Fast & numerically stable statistical analysis
Fast and Numerically Stable Statistical Analysis Utilities
Perform fast and numerically stable statistical analysis using wink-statistics
. It can handle real-time stream of data and can incrementally compute required statistic that usually would take more than one pass over the data as in standard deviation or simple linear regression.
Use npm to install:
npm install wink-statistics --save
Here is an example of computing slope
, intercept
and r2
etc. from a stream of (x, y)
data in real-time:
// Load wink-statistics.
var stats = require( 'wink-statistics' );
// Instantiate streaming simple linear regression
var regression = stats.streaming.simpleLinearRegression();
// Following would be ideally placed within a stream of data:
regression.compute( 10, 80 );
regression.compute( 15, 75 );
regression.compute( 16, 65 );
regression.compute( 18, 50 );
regression.compute( 21, 45 );
regression.compute( 30, 30 );
regression.compute( 36, 18 );
regression.compute( 40, 9 );
// Use result() method to access the outcome in real time.
regression.result();
// returns { slope: -2.3621,
// intercept: 101.4188,
// r: -0.9766,
// r2: 0.9537,
// se: 5.624,
// size: 8
// }
The functions under the data
name space require data in an array. Here is an example of boxplot analysis:
var boxplot = stats.data.boxplot;
var data = [
-12, 14, 14, 14, 16, 18, 20, 20, 21, 23, 27, 27, 27, 29, 31,
31, 32, 32, 34, 36, 40, 40, 40, 40, 40, 42, 51, 56, 60, 88
];
boxplot( data );
// returns {
// min: -12, q1: 20, median: 31, q3: 40, max: 88,
// iqr: 20, range: 100, size: 30,
// leftOutliers: { begin: 0, end: 0, count: 1, fence: 14 },
// rightOutliers: { begin: 29, end: 29, count: 1, fence: 60 },
// leftNotch: 25.230655727612252,
// rightNotch: 36.76934427238775
// }
wink-stats
can handle data in different formats to avoid pre-processing. For example, you can compute median from the array of objects containing value:
var median = stats.data.median;
var data = [
{ value: 1 },
{ value: 1 },
{ value: 2 },
{ value: 2 },
{ value: 3 },
{ value: 3 },
{ value: 4 },
{ value: 4 }
];
// Use key name — `value` as the `accessor`
median( data, 'value' );
// returns 2.5
It even supports passing functions as accessors
to handle even more complex data structures.
Check out the statistics API documentation to learn more.
If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.
Wink is a family of open source packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.
wink-statistics is copyright 2017-20 GRAYPE Systems Private Limited.
It is licensed under the terms of the MIT License.