/** * @license Apache-2.0 * * Copyright (c) 2020 The Stdlib Authors. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ 'use strict'; // MODULES // var setReadOnly = require( '@stdlib/utils/define-read-only-property' ); var isNonNegativeIntegerArray = require( '@stdlib/assert/is-nonnegative-integer-array' ); var isMatrixLike = require( '@stdlib/assert/is-matrix-like' ); var isArrayArray = require( '@stdlib/assert/is-array-array' ); var array = require( '@stdlib/ndarray/array' ); var incrmin = require( './../../incr/min' ); var gsum = require( '@stdlib/blas/ext/base/gsum' ); var min = require( '@stdlib/math/base/special/min' ); var copy = require( '@stdlib/utils/copy' ); var chisqCDF = require( './../../base/dists/chisquare/cdf' ); var prettyPrint = require( './print.js' ); var defaults = require( './defaults.json' ); var sumByDimension = require( './sum.js' ); var outer = require( './outer.js' ); var absdiff = require( './absdiff.js' ); var validate = require( './validate.js' ); // MAIN // /** * Performs a chi-square independence test. * * @param {(ndarray|ArrayArray)} x - two-way table of cell counts * @param {Options} [options] - function options * @param {number} [options.alpha=0.05] - significance level * @param {boolean} [options.correct=true] - boolean indicating whether to use Yates' continuity correction when provided a 2x2 contingency table * @throws {TypeError} first argument must be an array of arrays or ndarray-like object with dimension two * @returns {Object} test results * * @example * * @example * var x = [ [ 20, 30 ], [ 30, 20 ] ]; * var out = chi2test( x ); * // returns { 'rejected': false, 'alpha': 0.05, 'pValue': ~0.072, ... } */ function chi2test( x, options ) { var absDiff; var colSums; var rowSums; var minAbs; var yates; var means; var param; var nrow; var ncol; var opts; var pval; var stat; var err; var out; var N; var i; if ( isArrayArray( x ) ) { x = array( x ); } if ( !isMatrixLike( x ) ) { throw new TypeError( 'invalid argument. First argument `x` must be an array of arrays or ndarray-like object with dimension two. Value: `' + x + '`.' ); } if ( !isNonNegativeIntegerArray( x.data ) ) { throw new TypeError( 'invalid argument. First argument `x` must contain nonnegative integers. Value: `' + x + '`.' ); } opts = copy( defaults ); if ( arguments.length > 1 ) { err = validate( opts, options ); if ( err ) { throw err; } } N = gsum( x.length, x.data, 1 ); nrow = x.shape[ 0 ]; ncol = x.shape[ 1 ]; colSums = sumByDimension( x, 1 ); rowSums = sumByDimension( x, 2 ); means = outer( rowSums, colSums ); for ( i = 0; i < means.length; i++ ) { means.data[ i ] /= N; } absDiff = absdiff( x, means ); if ( opts.correct && nrow === 2 && ncol === 2 ) { // Apply Yates' continuity correction: minAbs = incrmin(); for ( i = 0; i < absDiff.length; i++ ) { minAbs( absDiff[ i ] ); } yates = min( 0.5, minAbs() ); for ( i = 0; i < absDiff.length; i++ ) { absDiff[ i ] -= yates; } } for ( i = 0; i < absDiff.length; i++ ) { absDiff[ i ] *= absDiff[ i ]; absDiff[ i ] /= means.data[ i ]; } stat = gsum( absDiff.length, absDiff, 1 ); param = ( nrow - 1 ) * ( ncol - 1 ); pval = 1 - chisqCDF( stat, param ); out = {}; setReadOnly( out, 'rejected', pval <= opts.alpha ); setReadOnly( out, 'alpha', opts.alpha ); setReadOnly( out, 'pValue', pval ); setReadOnly( out, 'df', param ); setReadOnly( out, 'expected', means ); setReadOnly( out, 'statistic', stat ); setReadOnly( out, 'method', 'Chi-square independence test' ); setReadOnly( out, 'print', prettyPrint( out ) ); return out; } // EXPORTS // module.exports = chi2test;