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