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9 9 1 3 3 3 21 1 7 7 35 35 35 35 35 35 35 28 56 24 24 7 7 1 1 1 1 1 1 2 2 2 1 1 2 7 1 1 1 8 5 5 1 1 317 317 314 3 1 5 1 1 338 337 337 1661 337 1 5 3 1 15 4 1 62 11 11 62 62 62 11 1 12 10 1 148 10 148 148 148 10 1 1 2 2 120 8 120 2 8 2 14 8 2 8 2 2 2 2 2 6 2 2 1 3 3 3 1 30 12 12 12 3 3 1 2 3 30 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 | /* global module */ // # simple-statistics // // A simple, literate statistics system. The code below uses the // [Javascript module pattern](http://www.adequatelygood.com/2010/3/JavaScript-Module-Pattern-In-Depth), // eventually assigning `simple-statistics` to `ss` in browsers or the // `exports` object for node.js (function() { var ss = {}; Eif (typeof module !== 'undefined') { // Assign the `ss` object to exports, so that you can require // it in [node.js](http://nodejs.org/) module.exports = ss; } else { // Otherwise, in a browser, we assign `ss` to the window object, // so you can simply refer to it as `ss`. this.ss = ss; } // # [Linear Regression](http://en.wikipedia.org/wiki/Linear_regression) // // [Simple linear regression](http://en.wikipedia.org/wiki/Simple_linear_regression) // is a simple way to find a fitted line // between a set of coordinates. function linear_regression() { var linreg = {}, data = []; // Assign data to the model. Data is assumed to be an array. linreg.data = function(x) { if (!arguments.length) return data; data = x.slice(); return linreg; }; // Calculate the slope and y-intercept of the regression line // by calculating the least sum of squares linreg.mb = function() { var m, b; // Store data length in a local variable to reduce // repeated object property lookups var data_length = data.length; //if there's only one point, arbitrarily choose a slope of 0 //and a y-intercept of whatever the y of the initial point is if (data_length === 1) { m = 0; b = data[0][1]; } else { // Initialize our sums and scope the `m` and `b` // variables that define the line. var sum_x = 0, sum_y = 0, sum_xx = 0, sum_xy = 0; // Use local variables to grab point values // with minimal object property lookups var point, x, y; // Gather the sum of all x values, the sum of all // y values, and the sum of x^2 and (x*y) for each // value. // // In math notation, these would be SS_x, SS_y, SS_xx, and SS_xy for (var i = 0; i < data_length; i++) { point = data[i]; x = point[0]; y = point[1]; sum_x += x; sum_y += y; sum_xx += x * x; sum_xy += x * y; } // `m` is the slope of the regression line m = ((data_length * sum_xy) - (sum_x * sum_y)) / ((data_length * sum_xx) - (sum_x * sum_x)); // `b` is the y-intercept of the line. b = (sum_y / data_length) - ((m * sum_x) / data_length); } // Return both values as an object. return { m: m, b: b }; }; // a shortcut for simply getting the slope of the regression line linreg.m = function() { return linreg.mb().m; }; // a shortcut for simply getting the y-intercept of the regression // line. linreg.b = function() { return linreg.mb().b; }; // ## Fitting The Regression Line // // This is called after `.data()` and returns the // equation `y = f(x)` which gives the position // of the regression line at each point in `x`. linreg.line = function() { // Get the slope, `m`, and y-intercept, `b`, of the line. var mb = linreg.mb(), m = mb.m, b = mb.b; // Return a function that computes a `y` value for each // x value it is given, based on the values of `b` and `a` // that we just computed. return function(x) { return b + (m * x); }; }; return linreg; } // # [R Squared](http://en.wikipedia.org/wiki/Coefficient_of_determination) // // The r-squared value of data compared with a function `f` // is the sum of the squared differences between the prediction // and the actual value. function r_squared(data, f) { if (data.length < 2) return 1; // Compute the average y value for the actual // data set in order to compute the // _total sum of squares_ var sum = 0, average; for (var i = 0; i < data.length; i++) { sum += data[i][1]; } average = sum / data.length; // Compute the total sum of squares - the // squared difference between each point // and the average of all points. var sum_of_squares = 0; for (var j = 0; j < data.length; j++) { sum_of_squares += Math.pow(average - data[j][1], 2); } // Finally estimate the error: the squared // difference between the estimate and the actual data // value at each point. var err = 0; for (var k = 0; k < data.length; k++) { err += Math.pow(data[k][1] - f(data[k][0]), 2); } // As the error grows larger, its ratio to the // sum of squares increases and the r squared // value grows lower. return 1 - (err / sum_of_squares); } // # [Bayesian Classifier](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) // // This is a naïve bayesian classifier that takes // singly-nested objects. function bayesian() { // The `bayes_model` object is what will be exposed // by this closure, with all of its extended methods, and will // have access to all scope variables, like `total_count`. var bayes_model = {}, // The number of items that are currently // classified in the model total_count = 0, // Every item classified in the model data = {}; // ## Train // Train the classifier with a new item, which has a single // dimension of Javascript literal keys and values. bayes_model.train = function(item, category) { // If the data object doesn't have any values // for this category, create a new object for it. if (!data[category]) data[category] = {}; // Iterate through each key in the item. for (var k in item) { var v = item[k]; // Initialize the nested object `data[category][k][item[k]]` // with an object of keys that equal 0. if (data[category][k] === undefined) data[category][k] = {}; if (data[category][k][v] === undefined) data[category][k][v] = 0; // And increment the key for this key/value combination. data[category][k][item[k]]++; } // Increment the number of items classified total_count++; }; // ## Score // Generate a score of how well this item matches all // possible categories based on its attributes bayes_model.score = function(item) { // Initialize an empty array of odds per category. var odds = {}, category; // Iterate through each key in the item, // then iterate through each category that has been used // in previous calls to `.train()` for (var k in item) { var v = item[k]; for (category in data) { // Create an empty object for storing key - value combinations // for this category. Eif (odds[category] === undefined) odds[category] = {}; // If this item doesn't even have a property, it counts for nothing, // but if it does have the property that we're looking for from // the item to categorize, it counts based on how popular it is // versus the whole population. if (data[category][k]) { odds[category][k + '_' + v] = (data[category][k][v] || 0) / total_count; } else { odds[category][k + '_' + v] = 0; } } } // Set up a new object that will contain sums of these odds by category var odds_sums = {}; for (category in odds) { // Tally all of the odds for each category-combination pair - // the non-existence of a category does not add anything to the // score. for (var combination in odds[category]) { Eif (odds_sums[category] === undefined) odds_sums[category] = 0; odds_sums[category] += odds[category][combination]; } } return odds_sums; }; // Return the completed model. return bayes_model; } // # sum // // is simply the result of adding all numbers // together, starting from zero. // // This runs on `O(n)`, linear time in respect to the array function sum(x) { var value = 0; for (var i = 0; i < x.length; i++) { value += x[i]; } return value; } // # mean // // is the sum over the number of values // // This runs on `O(n)`, linear time in respect to the array function mean(x) { // The mean of no numbers is null if (x.length === 0) return null; return sum(x) / x.length; } // # geometric mean // // a mean function that is more useful for numbers in different // ranges. // // this is the nth root of the input numbers multiplied by each other // // This runs on `O(n)`, linear time in respect to the array function geometric_mean(x) { // The mean of no numbers is null if (x.length === 0) return null; // the starting value. var value = 1; for (var i = 0; i < x.length; i++) { // the geometric mean is only valid for positive numbers if (x[i] <= 0) return null; // repeatedly multiply the value by each number value *= x[i]; } return Math.pow(value, 1 / x.length); } // # harmonic mean // // a mean function typically used to find the average of rates // // this is the reciprocal of the arithmetic mean of the reciprocals // of the input numbers // // This runs on `O(n)`, linear time in respect to the array function harmonic_mean(x) { // The mean of no numbers is null if (x.length === 0) return null; var reciprocal_sum = 0; for (var i = 0; i < x.length; i++) { // the harmonic mean is only valid for positive numbers if (x[i] <= 0) return null; reciprocal_sum += 1 / x[i]; } // divide n by the the reciprocal sum return x.length / reciprocal_sum; } // # min // // This is simply the minimum number in the set. // // This runs on `O(n)`, linear time in respect to the array function min(x) { var value; for (var i = 0; i < x.length; i++) { // On the first iteration of this loop, min is // undefined and is thus made the minimum element in the array if (x[i] < value || value === undefined) value = x[i]; } return value; } // # max // // This is simply the maximum number in the set. // // This runs on `O(n)`, linear time in respect to the array function max(x) { var value; for (var i = 0; i < x.length; i++) { // On the first iteration of this loop, max is // undefined and is thus made the maximum element in the array if (x[i] > value || value === undefined) value = x[i]; } return value; } // # [variance](http://en.wikipedia.org/wiki/Variance) // // is the sum of squared deviations from the mean // // depends on `mean()` function variance(x) { // The variance of no numbers is null if (x.length === 0) return null; var mean_value = mean(x), deviations = []; // Make a list of squared deviations from the mean. for (var i = 0; i < x.length; i++) { deviations.push(Math.pow(x[i] - mean_value, 2)); } // Find the mean value of that list return mean(deviations); } // # [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation) // // is just the square root of the variance. // // depends on `variance()` function standard_deviation(x) { // The standard deviation of no numbers is null if (x.length === 0) return null; return Math.sqrt(variance(x)); } // The sum of deviations to the Nth power. // When n=2 it's the sum of squared deviations. // When n=3 it's the sum of cubed deviations. // // depends on `mean()` function sum_nth_power_deviations(x, n) { var mean_value = mean(x), sum = 0; for (var i = 0; i < x.length; i++) { sum += Math.pow(x[i] - mean_value, n); } return sum; } // # [variance](http://en.wikipedia.org/wiki/Variance) // // is the sum of squared deviations from the mean // // depends on `sum_nth_power_deviations` function sample_variance(x) { // The variance of no numbers is null if (x.length <= 1) return null; var sum_squared_deviations_value = sum_nth_power_deviations(x, 2); // Find