als-statistics/tests/analyze/base-stats.test.js

Modular JS statistics toolkit for Node.js and the browser: descriptive stats, correlations (Pearson/Spearman/Kendall), t-tests & ANOVA (Student/Welch), reliability (Cronbach’s alpha), regression (linear/logistic), clustering (DBSCAN/HDBSCAN), and table/co

import { describe, it } from 'node:test'
import assert from 'node:assert';
import Statistics from '../../lib/index.js'
import { BaseStats } from '../../lib/analyze/test-base/stats.js'
const { Table, Stats, Analyze } = Statistics;

function approx(actual, expected, epsilon = 1e-6) {
   assert.ok(Math.abs(actual - expected) < epsilon, `Expected ${actual} ≈ ${expected}`);
}

describe('BaseStats', () => {
   it('mean and variance', () => {
      const stats = new BaseStats([1, 2, 3, NaN, 4], 'test');
      assert(stats.mean === 2.5);
      assert(stats.variance === 1.25); // Population
      assert(stats.varianceSample === 1.6666666666666667); // Sample
   });

   it('empty array', () => {
      const stats = new BaseStats([], 'test');
      assert(isNaN(stats.mean));
      assert(isNaN(stats.variance));
   });
});

describe('OneWayAnova', () => {
   it.skip('standard ANOVA', () => {
      const test = new Analyze.CompareMeans.OneWayAnova({ g1: [1, 2, 3], g2: [4, 5, 6], g3: [7, 8, 9] });
      assert(test.F === 12); // 27
      approx(test.p,0.002, 3); // 0.001
   });

   it('Welch ANOVA (post-fix)', () => {
      const test = new Analyze.CompareMeans.OneWayAnova({ g1: [1, 2], g2: [3, 6, 9], g3: [10, 20] }, true);
      const {p,F} = test
      assert(!isNaN(F)); // Pre-fix was NaN
      assert(p >= 0);
   });
});

describe('Table', () => {
   it('addColumn and mean', () => {
      const table = new Table({ col1: [1, 2, 3], col2: [4, 5, 6] });
      assert(table.columns.col1.mean === 2);
      table.addColumn('col3', [7, 8, 9]);
      assert(table.n === 3);
      assert(table.columns.col3.mean === 8);
   });
});

describe('IndependentTTest', () => {
   it('basic t-test', () => {
      const test = new Analyze.CompareMeans.IndependentTTest({ group1: [1, 2, 3], group2: [4, 5, 6] });
      approx(test.t,-3, 1);
      approx(test.p,0.039, 3)
   });
});

describe('Pearson', () => {
   it('correlation', () => {
      const pearson = new Analyze.Correlate.Pearson({ x: [1, 2, 3], y: [4, 5, 6] });
      assert(pearson.r === 1);
      approx(pearson.p, 0, 3); // With df=1, but edge
   });
});