var sk = require("statkit.js"); // log-likelihood for the model y ~ N(m*x + b, 1/t) function lnlike(theta, x, y) { var m = theta[0], b = theta[1], t = theta[2]; var s = 0.0; for (var i = 0; i < x.length; i++) { var r = y[i] - (m * x[i] + b); s += r*r*t - Math.log(t); } return -0.5*s; } // uniform log-prior for m, b, t function lnprior(theta) { var m = theta[0], b = theta[1], t = theta[2]; if (0.0 < m && m < 1.0 && 0.0 < b && b < 10.0 && 0.0 < t && t < 100.0) { return 0.0; } return -Infinity; } // posterior log-probability function function lnpost(theta, x, y) { var lp = lnprior(theta); if (!isFinite(lp)) { return -Infinity; } return lp + lnlike(theta, x, y); } var x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]; var y = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]; var res = sk.metropolis(function(theta) { return lnpost(theta, x, y); }, [0.5, 3.0, 1.0], 1000000, 0.1, 50000, 100); console.log('acceptance rate:', res.accepted) console.log('posteriors (16/50/84 percentiles):') console.log('m', sk.quantile(res.chain[0], 0.16), sk.median(res.chain[0]), sk.quantile(res.chain[0], 0.84)) console.log('b', sk.quantile(res.chain[1], 0.16), sk.median(res.chain[1]), sk.quantile(res.chain[1], 0.84)) console.log('t', sk.quantile(res.chain[2], 0.16), sk.median(res.chain[2]), sk.quantile(res.chain[2], 0.84))

var sk = require("statkit"); var lsat = [576, 635, 558, 578, 666, 580, 555, 661, 651, 605, 653, 575, 545, 572, 594]; var gpa = [3.39, 3.30, 2.81, 3.03, 3.44, 3.07, 3.00, 3.43, 3.36, 3.13, 3.12, 2.74, 2.76, 2.88, 2.96]; var corr = sk.corr(gpa, lsat); var ci = sk.bootci(100000, sk.corr, gpa, lsat); console.log("corr = ", corr, "ci = ", ci);

var sk = require("statkit"); var x = [10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5]; var y = [8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]; var A = new Array(x.length*2); for (var i = 0; i < x.length; ++i) { A[2*i] = 1; A[2*i + 1] = x[i]; } var b = sk.lstsq(x.length, 2, A, y); console.log("intercept = ", b[0], "slope = ", b[1]);