the mean value of that list return sum_squared_deviations_value / (x.length - 1); } // # [standard deviation](http://en.wikipedia.org/wiki/Standard_deviation) // // is just the square root of the variance. // // depends on `sample_variance()` function sample_standard_deviation(x) { // The standard deviation of no numbers is null if (x.length <= 1) return null; return Math.sqrt(sample_variance(x)); } // # [covariance](http://en.wikipedia.org/wiki/Covariance) // // sample covariance of two datasets: // how much do the two datasets move together? // x and y are two datasets, represented as arrays of numbers. // // depends on `mean()` function sample_covariance(x, y) { // The two datasets must have the same length which must be more than 1 if (x.length <= 1 || x.length != y.length){ return null; } // determine the mean of each dataset so that we can judge each // value of the dataset fairly as the difference from the mean. this // way, if one dataset is [1, 2, 3] and [2, 3, 4], their covariance // does not suffer because of the difference in absolute values var xmean = mean(x), ymean = mean(y), sum = 0; // for each pair of values, the covariance increases when their // difference from the mean is associated - if both are well above // or if both are well below // the mean, the covariance increases significantly. for (var i = 0; i < x.length; i++){ sum += (x[i] - xmean) * (y[i] - ymean); } // the covariance is weighted by the length of the datasets. return sum / (x.length - 1); } // # [correlation](http://en.wikipedia.org/wiki/Correlation_and_dependence) // // Gets a measure of how correlated two datasets are, between -1 and 1 // // depends on `sample_standard_deviation()` and `sample_covariance()` function sample_correlation(x, y) { var cov = sample_covariance(x, y), xstd = sample_standard_deviation(x), ystd = sample_standard_deviation(y); if (cov === null || xstd === null || ystd === null) { return null; } return cov / xstd / ystd; } // # [median](http://en.wikipedia.org/wiki/Median) // // The middle number of a list. This is often a good indicator of 'the middle' // when there are outliers that skew the `mean()` value. function median(x) { // The median of an empty list is null if (x.length === 0) return null; // Sorting the array makes it easy to find the center, but // use `.slice()` to ensure the original array `x` is not modified var sorted = x.slice().sort(function (a, b) { return a - b; }); // If the length of the list is odd, it's the central number if (sorted.length % 2 === 1) { return sorted[(sorted.length - 1) / 2]; // Otherwise, the median is the average of the two numbers // at the center of the list } else { var a = sorted[(sorted.length / 2) - 1]; var b = sorted[(sorted.length / 2)]; return (a + b) / 2; } } // # [mode](http://bit.ly/W5K4Yt) // // The mode is the number that appears in a list the highest number of times. // There can be multiple modes in a list: in the event of a tie, this // algorithm will return the most recently seen mode. // // This implementation is inspired by [science.js](https://github.com/jasondavies/science.js/blob/master/src/stats/mode.js) // // This runs on `O(n)`, linear time in respect to the array function mode(x) { // Handle edge cases: // The median of an empty list is null if (x.length === 0) return null; else if (x.length === 1) return x[0]; // Sorting the array lets us iterate through it below and be sure // that every time we see a new number it's new and we'll never // see the same number twice var sorted = x.slice().sort(function (a, b) { return a - b; }); // This assumes it is dealing with an array of size > 1, since size // 0 and 1 are handled immediately. Hence it starts at index 1 in the // array. var last = sorted[0], // store the mode as we find new modes value, // store how many times we've seen the mode max_seen = 0, // how many times the current candidate for the mode // has been seen seen_this = 1; // end at sorted.length + 1 to fix the case in which the mode is // the highest number that occurs in the sequence. the last iteration // compares sorted[i], which is undefined, to the highest number // in the series for (var i = 1; i < sorted.length + 1; i++) { // we're seeing a new number pass by if (sorted[i] !== last) { // the last number is the new mode since we saw it more // often than the old one if (seen_this > max_seen) { max_seen = seen_this; value = last; } seen_this = 1; last = sorted[i]; // if this isn't a new number, it's one more occurrence of // the potential mode } else { seen_this++; } } return value; } // # [t-test](http://en.wikipedia.org/wiki/Student's_t-test) // // This is to compute a one-sample t-test, comparing the mean // of a sample to a known value, x. // // in this case, we're trying to determine whether the // population mean is equal to the value that we know, which is `x` // here. usually the results here are used to look up a // [p-value](http://en.wikipedia.org/wiki/P-value), which, for // a certain level of significance, will let you determine that the // null hypothesis can or cannot be rejected. // // Depends on `standard_deviation()` and `mean()` function t_test(sample, x) { // The mean of the sample var sample_mean = mean(sample); // The standard deviation of the sample var sd = standard_deviation(sample); // Square root the length of the sample var rootN = Math.sqrt(sample.length); // Compute the known value against the sample, // returning the t value return (sample_mean - x) / (sd / rootN); } // # [2-sample t-test](http://en.wikipedia.org/wiki/Student's_t-test) // // This is to compute two sample