als-statistics/tests/table/comparative.test.js

A powerful and lightweight JavaScript library for descriptive statistics, regression, clustering, outlier detection, and noise analysis using a flexible table/column architecture.

const { describe, it } = require('node:test');
const assert = require('node:assert');
const Comparative = require('../../lib/table/instruments/comparative');
const RatioColumn = require('../../lib/ratio-column/index');

describe('Comparative', () => {
   const sample1 = new RatioColumn([10, 20, 30, 40, 50]);
   const sample2 = new RatioColumn([15, 25, 35, 45, 55]);
   const analysis = new Comparative(sample1, sample2);

   it('should calculate sum of covariance (sumCov)', () => {
      assert.strictEqual(analysis.sumCov.toFixed(2), '1000.00');
   });

   it('should calculate covariance for population', () => {
      assert.strictEqual(analysis.covariancePopulation.toFixed(2), '200.00');
   });

   it('should calculate covariance for sample', () => {
      assert.strictEqual(analysis.covarianceSample.toFixed(2), '250.00');
   });

   it('should calculate correlation coefficient for population', () => {
      assert.strictEqual(analysis.correlationPopulation.toFixed(2), '1.00');
   });

   it('should calculate correlation coefficient for sample', () => {
      assert.strictEqual(analysis.correlationSample.toFixed(2), '1.00');
   });

   it('should perform two-sample t-test', () => {
      const { t, df, F } = analysis.twoSampleTTest();
      assert.strictEqual(t.toFixed(2), '-0.50');
      assert.strictEqual(df, 8);
      assert.strictEqual(F.toFixed(2), '1.00');
   });

   it('should throw an error if sample sizes do not match', () => {
      const sample3 = new RatioColumn([10, 20, 30]);
      assert.throws(() => new Comparative(sample1, sample3), /Length of samples must match/);
   });

   it('should return correlation as 0 if one sample has zero variance', () => {
      const sample1 = new RatioColumn([1, 1, 1, 1, 1]);
      const sample2 = new RatioColumn([10, 20, 30, 40, 50]);
      const comparison = new Comparative(sample1, sample2);
      assert.strictEqual(comparison.correlationPopulation, 0);
   });

   it('should handle zero variance in two-sample t-test', () => {
      const sample1 = new RatioColumn([1, 1, 1, 1, 1]);
      const sample2 = new RatioColumn([10, 20, 30, 40, 50]);
      const comparison = new Comparative(sample1, sample2);
      const { t, df, F } = comparison.twoSampleTTest();
      assert.ok(!isNaN(t)); // Проверяем, что t определён
      assert.strictEqual(df, 8);
      assert.strictEqual(F, 0); // Одна из дисперсий 0
   });

   it('should return 0 correlationSample with zero variance', () => {
      const sample1 = new RatioColumn([1, 1, 1]);
      const sample2 = new RatioColumn([10, 20, 30]);
      const comparison = new Comparative(sample1, sample2);
      assert.strictEqual(comparison.correlationSample, 0);
   });

   it('should return zero t-statistic when means are equal', () => {
      const sample1 = new RatioColumn([1, 2, 3]);
      const sample2 = new RatioColumn([1, 2, 3]);
      const comparison = new Comparative(sample1, sample2);
      const { t } = comparison.twoSampleTTest();
      assert.strictEqual(t, 0);
   });
});