filer/perf/simple-statistics/README.test.md

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2014-12-16 23:53:29 +00:00
[![Build Status](https://secure.travis-ci.org/tmcw/simple-statistics.png?branch=master)](http://travis-ci.org/tmcw/simple-statistics)
A JavaScript implementation of descriptive, regression, and inference statistics.
Implemented in literate JavaScript with no dependencies, designed to work
in all modern browsers (including IE) as well as in node.js.
# [API](API.md)
[Full documentation](API.md)
---
```
Basic Array Operations
.mixin()
.mean(x)
.sum(x)
.variance(x)
.standard_deviation(x)
.median_absolute_deviation(x)
.median(x)
.geometric_mean(x)
.harmonic_mean(x)
.root_mean_square(x)
.min(x)
.max(x)
.t_test(sample, x)
.t_test_two_sample(sample_x, sample_y, difference)
.sample_variance(x)
.sample_covariance(x)
.sample_correlation(x)
.quantile(sample, p)
.iqr(sample)
.sample_skewness(sample)
.jenks(data, number_of_classes)
.r_squared(data, function)
.cumulative_std_normal_probability(z)
.z_score(x, mean, standard_deviation)
.standard_normal_table
Regression
.linear_regression()
.data([[1, 1], [2, 2]])
.line()
.m()
.b()
Classification
.bayesian()
.train(item, category)
.score(item)
```
---
# [Literate Source](http://macwright.org/simple-statistics/)
## Usage
To use it in browsers, grab [simple_statistics.js](https://raw.github.com/tmcw/simple-statistics/master/src/simple_statistics.js).
To use it in node, install it with [npm](https://npmjs.org/) or add it to your package.json.
npm install simple-statistics
To use it with [component](https://github.com/component/component),
component install tmcw/simple-statistics
To use it with [bower](http://bower.io/),
bower install simple-statistics
## Basic Descriptive Statistics
```javascript
// Require simple statistics
var ss = require('simple-statistics');
// The input is a simple array
var list = [1, 2, 3];
// Many different descriptive statistics are supported
var sum = ss.sum(list),
mean = ss.mean(list),
min = ss.min(list),
geometric_mean = ss.geometric_mean(list),
max = ss.max(list),
quantile = ss.quantile(0.25);
```
## Linear Regression
```javascript
// For a linear regression, it's a two-dimensional array
var data = [ [1, 2], [2, 3] ];
// simple-statistics can produce a linear regression and return
// a friendly javascript function for the line.
var line = ss.linear_regression()
.data(data)
.line();
// get a point along the line function
line(0);
var line = ss.linear_regression()
// Get the r-squared value of the line estimation
ss.r_squared(data, line);
```
### Bayesian Classifier
```javascript
var bayes = ss.bayesian();
bayes.train({ species: 'Cat' }, 'animal');
bayes.score({ species: 'Cat' });
// { animal: 1 }
```
### Mixin Style
_This is **optional** and not used by default. You can opt-in to mixins
with `ss.mixin()`._
This mixes `simple-statistics` methods into the Array prototype - note that
[extending native objects](http://perfectionkills.com/extending-built-in-native-objects-evil-or-not/) is a
tricky move.
This will _only work_ if `defineProperty` is available, which means modern browsers
and nodejs - on IE8 and below, calling `ss.mixin()` will throw an exception.
```javascript
// mixin to Array class
ss.mixin();
// The input is a simple array
var list = [1, 2, 3];
// The same descriptive techniques as above, but in a simpler style
var sum = list.sum(),
mean = list.mean(),
min = list.min(),
max = list.max(),
quantile = list.quantile(0.25);
```
## Examples
* [Linear regression with simple-statistics and d3js](http://bl.ocks.org/3931800)
* [Jenks Natural Breaks with a choropleth map with d3js](http://bl.ocks.org/tmcw/4969184)
# Contributors
* Tom MacWright
* [Matt Sacks](https://github.com/mattsacks)
* Doron Linder
* [Alexander Sicular](https://github.com/siculars)