t-test. // Tests whether "mean(X)-mean(Y) = difference", ( // in the most common case, we often have `difference == 0` to test if two samples // are likely to be taken from populations with the same mean value) with // no prior knowledge on standard deviations of both samples // other than the fact that they have the same standard deviation. // // Usually the results here are used to look up a // [p-value](http://en.wikipedia.org/wiki/P-value), which, for // a certain level of significance, will let you determine that the // null hypothesis can or cannot be rejected. // // `diff` can be omitted if it equals 0. // // [This is used to confirm or deny](http://www.monarchlab.org/Lab/Research/Stats/2SampleT.aspx) // a null hypothesis that the two populations that have been sampled into // `sample_x` and `sample_y` are equal to each other. // // Depends on `sample_variance()` and `mean()` function t_test_two_sample(sample_x, sample_y, difference) { var n = sample_x.length, m = sample_y.length; // If either sample doesn't actually have any values, we can't // compute this at all, so we return `null`. if (!n || !m) return null ; // default difference (mu) is zero if (!difference) difference = 0; var meanX = mean(sample_x), meanY = mean(sample_y); var weightedVariance = ((n - 1) * sample_variance(sample_x) + (m - 1) * sample_variance(sample_y)) / (n + m - 2); return (meanX - meanY - difference) / Math.sqrt(weightedVariance * (1 / n + 1 / m)); } // # chunk // // Split an array into chunks of a specified size. This function // has the same behavior as [PHP's array_chunk](http://php.net/manual/en/function.array-chunk.php) // function, and thus will insert smaller-sized chunks at the end if // the input size is not divisible by the chunk size. // // `sample` is expected to be an array, and `chunkSize` a number. // The `sample` array can contain any kind of data. function chunk(sample, chunkSize) { // a list of result chunks, as arrays in an array var output = []; // `chunkSize` must be zero or higher - otherwise the loop below, // in which we call `start += chunkSize`, will loop infinitely. // So, we'll detect and return null in that case to indicate // invalid input. if (chunkSize <= 0) { return null; } // `start` is the index at which `.slice` will start selecting // new array elements for (var start = 0; start < sample.length; start += chunkSize) { // for each chunk, slice that part of the array and add it // to the output. The `.slice` function does not change // the original array. output.push(sample.slice(start, start + chunkSize)); } return output; } // # shuffle_in_place // // A [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle) // in-place - which means that it will change the order of the original // array by reference. function shuffle_in_place(sample, randomSource) { // a custom random number source can be provided if you want to use // a fixed seed or another random number generator, like // [random-js](https://www.npmjs.org/package/random-js) randomSource = randomSource || Math.random; // store the current length of the sample to determine // when no elements remain to shuffle. var length = sample.length; // temporary is used to hold an item when it is being // swapped between indices. var temporary; // The index to swap at each stage. var index; // While there are still items to shuffle while (length > 0) { // chose a random index within the subset of the array // that is not yet shuffled index = Math.floor(randomSource() * length--); // store the value that we'll move temporarily temporary = sample[length]; // swap the value at `sample[length]` with `sample[index]` sample[length] = sample[index]; sample[index] = temporary; } return sample; } // # shuffle // // A [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle) // is a fast way to create a random permutation of a finite set. function shuffle(sample, randomSource) { // slice the original array so that it is not modified sample = sample.slice(); // and then shuffle that shallow-copied array, in place return shuffle_in_place(sample.slice(), randomSource); } // # quantile // // This is a population quantile, since we assume to know the entire // dataset in this library. Thus I'm trying to follow the // [Quantiles of a Population](http://en.wikipedia.org/wiki/Quantile#Quantiles_of_a_population) // algorithm from wikipedia. // // Sample is a one-dimensional array of numbers, // and p is either a decimal number from 0 to 1 or an array of decimal // numbers from 0 to 1. // In terms of a k/q quantile, p = k/q - it's just dealing with fractions or dealing // with decimal values. // When p is an array, the result of the function is also an array containing the appropriate // quantiles in input order function quantile(sample, p) { // We can't derive quantiles from an empty list if (sample.length === 0) return null; // Sort a copy of the array. We'll need a sorted array to index // the values in sorted order. var sorted = sample.slice().sort(function (a, b) { return a - b; }); if (p.length) { // Initialize the result array var results = []; // For each requested quantile for (var i = 0; i < p.length; i++) { results[i] = quantile_sorted(sorted, p[i]); } return results; } else { return quantile_sorted(sorted, p); } } // # quantile // // This is the internal implementation of quantiles: when you know // that the order is sorted, you don't need to re-sort it, and the computations // are much faster. function quantile_sorted(sample, p) { var idx = (sample.length) * p; if (p < 0 || p > 1) { return null; } else if (p === 1) { // If p is 1, directly return the last element return sample[sample.length - 1]; } else if (p === 0) { // If p is 0, directly return the first element return sample[0]; } else if (idx % 1 !