als-statistics/lib/table/instruments/comparative.js

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

const PearsonP = require('./pearson')
class Comparative {
   constructor(sample1, sample2) {
      if (sample1.n !== sample2.n) throw new Error("Length of samples must match");
      this.sample1 = sample1; this.sample2 = sample2;
      this.n = this.sample1.n
   }

   get sumCov() {
      const { sample1: { mean: m1, values: values1 }, sample2: { mean: m2, values: values2 }, n } = this
      let sumCov = 0;
      for (let i = 0; i < n; i++) { sumCov += (values1[i] - m1) * (values2[i] - m2) }
      return sumCov
   }

   get covariancePopulation() { return this.sumCov / this.n }
   get covarianceSample() { return this.n < 2 ? 0 : this.sumCov / (this.n - 1) }
   get correlationPopulation() { return this.pearsonPopulation.r }
   get correlationSample() { return this.pearsonSample.r }
   get pearsonPopulation() {
      return new PearsonP(this.n, this.covariancePopulation, this.sample1.stdDevPopulation, this.sample2.stdDevPopulation)
   }
   get pearsonSample() {
      return new PearsonP(this.n, this.covarianceSample, this.sample1.stdDevSample, this.sample2.stdDevSample)
   }

   twoSampleTTest() {
      const { n, sample1: { mean: m1, varianceSample: var1 }, sample2: { mean: m2, varianceSample: var2 } } = this
      const pooledVariance = ((n - 1) * var1 + (n - 1) * var2) / (2 * n - 2);
      const sp = Math.sqrt(pooledVariance);
      const t = (m1 - m2) / (sp * Math.sqrt(2 / n));
      const df = 2 * n - 2;
      const F = var1 / var2;
      return { t, df, F };
   }
}

module.exports = Comparative