anova1.js (4834B)
1 /** 2 * @license Apache-2.0 3 * 4 * Copyright (c) 2018 The Stdlib Authors. 5 * 6 * Licensed under the Apache License, Version 2.0 (the "License"); 7 * you may not use this file except in compliance with the License. 8 * You may obtain a copy of the License at 9 * 10 * http://www.apache.org/licenses/LICENSE-2.0 11 * 12 * Unless required by applicable law or agreed to in writing, software 13 * distributed under the License is distributed on an "AS IS" BASIS, 14 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 15 * See the License for the specific language governing permissions and 16 * limitations under the License. 17 */ 18 19 'use strict'; 20 21 // MODULES // 22 23 var isNumberArray = require( '@stdlib/assert/is-number-array' ).primitives; 24 var isTypedArrayLike = require( '@stdlib/assert/is-typed-array-like' ); 25 var isArray = require( '@stdlib/assert/is-array' ); 26 var setReadOnly = require( '@stdlib/utils/define-read-only-property' ); 27 var hasOwnProp = require( '@stdlib/assert/has-own-property' ); 28 var cdf = require( './../../base/dists/f/cdf' ); 29 var copy = require( '@stdlib/utils/copy' ); 30 var defaults = require( './defaults.json' ); 31 var validate = require( './validate.js' ); 32 var unique = require( './unique.js' ); 33 var meanTable = require( './mean_table.js' ); 34 var mean = require( './mean.js' ); 35 var prettyPrint = require( './print.js' ); 36 37 38 // MAIN // 39 40 /** 41 * Perform a one-way analysis of variance (ANOVA). 42 * 43 * @param {NumericArray} x - measured values 44 * @param {Array} factor - array of treatments 45 * @param {Options} [options] - function options 46 * @param {number} [options.alpha=0.05] - significance level 47 * @throws {TypeError} options argument must be an object 48 * @throws {TypeError} must provide valid options 49 * @throws {TypeError} `x` must be a numeric array 50 * @throws {TypeError} `factor` must be an array 51 * @throws {RangeError} `factor` must have at least two unique elements 52 * @throws {RangeError} length of `x` must be greater than or equal to two 53 * @throws {RangeError} `x` and `factor` must have the same length 54 * @returns {Object} test results 55 */ 56 function anova1( x, factor, options ) { 57 var meanSumSqTreat; // Mean sum of squares 58 var meanSumSqError; 59 var ssTreatment; 60 var sumSqTotal; 61 var sumSqError; 62 var treatment; // Index variable 63 var grandMean; 64 var nGroups; 65 var fScore; 66 var treats; 67 var means; 68 var numDf; 69 var denDf; 70 var nobs; 71 var pVal; 72 var opts; 73 var err; 74 var out; 75 var sq; 76 var i; 77 78 if ( !isTypedArrayLike( x ) && !isNumberArray( x ) ) { 79 throw new TypeError( 'invalid argument. First argument must be a numeric array. Value: `' + x + '`.' ); 80 } 81 opts = copy( defaults ); 82 if ( arguments.length > 2 ) { 83 err = validate( opts, options ); 84 if ( err ) { 85 throw err; 86 } 87 } 88 nobs = x.length; 89 if ( nobs <= 1 ) { 90 throw new RangeError( 'invalid argument. First argument must have at least two elements. Value: `' + x + '`.' ); 91 } 92 if ( !isArray( factor ) ) { 93 throw new TypeError( 'invalid argument. Second argument must be an array. Value: `' + treats + '`.' ); 94 } 95 96 treats = unique( factor ); 97 nGroups = treats.length; 98 if ( nGroups <= 1 ) { 99 throw new RangeError( 'invalid argument. Second argument must contain at least two unique elements. Value: `' + treats + '`.' ); 100 } 101 if ( nobs !== factor.length ) { 102 throw new RangeError( 'invalid arguments. Arguments `x` and `factor` must be arrays of the same length.' ); 103 } 104 105 sumSqTotal = 0.0; 106 ssTreatment = 0.0; 107 means = meanTable( x, factor, treats ); 108 grandMean = mean( x ); 109 110 // Now get total ss: 111 for ( i = 0; i < nobs; i++ ) { 112 sq = ( x[i] - grandMean ) * ( x[i] - grandMean ); 113 sumSqTotal += sq; 114 } 115 116 sq = 0.0; 117 for ( treatment in means ) { 118 if ( hasOwnProp( means, treatment ) ) { 119 // Already have sq defined 120 sq = ( means[treatment].mean - grandMean ) * 121 ( means[treatment].mean - grandMean ); 122 ssTreatment += means[treatment].sampleSize * sq; 123 } 124 } 125 numDf = nGroups - 1; 126 denDf = nobs - nGroups; 127 sumSqError = sumSqTotal - ssTreatment; 128 meanSumSqTreat = ssTreatment / numDf; 129 meanSumSqError = sumSqError / denDf; 130 fScore = meanSumSqTreat / meanSumSqError; 131 132 pVal = 1.0 - cdf( fScore, numDf, denDf ); 133 134 out = {}; 135 136 treatment = {}; 137 setReadOnly( treatment, 'df', numDf ); 138 setReadOnly( treatment, 'ss', ssTreatment ); 139 setReadOnly( treatment, 'ms', meanSumSqTreat ); 140 setReadOnly( out, 'treatment', treatment ); 141 142 err = {}; 143 setReadOnly( err, 'df', denDf ); 144 setReadOnly( err, 'ss', sumSqError ); 145 setReadOnly( err, 'ms', meanSumSqError ); 146 setReadOnly( out, 'error', err ); 147 148 setReadOnly( out, 'statistic', fScore ); 149 setReadOnly( out, 'pValue', pVal ); 150 setReadOnly( out, 'means', means ); 151 setReadOnly( out, 'method', 'One-Way ANOVA' ); 152 setReadOnly( out, 'alpha', opts.alpha ); 153 setReadOnly( out, 'rejected', pVal <= opts.alpha ); 154 setReadOnly( out, 'print', prettyPrint( out ) ); 155 return out; 156 } 157 158 159 // EXPORTS // 160 161 module.exports = anova1;