== 0) { // If p is not integer, return the next element in array return sample[Math.ceil(idx) - 1]; } else if (sample.length % 2 === 0) { // If the list has even-length, we'll take the average of this number // and the next value, if there is one return (sample[idx - 1] + sample[idx]) / 2; } else { // Finally, in the simple case of an integer value // with an odd-length list, return the sample value at the index. return sample[idx]; } } // # [Interquartile range](http://en.wikipedia.org/wiki/Interquartile_range) // // A measure of statistical dispersion, or how scattered, spread, or // concentrated a distribution is. It's computed as the difference between // the third quartile and first quartile. function iqr(sample) { // We can't derive quantiles from an empty list if (sample.length === 0) return null; // Interquartile range is the span between the upper quartile, // at `0.75`, and lower quartile, `0.25` return quantile(sample, 0.75) - quantile(sample, 0.25); } // # [Median Absolute Deviation](http://en.wikipedia.org/wiki/Median_absolute_deviation) // // The Median Absolute Deviation (MAD) is a robust measure of statistical // dispersion. It is more resilient to outliers than the standard deviation. function mad(x) { // The mad of nothing is null if (!x || x.length === 0) return null; var median_value = median(x), median_absolute_deviations = []; // Make a list of absolute deviations from the median for (var i = 0; i < x.length; i++) { median_absolute_deviations.push(Math.abs(x[i] - median_value)); } // Find the median value of that list return median(median_absolute_deviations); } // ## Compute Matrices for Jenks // // Compute the matrices required for Jenks breaks. These matrices // can be used for any classing of data with `classes <= n_classes` function jenksMatrices(data, n_classes) { // in the original implementation, these matrices are referred to // as `LC` and `OP` // // * lower_class_limits (LC): optimal lower class limits // * variance_combinations (OP): optimal variance combinations for all classes var lower_class_limits = [], variance_combinations = [], // loop counters i, j, // the variance, as computed at each step in the calculation variance = 0; // Initialize and fill each matrix with zeroes for (i = 0; i < data.length + 1; i++) { var tmp1 = [], tmp2 = []; // despite these arrays having the same values, we need // to keep them separate so that changing one does not change // the other for (j = 0; j < n_classes + 1; j++) { tmp1.push(0); tmp2.push(0); } lower_class_limits.push(tmp1); variance_combinations.push(tmp2); } for (i = 1; i < n_classes + 1; i++) { lower_class_limits[1][i] = 1; variance_combinations[1][i] = 0; // in the original implementation, 9999999 is used but // since Javascript has `Infinity`, we use that. for (j = 2; j < data.length + 1; j++) { variance_combinations[j][i] = Infinity; } } for (var l = 2; l < data.length + 1; l++) { // `SZ` originally. this is the sum of the values seen thus // far when calculating variance. var sum = 0, // `ZSQ` originally. the sum of squares of values seen // thus far sum_squares = 0, // `WT` originally. This is the number of w = 0, // `IV` originally i4 = 0; // in several instances, you could say `Math.pow(x, 2)` // instead of `x * x`, but this is slower in some browsers // introduces an unnecessary concept. for (var m = 1; m < l + 1; m++) { // `III` originally var lower_class_limit = l - m + 1, val = data[lower_class_limit - 1]; // here we're estimating variance for each potential classing // of the data, for each potential number of classes. `w` // is the number of data points considered so far. w++; // increase the current sum and sum-of-squares sum += val; sum_squares += val * val; // the variance at this point in the sequence is the difference // between the sum of squares and the total x 2, over the number // of samples. variance = sum_squares - (sum * sum) / w; i4 = lower_class_limit - 1; if (i4 !== 0) { for (j = 2; j < n_classes + 1; j++) { // if adding this element to an existing class // will increase its variance beyond the limit, break // the class at this point, setting the `lower_class_limit` // at this point. if (variance_combinations[l][j] >= (variance + variance_combinations[i4][j - 1])) { lower_class_limits[l][j] = lower_class_limit; variance_combinations[l][j] = variance + variance_combinations[i4][j - 1]; } } } } lower_class_limits[l][1] = 1; variance_combinations[l][1] = variance; } // return the two matrices. for just providing breaks, only // `lower_class_limits` is needed, but variances can be useful to // evaluate goodness of fit. return { lower_class_limits: lower_class_limits, variance_combinations: variance_combinations }; } // ## Pull Breaks Values for Jenks // // the second part of the jenks recipe: take the calculated matrices // and derive an array of n breaks. function jenksBreaks(data, lower_class_limits, n_classes) { var k = data.length - 1, kclass = [], countNum = n_classes; // the calculation of classes will never include the upper and // lower bounds, so we need to explicitly set them kclass[n_classes] = data[data.length - 1]; kclass[0] = data[0]; // the lower_class_limits matrix is used as indices into itself // here: the `k` variable is reused in each iteration. while (countNum > 1) { kclass[countNum - 1] = data[lower_class_limits[k][countNum] - 2]; k = lower_class_limits[k][countNum] - 1; countNum--; } return kclass; } // # [Jenks natural