als-statistics/lib/analyze/regression/regression-base.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 { htmlTable } from "../../utils/html-table.js";

export class RegressionBase {
   constructor(columns, yName, xNames = [], step) {
      this.columns = columns;
      this.yName = yName;
      this.xNames = xNames
      this.step = step
   }

   calculate() {
      const { columns, yName, xNames } = this
      this.y = columns[yName];
      this.X = columns[xNames[0]].map((_, i) => [1, ...xNames.map(name => columns[name][i])]);
      this.n = this.X.length;
      this.k = this.X[0].length;
      this.computeCoefficients();
      this.coefficients.forEach(c => {
         if (!isFinite(c)) throw new Error(`Regression failed: singular matrix or constant predictors ${c}`);
      })
      this.yHat = this.predict(this.X);
   }

   computeCoefficients() { }
   predict() { }

   get result() {
      if (!this.coefficients) this.calculate()
      return { step: this.step, n: this.n, Variable: ['Intercept', ...this.xNames], Coefficient: this.coefficients };
   }

   get htmlTable() {
      const { result } = this, { step, n } = result;
      const headers = Object.keys(result).filter(k => k !== 'step' && k !== 'n');
      const rows = headers.map((k) => result[k])
      const header = `Step:${step} (${this.xNames.length} predictors, n=${n})`
      return htmlTable(rows, headers, { header, firstColHeader: true, transposeValues:true })
   }
}