w-statistic
Version:
A tool for statistical analysis.
126 lines (112 loc) • 4.6 kB
JavaScript
import size from 'lodash-es/size.js'
import isarr from 'wsemi/src/isarr.mjs'
import isnum from 'wsemi/src/isnum.mjs'
import cdbl from 'wsemi/src/cdbl.mjs'
import arrFilterByPnumAndToLog from 'wsemi/src/arrFilterByPnumAndToLog.mjs'
import arrAverageWithNormCI from './arrAverageWithNormCI.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，故數據是取log後為常態分佈才能以此計算，而於取log後座標算出樣本平均值，會再取其指數(exp)值回傳。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/arrAverageWithLogNormCI.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 arrAverageWithLogNormCI(arr, 0.95))
* // => [ 27.615050054890812, 26.794611985184215 ]
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithLogNormCI(arr, 0.95, 'upper-tail'))
* // => 41.610938902127394
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithLogNormCI(arr, 0.95, 'lower-tail'))
* // => 17.782212338746568
*
* 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 arrAverageWithLogNormCI(arr, 0.95, 'upper-tail'))
* // => 19.28628574730297
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithLogNormCI(arr, 0.25))
* // => [ 36.223415575653085, 20.42696800987406 ]
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithLogNormCI(arr, 0.5))
* // => [ 32.05361593350904, 23.084277065874097 ]
*
* arr = [6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36]
* console.log(await arrAverageWithLogNormCI(arr, 0.75))
* // => [ 29.373829998564737, 25.190264640613567 ]
*
* 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 arrAverageWithLogNormCI(arr, 0.50))
* // => [ 1.7639684225556826, 0.6249032382061773 ]
*
* arr = ['abc', '0', 0, '0.1', 0.1, '1', 1, '2.5', 2.5, 22.5, 'xyz']
* console.log(await arrAverageWithLogNormCI(arr, 0.50))
* // => [ 1.7639684225556826, 0.6249032382061773 ]
*
* }
* test()
* .catch((err) => {
* console.log(err)
* })
*
*/
async function arrAverageWithLogNormCI(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'`)
}
//取大於0數值並取log
let rs = arrFilterByPnumAndToLog(arr)
// console.log('arrFilterByPnumAndToLog', rs)
//n
let n = size(rs)
//check
if (n === 0) {
return Promise.reject(`no effective data`)
}
//arrAverageWithNormCI
let r = await arrAverageWithNormCI(rs, p, mode)
// console.log('arrAverageWithNormCI', r)
//exp
if (isarr(r)) {
r = [
Math.exp(r[0]),
Math.exp(r[1]),
]
}
else {
r = Math.exp(r)
}
return r
}
export default arrAverageWithLogNormCI