breaks optimization](http://en.wikipedia.org/wiki/Jenks_natural_breaks_optimization) // // Implementations: [1](http://danieljlewis.org/files/2010/06/Jenks.pdf) (python), // [2](https://github.com/vvoovv/djeo-jenks/blob/master/main.js) (buggy), // [3](https://github.com/simogeo/geostats/blob/master/lib/geostats.js#L407) (works) // // Depends on `jenksBreaks()` and `jenksMatrices()` function jenks(data, n_classes) { if (n_classes > data.length) return null; // sort data in numerical order, since this is expected // by the matrices function data = data.slice().sort(function (a, b) { return a - b; }); // get our basic matrices var matrices = jenksMatrices(data, n_classes), // we only need lower class limits here lower_class_limits = matrices.lower_class_limits; // extract n_classes out of the computed matrices return jenksBreaks(data, lower_class_limits, n_classes); } // # [Skewness](http://en.wikipedia.org/wiki/Skewness) // // A measure of the extent to which a probability distribution of a // real-valued random variable "leans" to one side of the mean. // The skewness value can be positive or negative, or even undefined. // // Implementation is based on the adjusted Fisher-Pearson standardized // moment coefficient, which is the version found in Excel and several // statistical packages including Minitab, SAS and SPSS. // // Depends on `sum_nth_power_deviations()` and `sample_standard_deviation` function sample_skewness(x) { // The skewness of less than three arguments is null if (x.length < 3) return null; var n = x.length, cubed_s = Math.pow(sample_standard_deviation(x), 3), sum_cubed_deviations = sum_nth_power_deviations(x, 3); return n * sum_cubed_deviations / ((n - 1) * (n - 2) * cubed_s); } // # Standard Normal Table // A standard normal table, also called the unit normal table or Z table, // is a mathematical table for the values of Φ (phi), which are the values of // the cumulative distribution function of the normal distribution. // It is used to find the probability that a statistic is observed below, // above, or between values on the standard normal distribution, and by // extension, any normal distribution. // // The probabilities are taken from http://en.wikipedia.org/wiki/Standard_normal_table // The table used is the cumulative, and not cumulative from 0 to mean // (even though the latter has 5 digits precision, instead of 4). var standard_normal_table = [ /* z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 */ /* 0.0 */ 0.5000, 0.5040, 0.5080, 0.5120, 0.5160, 0.5199, 0.5239, 0.5279, 0.5319, 0.5359, /* 0.1 */ 0.5398, 0.5438, 0.5478, 0.5517, 0.5557, 0.5596, 0.5636, 0.5675, 0.5714, 0.5753, /* 0.2 */ 0.5793, 0.5832, 0.5871, 0.5910, 0.5948, 0.5987, 0.6026, 0.6064, 0.6103, 0.6141, /* 0.3 */ 0.6179, 0.6217, 0.6255, 0.6293, 0.6331, 0.6368, 0.6406, 0.6443, 0.6480, 0.6517, /* 0.4 */ 0.6554, 0.6591, 0.6628, 0.6664, 0.6700, 0.6736, 0.6772, 0.6808, 0.6844, 0.6879, /* 0.5 */ 0.6915, 0.6950, 0.6985, 0.7019, 0.7054, 0.7088, 0.7123, 0.7157, 0.7190, 0.7224, /* 0.6 */ 0.7257, 0.7291, 0.7324, 0.7357, 0.7389, 0.7422, 0.7454, 0.7486, 0.7517, 0.7549, /* 0.7 */ 0.7580, 0.7611, 0.7642, 0.7673, 0.7704, 0.7734, 0.7764, 0.7794, 0.7823, 0.7852, /* 0.8 */ 0.7881, 0.7910, 0.7939, 0.7967, 0.7995, 0.8023, 0.8051, 0.8078, 0.8106, 0.8133, /* 0.9 */ 0.8159, 0.8186, 0.8212, 0.8238, 0.8264, 0.8289, 0.8315, 0.8340, 0.8365, 0.8389, /* 1.0 */ 0.8413, 0.8438, 0.8461, 0.8485, 0.8508, 0.8531, 0.8554, 0.8577, 0.8599, 0.8621, /* 1.1 */ 0.8643, 0.8665, 0.8686, 0.8708, 0.8729, 0.8749, 0.8770, 0.8790, 0.8810, 0.8830, /* 1.2 */ 0.8849, 0.8869, 0.8888, 0.8907, 0.8925, 0.8944, 0.8962, 0.8980, 0.8997, 0.9015, /* 1.3 */ 0.9032, 0.9049, 0.9066, 0.9082, 0.9099, 0.9115, 0.9131, 0.9147, 0.9162, 0.9177, /* 1.4 */ 0.9192, 0.9207, 0.9222, 0.9236, 0.9251, 0.9265, 0.9279, 0.9292, 0.9306, 0.9319, /* 1.5 */ 0.9332, 0.9345, 0.9357, 0.9370, 0.9382, 0.9394, 0.9406, 0.9418, 0.9429, 0.9441, /* 1.6 */ 0.9452, 0.9463, 0.9474, 0.9484, 0.9495, 0.9505, 0.9515, 0.9525, 0.9535, 0.9545, /* 1.7 */ 0.9554, 0.9564, 0.9573, 0.9582, 0.9591, 0.9599, 0.9608, 0.9616, 0.9625, 0.9633, /* 1.8 */ 0.9641, 0.9649, 0.9656, 0.9664, 0.9671, 0.9678, 0.9686, 0.9693, 0.9699, 0.9706, /* 1.9 */ 0.9713, 0.9719, 0.9726, 0.9732, 0.9738, 0.9744, 0.9750, 0.9756, 0.9761, 0.9767, /* 2.0 */ 0.9772, 0.9778, 0.9783, 0.9788, 0.9793, 0.9798, 0.9803, 0.9808, 0.9812, 0.9817, /* 2.1 */ 0.9821, 0.9826, 0.9830, 0.9834, 0.9838, 0.9842, 0.9846, 0.9850, 0.9854, 0.9857, /* 2.2 */ 0.9861, 0.9864, 0.9868, 0.9871, 0.9875, 0.9878, 0.9881, 0.9884, 0.9887, 0.9890, /* 2.3 */ 0.9893, 0.9896, 0.9898, 0.9901, 0.9904, 0.9906, 0.9909, 0.9911, 0.9913, 0.9916, /* 2.4 */ 0.9918, 0.9920, 0.9922, 0.9925, 0.9927, 0.9929, 0.9931, 0.9932, 0.9934, 0.9936, /* 2.5 */ 0.9938, 0.9940, 0.9941, 0.9943, 0.9945, 0.9946, 0.9948, 0.9949, 0.9951, 0.9952, /* 2.6 */ 0.9953, 0.9955, 0.9956, 0.9957, 0.9959, 0.9960, 0.9961, 0.9962, 0.9963, 0.9964, /* 2.7 */ 0.9965, 0.9966, 0.9967, 0.9968, 0.9969, 0.9970, 0.9971, 0.9972, 0.9973, 0.9974, /* 2.8 */ 0.9974, 0.9975, 0.9976, 0.9977, 0.9977, 0.9978, 0.9979, 0.9979, 0.9980, 0.9981, /* 2.9 */ 0.9981, 0.9982, 0.9982, 0.9983, 0.9984, 0.9984, 0.9985, 0.9985, 0.9986, 0.9986, /* 3.0 */ 0.9987, 0.9987, 0.9987, 0.9988, 0.9988, 0.9989, 0.9989, 0.9989, 0.9990, 0.9990 ]; // # [Cumulative Standard Normal Probability](http://en.wikipedia.org/wiki/Standard_normal_table) // // Since probability tables cannot be // printed for every normal distribution, as there are an infinite variety // of normal distributions, it is common practice to convert a normal to a // standard normal and then use the standard normal