: $$n_{\text {sdof }}=(r-1)(k-1)-n_{\text {empty }}(expected)$$ with number of rows $$r$$ and columns $$k,$$ and where $$n_{\text {empty }}(expected)$$ is the number of empty bins of the dependent frequency estimates of the sample. The pedestal is defined as: $$\chi_{\mathrm{ped}}^{2}=n_{\mathrm{sdof}}+c \cdot \sqrt{2 n_{\mathrm{sdof}}}$$

$$\chi_{\text {b.n. }}^{2}(\rho, N, r, k)=N \sum_{i, j}^{k, r} \frac{\left(F_{i j}(\rho=\rho)-F_{i j}(\rho=0)\right)^{2}}{F_{i j}(\rho=0)}$$

$$X_{\mathrm{b.n.}}^{2}(\rho, N, r, k)=\chi_{\mathrm{ped}}^{2}+\left\{\frac{\chi_{\max }^{2}(N, r, k)-\chi_{\mathrm{ped}}^{2}}{\chi_{\mathrm{b.n.}}^{2}(1, N, r, k)}\right\} \cdot \chi_{\mathrm{b.n.}}^{2}(\rho, N, r, k)$$

We can now perform the necessary steps to obtain the correlation coefficient $$\phi_{K}$$ :

1. In case of unbinned interval variables, apply a binning to each one. A reasonable binning is generally use-case specific. As a default setting we take 10 uniform bins per variable.
2. Fill the contingency table for a chosen variable pair, which contains $$N$$ records, has $$r$$ rows and $$k$$ columns.
3. Evaluate the $$\chi^{2}$$ contingency test using the Pearson's $$\chi^{2}$$ test statistic ($$\chi^{2}=\sum_{i, j} \frac{\left(O_{i j}-E_{i j}\right)^{2}}{E_{i j}}$$) and the statistically dependent frequency estimates.
4. Interpret the $$\chi^{2}$$ value as coming from a bi-variate normal distribution without statistical fluctuations.
• If $$\chi^{2}<\chi_{\text {ped }}^{2},$$ set $$\rho$$ to zero.
• Else, with fixed $$N, r, k,$$ invert the $$X_{\mathrm{b.n.}}^{2}$$ function, $$e . g .$$ using Brent's method, and numerically solve for $$\rho$$ in the range [0,1] .
• The solution for $$\rho$$ defines the correlation coefficient $$\phi_{K}$$. The procedure can be extended to more variables by using a multi-variate Gaussian instead of a bi-variate one.

When I create some simple 100x5 of random data there is some correlation between c and d in the dataframe which is not intuitive to me and the Pearson correlation suggest it is not a real correaltion.

df = pd.DataFrame(
np.random.rand(100, 5),
columns=["a", "b", "c", "d", "e"]
)


I'm one of the $$\phi_k$$ authors, so happy to help out.

Indeed there is no closed-form formula for phi_k, but it boils down to interpreting the Pearson $$\chi^2$$ value between two (binned) variables as coming from a tilted bivariate normal distribution. The $$\chi^2$$ needs to pass a certain noise pedestal, else $$\phi_k$$ is zero.

For low statistics samples $$\phi_k$$ is indeed affected by statistical fluctuations (like any correlation constant). In case of low statistics the spread in the reported values goes up, but the median is unbiased (see publication). $$\phi_k$$ is affected more than Pearson's correlation constant, because, unlike Pearson, $$\phi_k$$ does not use exact positional information. For numeric variables it bins the data, and then only uses the number of counts per bin, essentially treating them as categorical variables.

So in case of a low statistics sample, be sure to always check the significance of the correlations found (this holds for any correlation constant!). For $$\phi_k$$ simply call:

df.significance_matrix()

to get the Z-values. In your example you will see that $$\phi_k$$ is only evaluated when (roughly) $$Z > 0.5$$, that the Z scores lie around zero, and also that none of the Z scores are truly significant, say $$Z > 5$$.

In general, $$\phi_k$$ is useful when you have a set of variables where some are categorical or ordinal. If you only have numeric variables, $$\phi_k$$ will work fine but other correlation constants will be more precise, in particular for very low statistics samples.

Hope this helps!