Spearman Rank Correlation and Kendall Tau

Install and import#

bash

npm install fintech-algorithms

import { spearmanRankCorrelationAndKendallTau } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/dependence-regression-and-model-foundations/spearman-rank-correlation-and-kendall-tau";

Signature#

spearmanRankCorrelationAndKendallTau(input)

Worked example#

verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

Input#

input

{
  "x": [1, 2, 3, 4, 5, 6],
  "y": [2, 3, 5, 4, 6, 7],
  "groups": ["A", "A", "A", "B", "B", "B"],
  "predictX": 7,
  "otherPredictor": [2, 4, 5, 8, 9, 13]
}

Call#

spearmanRankCorrelationAndKendallTau(input)

Returns#

object with 2 fields: spearman, kendallTauA

{
  "spearman": 0.9428571428571428,
  "kendallTauA": 0.8666666666666667
}

Diagrams#

How it works#

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References#

Linear Least Squares Regression — NIST/SEMATECH e-Handbook
Correlation — NIST/SEMATECH e-Handbook
Historical-example decision