table to find probabilities function cumulative_std_normal_probability(z) { // Calculate the position of this value. var absZ = Math.abs(z), // Each row begins with a different // significant digit: 0.5, 0.6, 0.7, and so on. So the row is simply // this value's significant digit: 0.567 will be in row 0, so row=0, // 0.643 will be in row 1, so row=10. row = Math.floor(absZ * 10), column = 10 * (Math.floor(absZ * 100) / 10 - Math.floor(absZ * 100 / 10)), index = Math.min((row * 10) + column, standard_normal_table.length - 1); // The index we calculate must be in the table as a positive value, // but we still pay attention to whether the input is positive // or negative, and flip the output value as a last step. if (z >= 0) { return standard_normal_table[index]; } else { // due to floating-point arithmetic, values in the table with // 4 significant figures can nevertheless end up as repeating // fractions when they're computed here. return +(1 - standard_normal_table[index]).toFixed(4); } } // # [Z-Score, or Standard Score](http://en.wikipedia.org/wiki/Standard_score) // // The standard score is the number of standard deviations an observation // or datum is above or below the mean. Thus, a positive standard score // represents a datum above the mean, while a negative standard score // represents a datum below the mean. It is a dimensionless quantity // obtained by subtracting the population mean from an individual raw // score and then dividing the difference by the population standard // deviation. // // The z-score is only defined if one knows the population parameters; // if one only has a sample set, then the analogous computation with // sample mean and sample standard deviation yields the // Student's t-statistic. function z_score(x, mean, standard_deviation) { return (x - mean) / standard_deviation; } // We use `ε`, epsilon, as a stopping criterion when we want to iterate // until we're "close enough". var epsilon = 0.0001; // # [Factorial](https://en.wikipedia.org/wiki/Factorial) // // A factorial, usually written n!, is the product of all positive // integers less than or equal to n. Often factorial is implemented // recursively, but this iterative approach is significantly faster // and simpler. function factorial(n) { // factorial is mathematically undefined for negative numbers if (n < 0 ) { return null; } // typically you'll expand the factorial function going down, like // 5! = 5 * 4 * 3 * 2 * 1. This is going in the opposite direction, // counting from 2 up to the number in question, and since anything // multiplied by 1 is itself, the loop only needs to start at 2. var accumulator = 1; for (var i = 2; i <= n; i++) { // for each number up to and including the number `n`, multiply // the accumulator my that number. accumulator *= i; } return accumulator; } // # Bernoulli Distribution // // The [Bernoulli distribution](http://en.wikipedia.org/wiki/Bernoulli_distribution) // is the probability discrete // distribution of a random variable which takes value 1 with success // probability `p` and value 0 with failure // probability `q` = 1 - `p`. It can be used, for example, to represent the // toss of a coin, where "1" is defined to mean "heads" and "0" is defined // to mean "tails" (or vice versa). It is // a special case of a Binomial Distribution // where `n` = 1. function bernoulli_distribution(p) { // Check that `p` is a valid probability (0 ≤ p ≤ 1) if (p < 0 || p > 1 ) { return null; } return binomial_distribution(1, p); } // # Binomial Distribution // // The [Binomial Distribution](http://en.wikipedia.org/wiki/Binomial_distribution) is the discrete probability // distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields // success with probability `probability`. Such a success/failure experiment is also called a Bernoulli experiment or // Bernoulli trial; when trials = 1, the Binomial Distribution is a Bernoulli Distribution. function binomial_distribution(trials, probability) { // Check that `p` is a valid probability (0 ≤ p ≤ 1), // that `n` is an integer, strictly positive. if (probability < 0 || probability > 1 || trials <= 0 || trials % 1 !== 0) { return null; } // a [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function) function probability_mass(x, trials, probability) { return factorial(trials) / (factorial(x) * factorial(trials - x)) * (Math.pow(probability, x) * Math.pow(1 - probability, trials - x)); } // We initialize `x`, the random variable, and `accumulator`, an accumulator // for the cumulative distribution function to 0. `distribution_functions` // is the object we'll return with the `probability_of_x` and the // `cumulative_probability_of_x`, as well as the calculated mean & // variance. We iterate until the `cumulative_probability_of_x` is // within `epsilon` of 1.0. var x = 0, cumulative_probability = 0, cells = {}; // This algorithm iterates through each potential outcome, // until the `cumulative_probability` is very close to 1, at // which point we've defined the vast majority of outcomes do { cells[x] = probability_mass(x, trials, probability); cumulative_probability += cells[x]; x++; // when the cumulative_probability is nearly 1, we've calculated // the useful range of this distribution } while (cumulative_probability < 1 - epsilon); return cells; } // # Poisson Distribution // // The [Poisson Distribution](http://en.wikipedia.org/wiki/Poisson_distribution) // is a discrete probability distribution that expresses the probability // of a given number of events occurring in a fixed interval of time // and/or space if these events occur with a known