time-to-botec/js/node_modules/@stdlib/stats/ttest/lib/main.js
NunoSempere b6addc7f05 feat: add the node modules
Necessary in order to clearly see the squiggle hotwiring.
2022-12-03 12:44:49 +00:00

182 lines
5.7 KiB
JavaScript

/**
* @license Apache-2.0
*
* Copyright (c) 2018 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 isNumberArray = require( '@stdlib/assert/is-number-array' ).primitives;
var isTypedArrayLike = require( '@stdlib/assert/is-typed-array-like' );
var setReadOnly = require( '@stdlib/utils/define-read-only-property' );
var isObject = require( '@stdlib/assert/is-plain-object' );
var tCDF = require( './../../base/dists/t/cdf' );
var tQuantile = require( './../../base/dists/t/quantile' );
var sqrt = require( '@stdlib/math/base/special/sqrt' );
var abs = require( '@stdlib/math/base/special/abs' );
var mean = require( './../../base/mean' );
var variance = require( './../../base/variance' );
var gcopy = require( '@stdlib/blas/base/gcopy' );
var NINF = require( '@stdlib/constants/float64/ninf' );
var PINF = require( '@stdlib/constants/float64/pinf' );
var Float64Array = require( '@stdlib/array/float64' );
var validate = require( './validate.js' );
var print = require( './print.js' ); // eslint-disable-line stdlib/no-redeclare
// MAIN //
/**
* Computes a one-sample or paired Student's t test.
*
* @param {NumericArray} x - input array
* @param {NumericArray} [y] - optional paired array
* @param {Options} [options] - function options
* @param {number} [options.alpha=0.05] - significance level
* @param {string} [options.alternative='two-sided'] - alternative hypothesis (`two-sided`, `less`, or `greater`)
* @param {number} [options.mu=0.0] - mean under `H0`
* @throws {TypeError} first argument must be a numeric array
* @throws {Error} first argument must have at least two elements
* @throws {Error} paired array must have the same length as the first argument
* @throws {TypeError} second argument must be either a numeric array or an options object
* @throws {TypeError} `alpha` option must be number
* @throws {RangeError} `alpha` option must be reside along the interval `[0,1]`
* @throws {TypeError} `alternative` option must be a recognized option value (`two-sided`, `less`, or `greater`)
* @throws {TypeError} `mu` option must be a number
* @returns {Object} test results
*
* @example
* var x = [ 4.0, 4.0, 6.0, 6.0, 5.0 ];
* var opts = {
* 'mu': 5.0
* };
* var out = ttest( x, opts );
* // returns {...}
*
* @example
* var x = [ 4.0, 4.0, 6.0, 6.0, 5.0 ];
* var opts = {
* 'alternative': 'greater'
* };
* var out = ttest( x, opts );
* // returns {...}
*/
function ttest( x ) {
var stderr;
var xmean;
var cint;
var pval;
var opts;
var stat;
var err;
var len;
var out;
var df;
var tq;
var y;
var i;
if ( !isTypedArrayLike( x ) && !isNumberArray( x ) ) {
throw new TypeError( 'invalid argument. First argument must be a numeric array. Value: `' + x + '`.' );
}
len = x.length;
if ( len < 2 ) {
throw new Error( 'invalid argument. First argument must have at least two elements. Value: `' + x + '`.' );
}
opts = {
'mu': 0.0,
'alpha': 0.05,
'alternative': 'two-sided'
};
if ( arguments.length === 2 ) {
if ( isObject( arguments[ 1 ] ) ) {
err = validate( opts, arguments[ 1 ] );
if ( err ) {
throw err;
}
} else {
y = arguments[ 1 ];
if ( !isTypedArrayLike( y ) && !isNumberArray( y ) ) {
throw new TypeError( 'invalid argument. Second argument must be either a numeric array or an options object. Value: `' + y + '`.' );
}
}
} else if ( arguments.length > 2 ) {
y = arguments[ 1 ];
if ( !isTypedArrayLike( y ) && !isNumberArray( y ) ) {
throw new TypeError( 'invalid argument. Second argument must be a numeric array. Value: `' + y + '`.' );
}
err = validate( opts, arguments[ 2 ] );
if ( err ) {
throw err;
}
}
if ( y ) {
if ( y.length !== len ) {
throw new Error( 'invalid arguments. The first and second arguments must have the same length.' );
}
x = gcopy( len, x, 1, new Float64Array( len ), 1 );
for ( i = 0; i < len; i++ ) {
x[ i ] -= y[ i ];
}
}
stderr = sqrt( variance( len, 1, x, 1 ) / len ); // TODO: replace with base/sem
xmean = mean( len, x, 1 ); // TODO: ideally, we would get both the sem and the mean from the same function and without needing to traverse 3-4 times
stat = ( xmean-opts.mu ) / stderr;
df = len - 1;
if ( opts.alternative === 'two-sided' ) {
pval = 2.0 * tCDF( -abs(stat), df );
tq = tQuantile( 1.0-(opts.alpha/2.0), df );
cint = [
opts.mu + ( (stat-tq)*stderr ),
opts.mu + ( (stat+tq)*stderr )
];
} else if ( opts.alternative === 'greater' ) {
pval = 1.0 - tCDF( stat, df );
tq = tQuantile( 1.0-opts.alpha, df );
cint = [
opts.mu + ( (stat-tq)*stderr ),
PINF
];
} else { // opts.alternative === 'less'
pval = tCDF( stat, df );
tq = tQuantile( 1.0-opts.alpha, df );
cint = [
NINF,
opts.mu + ( (stat+tq)*stderr )
];
}
out = {};
setReadOnly( out, 'rejected', pval <= opts.alpha );
setReadOnly( out, 'alpha', opts.alpha );
setReadOnly( out, 'pValue', pval );
setReadOnly( out, 'statistic', stat );
setReadOnly( out, 'ci', cint );
setReadOnly( out, 'df', df );
setReadOnly( out, 'nullValue', opts.mu );
setReadOnly( out, 'mean', xmean );
setReadOnly( out, 'sd', stderr );
setReadOnly( out, 'alternative', opts.alternative );
setReadOnly( out, 'method', ( y ) ? 'Paired t-test' : 'One-sample t-test' );
setReadOnly( out, 'print', print );
return out;
}
// EXPORTS //
module.exports = ttest;