https://github.com/rigtorp/statkit
Statistics toolkit for JavaScript
https://github.com/rigtorp/statkit
Last synced: about 2 months ago
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Statistics toolkit for JavaScript
- Host: GitHub
- URL: https://github.com/rigtorp/statkit
- Owner: rigtorp
- License: mit
- Created: 2014-06-26T04:39:37.000Z (almost 11 years ago)
- Default Branch: master
- Last Pushed: 2014-12-03T05:44:44.000Z (over 10 years ago)
- Last Synced: 2025-03-24T00:31:29.337Z (about 2 months ago)
- Language: JavaScript
- Homepage:
- Size: 172 KB
- Stars: 51
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
statkit
=======
A statistics toolkit for javascript.
Usage
=====
Install using [npm](https://npmjs.org):
```
npm install statkit
```
Fit a linear regression model using [MCMC](http://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo):
```javascript
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))
```
Calculate a confidence interval for a correlation using the bootstrap method:
```javascript
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);
```
Perform a linear regression on the first data set in
[Anscombe's quartet](http://en.wikipedia.org/wiki/Anscombe%27s_quartet):
```javascript
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]);
```
Functions
=========
* `min(a)` - [Minimum](http://en.wikipedia.org/wiki/Minimum)
* `max(a)` - [Maximum](http://en.wikipedia.org/wiki/Maximum)
* `range(a)` - [Range](http://en.wikipedia.org/wiki/Range_(statistics))
* `quantile(a)` - [Quantile](http://en.wikipedia.org/wiki/Quantile)
* `median(a)` - [Median](http://en.wikipedia.org/wiki/Median)
* `iqr(a)` - [Interquartile range](http://en.wikipedia.org/wiki/Interquartile_range)
* `mean(a)` - [Mean](http://en.wikipedia.org/wiki/Mean)
* `gmean(a)` - [Geometric mean](http://en.wikipedia.org/wiki/Mean)
* `hmean(a)` - [Harmonic mean](http://en.wikipedia.org/wiki/Mean)
* `var(a)` - [Variance](http://en.wikipedia.org/wiki/Variance)
* `std(a)` - [Standard deviation](http://en.wikipedia.org/wiki/Standard_deviation)
* `skew(a)` - [Skewness](http://en.wikipedia.org/wiki/Skewness)
* `kurt(a)` - [Kurtosis](http://en.wikipedia.org/wiki/Kurtosis)
* `corr(x, y)` - [Correlation](http://en.wikipedia.org/wiki/Correlation) between x and y
* `entropy(p)` - [Entropy](http://en.wikipedia.org/wiki/Entropy_(information_theory))
* `kldiv(p, q)` - [Kullback–Leibler divergence](http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence)
* `shuffle(a)` - [Shuffle](http://en.wikipedia.org/wiki/Random_permutation) using the [Fisher–Yates shuffle](http://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle)
* `sample(a)` - [Sample](http://en.wikipedia.org/wiki/Sampling_(statistics)) with replacement
* `boot(nboot, bootfun, data...)` - [Bootstrap](http://en.wikipedia.org/wiki/Bootstrapping_(statistics)) the bootfun statistic
* `bootci(nboot, bootfun, data...)` - Calculate bootstrap confidence intervals using the normal model
* `randn()` - Draw random sample from the [standard normal distribution](http://en.wikipedia.org/wiki/Normal_distribution) using the [Marsaglia polar method](http://en.wikipedia.org/wiki/Marsaglia_polar_method)
* `normcdf(x)` - Normal cumulative distribution function
* `norminv(p)` - Normal inverse cumulative distribution function
* `lufactor(A, n)` - Compute pivoted [LU decomposition](http://en.wikipedia.org/wiki/LU_decomposition)
* `lusolve(LU, p, b)` - Solve `Ax=b` given the LU factorization of `A`
* `qrfactor(m, n, A)` - Compute [QR factorization](http://en.wikipedia.org/wiki/QR_decomposition) of A
* `qrsolve(m, n, QR, tau, b)` - Solve the least squares problem `min ||Ax = b||` using QR factorization `QR` of `A`
* `lstsq(m, n, A, b)` - Solve the [least squares problem](http://en.wikipedia.org/wiki/Least_squares) `min ||Ax = b||`
* `metropolis(lnpost, p, iterations, scale, burn, thin)` - Sample from `lnpost` starting at `p` using the [Metropolis-Hastings algorithm](http://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm)
Credits
=======
(c) 2014 Erik Rigtorp . MIT License