Computes a one-way or two-way Pearson's chi-square test
$ npm install compute-chisq-testFor use in the browser, use browserify.
var chisqTest = require( 'compute-chisq-test' );This function is used to compute a goodness-of-fit test when x is an array or typed array, treating it as a vector of observed frequencies. When x is a matrix, it is assumed to be a contingency table of observed frequencies, and a two-way chi-square test for independence is computed.
The functions returns an object holding the pValue of the test, the calculated test statistic T and the degrees of freedom of the test (df).
var matrix = require( 'dstructs-matrix' ),
counts,
mat,
out;
// Example: does data come from a fair dice?
counts = [ 10, 8, 12, 15, 8, 9 ];
out = chisqTest( counts );
/*
{ T: ~3.613,
df: 5,
pValue: ~0.606 }
*/
/*
Example: is there an association between voting preferences and gender?
Table:
Voting Preferences
Republican Democrat Independent
Male 200 150 50
Female 250 300 50
*/
mat = matrix( new Int32Array([200,150,50,250,300,50]), [2,3] );
out = chisqTest( mat );
/*
{ T: ~16.2037,
df: 2,
pValue: ~0.0003 }
*/The returned object comes with a .toString() method which when invoked will print a formatted output of the results of the hypothesis test.
mat = matrix( new Int32Array([200,150,50,250,300,50]), [2,3] );
console.log( chisqTest( mat ).toString() );
/*
Two-way chi-square test for marginal independence.
null hypothesis: there is no association between the variables.
test statistic: 16.2037
df: 2
p-value: 0.0003
*/By default, in the array case it is assumed that the assumed theoretical distribution is uniform over the categories. To supply any discrete distribution, use the probs option:
// Example: does data come from loaded dice where a six has probability 1/2?
var counts = [ 10, 15, 17, 13, 7, 50 ];
var probs = [ 0.1, 0.1, 0.1, 0.1, 0.1, 0.5 ];
chisqTest( counts, {
'probs': probs
});
/*
{ T: ~6.928,
df: 5,
pValue: ~0.226 }
*/The probs option argument has to be a probability array, i.e. an array of non-negative values which sum to one.
To use Yates' continuity correction in the chi-square test of independence, set the correct option to true:
mat = matrix( new Int32Array([4,7,4,4]), [2,2] );
out = chisqTest( mat )
/*
{ T: 0.3533
df: 1,
pValue: 0.5522 }
*/
out = chisqTest( mat, {
'correct': true
});
/*
{ T: 0.0153,
df: 1,
pValue: 0.9014 }
*/var chisqTest = require( 'compute-chisq-test' ),
matrix = require( 'dstructs-matrix' ),
out,
counts, probs,
mat;
/*
Goodnes of Fit test:
example taken from: http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm
*/
counts = [ 48, 35, 15, 3 ];
probs = [ 0.58, 0.345, 0.07, 0.005 ];
out = chisqTest(counts,{
'probs': probs
});
/*
Chi-Square test of independence:
example taken from: http://stattrek.com/chi-square-test/independence.aspx?Tutorial=AP
Table:
Voting Preferences
Republican Democrat Independent
Male 200 150 50
Female 250 300 50
*/
mat = matrix( new Int32Array([200,150,50,250,300,50]), [2,3] );
// without Yates continuity correction
out = chisqTest( mat, {
'correct': false
});
/*
with Yates continuity correction,
suggested when at least one cell of the table has an expected count < 5
*/
out = chisqTest( mat, {
'correct': true
});To run the example code from the top-level application directory,
$ node ./examples/index.jsUnit tests use the Mocha test framework with Chai assertions. To run the tests, execute the following command in the top-level application directory:
$ make testAll new feature development should have corresponding unit tests to validate correct functionality.
This repository uses Istanbul as its code coverage tool. To generate a test coverage report, execute the following command in the top-level application directory:
$ make test-covIstanbul creates a ./reports/coverage directory. To access an HTML version of the report,
$ make view-covCopyright © 2014-2015. The Compute.io Authors.