w-statistic
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A tool for statistical analysis.
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<h2><a href="index.html">Home</a></h2><h3>Classes</h3><ul><li><a href="w-statistic.html">w-statistic</a><ul class='methods'><li data-type='method'><a href="w-statistic.html#.arrAverage">arrAverage</a></li><li data-type='method'><a href="w-statistic.html#.arrAverageWithLogNormCI">arrAverageWithLogNormCI</a></li><li data-type='method'><a href="w-statistic.html#.arrAverageWithNormCI">arrAverageWithNormCI</a></li><li data-type='method'><a href="w-statistic.html#.arrCount">arrCount</a></li><li data-type='method'><a href="w-statistic.html#.arrGammaFit">arrGammaFit</a></li><li data-type='method'><a href="w-statistic.html#.arrGammaHist">arrGammaHist</a></li><li data-type='method'><a href="w-statistic.html#.arrGammaInv">arrGammaInv</a></li><li data-type='method'><a href="w-statistic.html#.arrGeometricAverage">arrGeometricAverage</a></li><li data-type='method'><a href="w-statistic.html#.arrGeometricStd">arrGeometricStd</a></li><li data-type='method'><a href="w-statistic.html#.arrLogNormHist">arrLogNormHist</a></li><li data-type='method'><a href="w-statistic.html#.arrLogNormInv">arrLogNormInv</a></li><li data-type='method'><a href="w-statistic.html#.arrMovingAverage">arrMovingAverage</a></li><li data-type='method'><a href="w-statistic.html#.arrNormHist">arrNormHist</a></li><li data-type='method'><a href="w-statistic.html#.arrNormInv">arrNormInv</a></li><li data-type='method'><a href="w-statistic.html#.arrQuartile">arrQuartile</a></li><li data-type='method'><a href="w-statistic.html#.arrStd">arrStd</a></li><li data-type='method'><a href="w-statistic.html#.bin">bin</a></li><li data-type='method'><a href="w-statistic.html#.histGen">histGen</a></li><li data-type='method'><a href="w-statistic.html#.regLine">regLine</a></li><li data-type='method'><a href="w-statistic.html#.regMpLine">regMpLine</a></li><li data-type='method'><a href="w-statistic.html#.regPoly">regPoly</a></li><li data-type='method'><a href="w-statistic.html#.regPower">regPower</a></li><li data-type='method'><a href="w-statistic.html#.sampleRandom">sampleRandom</a></li><li data-type='method'><a href="w-statistic.html#.studentTInv">studentTInv</a></li></ul></li></ul>
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<h1 class="page-title">arrAverageWithNormCI.mjs</h1>
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<pre class="prettyprint source linenums"><code>import size from 'lodash-es/size.js'
import isNumber from 'lodash-es/isNumber.js'
import isarr from 'wsemi/src/isarr.mjs'
import isnum from 'wsemi/src/isnum.mjs'
import cdbl from 'wsemi/src/cdbl.mjs'
import arrFilterByNum from 'wsemi/src/arrFilterByNum.mjs'
import arrAverage from './arrAverage.mjs'
import arrStd from './arrStd.mjs'
import studentTInv from './studentTInv.mjs'
/**
* 基於Student-T累加分布之信賴水準(p)以及指定左右單尾條件下，計算樣本平均值。Student-T需用於常態分佈樣本。此時左尾代表樣本平均值可小於等於母數平均值，而右尾代表樣本平均值可大於等於母數平均值
*
* Unit Test: {@link https://github.com/yuda-lyu/w-statistic/blob/master/test/arrAverageWithNormCI.test.js Github}
* @memberOf w-statistic
* @param {Array} arr 輸入陣列，只提取有效數字(或為字串的數字)進行計算
* @param {Number} p 輸入信賴水準浮點數，需介於0至1之間
