|Pearson r correlation coefficients for various distributions of paired data (Credit: Denis Boigelot, Wikimedia Commons)|
A paper published this week in Science outlines a new statistic called the maximal information coefficient (MIC), which is able to equally describe the correlation between paired variables regardless of linear or nonlinear relationship. In other words, as Pearson's r gives a measure of the noise surrounding a linear regression, MIC should give similar scores to equally noisy relationships regardless of type.
The authors stress that the equitable nature of the MIC makes it appropriate in the comparison of a variety of relationships. In the paper, they demonstrate its use in explorations of several large data sets on global health, gene expression, human gut microbiota, and (an R-bloggers favorite!) major-league baseball.
Instructions for its use in R can be found on the author's website (under Downloads): http://www.exploredata.net/
Congrats to the authors!
[UPDATE: the R package, minerva, now provides an easy way to implement Maximal Information-Based Nonparametric Exploration (MINE) statistics, including MIC. An example of the the package using an data set from the Science article can be found in this post: http://menugget.blogspot.de/2014/09/maximal-information-coefficient-part-ii.html]