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import mean from "./mean.js";import shuffleInPlace from "./shuffle_in_place.js";
/** * Conducts a [permutation test](https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests) * to determine if two data sets are *significantly* different from each other, using * the difference of means between the groups as the test statistic. * The function allows for the following hypotheses: * - two_tail = Null hypothesis: the two distributions are equal. * - greater = Null hypothesis: observations from sampleX tend to be smaller than those from sampleY. * - less = Null hypothesis: observations from sampleX tend to be greater than those from sampleY. * [Learn more about one-tail vs two-tail tests.](https://en.wikipedia.org/wiki/One-_and_two-tailed_tests) * * @param {Array<number>} sampleX first dataset (e.g. treatment data) * @param {Array<number>} sampleY second dataset (e.g. control data) * @param {string} alternative alternative hypothesis, either 'two_sided' (default), 'greater', or 'less' * @param {number} k number of values in permutation distribution. * @param {Function} [randomSource=Math.random] an optional entropy source * @returns {number} p-value The probability of observing the difference between groups (as or more extreme than what we did), assuming the null hypothesis. * * @example * var control = [2, 5, 3, 6, 7, 2, 5]; * var treatment = [20, 5, 13, 12, 7, 2, 2]; * permutationTest(control, treatment); // ~0.1324 */function permutationTest(sampleX, sampleY, alternative, k, randomSource) {    // Set default arguments    if (k === undefined) {        k = 10000;    }    if (alternative === undefined) {        alternative = "two_side";    }    if (        alternative !== "two_side" &&        alternative !== "greater" &&        alternative !== "less"    ) {        throw new Error(            "`alternative` must be either 'two_side', 'greater', or 'less'."        );    }
    // get means for each sample    const meanX = mean(sampleX);    const meanY = mean(sampleY);
    // calculate initial test statistic. This will be our point of comparison with    // the generated test statistics.    const testStatistic = meanX - meanY;
    // create test-statistic distribution    const testStatDsn = new Array(k);
    // combine datsets so we can easily shuffle later    const allData = sampleX.concat(sampleY);    const midIndex = Math.floor(allData.length / 2);
    for (let i = 0; i < k; i++) {        // 1. shuffle data assignments        shuffleInPlace(allData, randomSource);        const permLeft = allData.slice(0, midIndex);        const permRight = allData.slice(midIndex, allData.length);
        // 2.re-calculate test statistic        const permTestStatistic = mean(permLeft) - mean(permRight);
        // 3. store test statistic to build test statistic distribution        testStatDsn[i] = permTestStatistic;    }
    // Calculate p-value depending on alternative    // For this test, we calculate the percentage of 'extreme' test statistics (subject to our hypothesis)    // more info on permutation test p-value calculations: https://onlinecourses.science.psu.edu/stat464/node/35    let numExtremeTStats = 0;    if (alternative === "two_side") {        for (let i = 0; i <= k; i++) {            if (Math.abs(testStatDsn[i]) >= Math.abs(testStatistic)) {                numExtremeTStats += 1;            }        }    } else if (alternative === "greater") {        for (let i = 0; i <= k; i++) {            if (testStatDsn[i] >= testStatistic) {                numExtremeTStats += 1;            }        }    } else {        // alternative === 'less'        for (let i = 0; i <= k; i++) {            if (testStatDsn[i] <= testStatistic) {                numExtremeTStats += 1;            }        }    }
    return numExtremeTStats / k;}
export default permutationTest;