* @param {String} mode 輸入檢定模式字串，可選'two-tailed'、'upper-tail'、'lower-tail'，預設'two-tailed'
* @returns {Number} 回傳樣本平均值
* @example
*
* async function test() {
*
* let arr
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithNormCI(arr, 0.95))
* // => [ 33.48954903620701, 32.87408732742935 ]
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithNormCI(arr, 0.95, 'upper-tail'))
* // => 41.85625495776645
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithNormCI(arr, 0.95, 'lower-tail'))
* // => 24.507381405869904
*
* arr = [18.7261764705882, 18.6629411764705, 19.3983050847457, 18.5099999999999, 18.9986446886446, 18.9083937823834, 19.1957837837837, 19.0423529411764, 19.2320588235294, 18.8526470588235, 18.7982198952879, 19.0423529411764, 19.075, 18.945238095238, 20.6691240875912]
* console.log(await arrAverageWithNormCI(arr, 0.95, 'upper-tail'))
* // => 19.297020890880212
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithNormCI(arr, 0.25))
* // => [ 39.026745129035845, 27.336891234600518 ]
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithNormCI(arr, 0.5))
* // => [ 36.53111791211017, 29.832518451526187 ]
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithNormCI(arr, 0.75))
* // => [ 34.74951166569791, 31.614124697938454 ]
*
* arr = ['abc', '-2.5', -2.5, '-1', -1, '-0.1', -0.1, '0', 0, '0.1', 0.1, '1', 1, '2.5', 2.5, 22.5, 'xyz']
* console.log(await arrAverageWithNormCI(arr, 0.50))
* // => [ 2.5700668400787645, 0.4299331599212357 ]
*
* arr = ['abc', '0', 0, '0.1', 0.1, '1', 1, '2.5', 2.5, 22.5, 'xyz']
* console.log(await arrAverageWithNormCI(arr, 0.50))
* // => [ 5.011682286477105, 1.5883177135228952 ]
*
* }
* test()
* .catch((err) => {
* console.log(err)
* })
*
*/
async function arrAverageWithNormCI(arr, p, mode = 'two-tailed') {
//check arr
if (!isarr(arr)) {
return Promise.reject('arr is not an array')
}
if (size(arr) === 0) {
return Promise.reject('arr is not an effective array')
}
//check p
if (!isnum(p)) {
return Promise.reject(`p[${p}] is not a number`)
}
p = cdbl(p)
if (p < 0) {
return Promise.reject(`p[${p}] < 0`)
}
if (p > 1) {
return Promise.reject(`p[${p}] > 1`)
}
//check mode
if (mode !== 'two-tailed' && mode !== 'upper-tail' && mode !== 'lower-tail') {
return Promise.reject(`mode[${mode}] is not 'two-tailed', 'upper-tail' or 'lower-tail'`)
}
//rs
let rs = arrFilterByNum(arr)
//n
let n = size(rs)
//check
if (n === 0) {
return Promise.reject(`no effective data`)
}
//avg, sigma
let avg = arrAverage(arr)
let sigma = arrStd(arr) //sigma為樣本標準差(用n-1)
//check avg
if (!isNumber(avg)) {
return Promise.reject(`avg[${avg}] is not a number`)
}
//check sigma
if (!isNumber(sigma)) {
return Promise.reject(`sigma[${sigma}] is not a number`)
}
//r
let r = null
if (mode === 'upper-tail' || mode === 'lower-tail') {
//upper-tail: 右單尾
//lower-tail: 左單尾
//tav
let tav = await studentTInv(n, p) //單尾
//r
if (mode === 'upper-tail') {
r = avg + tav * sigma / Math.sqrt(n)
}
else { //左單尾
r = avg - tav * sigma / Math.sqrt(n)
}
}
else if (mode === 'two-tailed') {
//雙尾: two-tailed
//tav
let tav = await studentTInv(n, p / 2) //雙尾
//r
let r1 = avg - tav * sigma / Math.sqrt(n)
let r2 = avg + tav * sigma / Math.sqrt(n)
r = [r1, r2]
}
return r
}
export default arrAverageWithNormCI
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