average rate and // independently of the time since the last event. // // The Poisson Distribution is characterized by the strictly positive // mean arrival or occurrence rate, `λ`. function poisson_distribution(lambda) { // Check that lambda is strictly positive if (lambda <= 0) { return null; } // our current place in the distribution var x = 0, // and we keep track of the current cumulative probability, in // order to know when to stop calculating chances. cumulative_probability = 0, // the calculated cells to be returned cells = {}; // a [probability mass function](https://en.wikipedia.org/wiki/Probability_mass_function) function probability_mass(x, lambda) { return (Math.pow(Math.E, -lambda) * Math.pow(lambda, x)) / factorial(x); } // This algorithm iterates through each potential outcome, // until the `cumulative_probability` is very close to 1, at // which point we've defined the vast majority of outcomes do { cells[x] = probability_mass(x, lambda); cumulative_probability += cells[x]; x++; // when the cumulative_probability is nearly 1, we've calculated // the useful range of this distribution } while (cumulative_probability < 1 - epsilon); return cells; } // # Percentage Points of the χ2 (Chi-Squared) Distribution // The [χ2 (Chi-Squared) Distribution](http://en.wikipedia.org/wiki/Chi-squared_distribution) is used in the common // chi-squared tests for goodness of fit of an observed distribution to a theoretical one, the independence of two // criteria of classification of qualitative data, and in confidence interval estimation for a population standard // deviation of a normal distribution from a sample standard deviation. // // Values from Appendix 1, Table III of William W. Hines & Douglas C. Montgomery, "Probability and Statistics in // Engineering and Management Science", Wiley (1980). var chi_squared_distribution_table = { 1: { 0.995: 0.00, 0.99: 0.00, 0.975: 0.00, 0.95: 0.00, 0.9: 0.02, 0.5: 0.45, 0.1: 2.71, 0.05: 3.84, 0.025: 5.02, 0.01: 6.63, 0.005: 7.88 }, 2: { 0.995: 0.01, 0.99: 0.02, 0.975: 0.05, 0.95: 0.10, 0.9: 0.21, 0.5: 1.39, 0.1: 4.61, 0.05: 5.99, 0.025: 7.38, 0.01: 9.21, 0.005: 10.60 }, 3: { 0.995: 0.07, 0.99: 0.11, 0.975: 0.22, 0.95: 0.35, 0.9: 0.58, 0.5: 2.37, 0.1: 6.25, 0.05: 7.81, 0.025: 9.35, 0.01: 11.34, 0.005: 12.84 }, 4: { 0.995: 0.21, 0.99: 0.30, 0.975: 0.48, 0.95: 0.71, 0.9: 1.06, 0.5: 3.36, 0.1: 7.78, 0.05: 9.49, 0.025: 11.14, 0.01: 13.28, 0.005: 14.86 }, 5: { 0.995: 0.41, 0.99: 0.55, 0.975: 0.83, 0.95: 1.15, 0.9: 1.61, 0.5: 4.35, 0.1: 9.24, 0.05: 11.07, 0.025: 12.83, 0.01: 15.09, 0.005: 16.75 }, 6: { 0.995: 0.68, 0.99: 0.87, 0.975: 1.24, 0.95: 1.64, 0.9: 2.20, 0.5: 5.35, 0.1: 10.65, 0.05: 12.59, 0.025: 14.45, 0.01: 16.81, 0.005: 18.55 }, 7: { 0.995: 0.99, 0.99: 1.25, 0.975: 1.69, 0.95: 2.17, 0.9: 2.83, 0.5: 6.35, 0.1: 12.02, 0.05: 14.07, 0.025: 16.01, 0.01: 18.48, 0.005: 20.28 }, 8: { 0.995: 1.34, 0.99: 1.65, 0.975: 2.18, 0.95: 2.73, 0.9: 3.49, 0.5: 7.34, 0.1: 13.36, 0.05: 15.51, 0.025: 17.53, 0.01: 20.09, 0.005: 21.96 }, 9: { 0.995: 1.73, 0.99: 2.09, 0.975: 2.70, 0.95: 3.33, 0.9: 4.17, 0.5: 8.34, 0.1: 14.68, 0.05: 16.92, 0.025: 19.02, 0.01: 21.67, 0.005: 23.59 }, 10: { 0.995: 2.16, 0.99: 2.56, 0.975: 3.25, 0.95: 3.94, 0.9: 4.87, 0.5: 9.34, 0.1: 15.99, 0.05: 18.31, 0.025: 20.48, 0.01: 23.21, 0.005: 25.19 }, 11: { 0.995: 2.60, 0.99: 3.05, 0.975: 3.82, 0.95: 4.57, 0.9: 5.58, 0.5: 10.34, 0.1: 17.28, 0.05: 19.68, 0.025: 21.92, 0.01: 24.72, 0.005: 26.76 }, 12: { 0.995: 3.07, 0.99: 3.57, 0.975: 4.40, 0.95: 5.23, 0.9: 6.30, 0.5: 11.34, 0.1: 18.55, 0.05: 21.03, 0.025: 23.34, 0.01: 26.22, 0.005: 28.30 }, 13: { 0.995: 3.57, 0.99: 4.11, 0.975: 5.01, 0.95: 5.89, 0.9: 7.04, 0.5: 12.34, 0.1: 19.81, 0.05: 22.36, 0.025: 24.74, 0.01: 27.69, 0.005: 29.82 }, 14: { 0.995: 4.07, 0.99: 4.66, 0.975: 5.63, 0.95: 6.57, 0.9: 7.79, 0.5: 13.34, 0.1: 21.06, 0.05: 23.68, 0.025: 26.12, 0.01: 29.14, 0.005: 31.32 }, 15: { 0.995: 4.60, 0.99: 5.23, 0.975: 6.27, 0.95: 7.26, 0.9: 8.55, 0.5: 14.34, 0.1: 22.31, 0.05: 25.00, 0.025: 27.49, 0.01: 30.58, 0.005: 32.80 }, 16: { 0.995: 5.14, 0.99: 5.81, 0.975: 6.91, 0.95: 7.96, 0.9: 9.31, 0.5: 15.34, 0.1: 23.54, 0.05: 26.30, 0.025: 28.85, 0.01: 32.00, 0.005: 34.27 }, 17: { 0.995: 5.70, 0.99: 6.41, 0.975: 7.56, 0.95: 8.67, 0.9: 10.09, 0.5: 16.34, 0.1: 24.77, 0.05: 27.59, 0.025: 30.19, 0.01: 33.41, 0.005: 35.72 }, 18: { 0.995: 6.26, 0.99: 7.01, 0.975: 8.23, 0.95: 9.39, 0.9: 10.87, 0.5: 17.34, 0.1: 25.99, 0.05: 28.87, 0.025: 31.53, 0.01: 34.81, 0.005: 37.16 }, 19: { 0.995: 6.84, 0.99: 7.63, 0.975: 8.91, 0.95: 10.12, 0.9: 11.65, 0.5: 18.34, 0.1: 27.20, 0.05: 30.14, 0.025: 32.85, 0.01: 36.19, 0.005: 38.58 }, 20: { 0.995: 7.43, 0.99: 8.26, 0.975: 9.59, 0.95: 10.85, 0.9: 12.44, 0.5: 19.34, 0.1: 28.41, 0.05: 31.41, 0.025: 34.17, 0.01: 37.57, 0.005: 40.00 }, 21: { 0.995: 8.03, 0.99: 8.90, 0.975: 10.28, 0.95: 11.59, 0.9: 13.24, 0.5: 20.34, 0.1: 29.62, 0.05: 32.67, 0.025: 35.48, 0.01: 38.93, 0.005: 41.40 }, 22: { 0.995: 8.64, 0.99: 9.54, 0.975: 10.98, 0.95: 12.34, 0.9: 14.04, 0.5: 21.34, 0.1: 30.81, 0.05: 33.92, 0.025: 36.78, 0.01: 40.29, 0.005: 42.80 }, 23: { 0.995: 9.26, 0.99: 10.20, 0.975: 11.69, 0.95: 13.09, 0.9: 14.85, 0.5: 22.34, 0.1: 32.01, 0.05: 35.17, 0.025: 38.08, 0.01: 41.64, 0.005: 44.18 }, 24: { 0.995: 9.89, 0.99: 10.86, 0.975: 12.40, 0.95: 13.85, 0.9: 15.66, 0.5: 23.34, 0.1: 33.20, 0.05: 36.42, 0.025: 39.36, 0.01: 42.98, 0.005: 45.56 }, 25: { 0.995: 10.52, 0.99: 11.52, 0.975: 13.12, 0.95: 14.61, 0.9: 16.47, 0.5: 24.34, 0.1: 34.28, 0.05: 37.65, 0.025: 40.65, 0.01: 44.31, 0.005: 46.93 }, 26: { 0.995: 11.16, 0.99: 12.20, 0.975: 13.84, 0.95: 15.38, 0.9: 17.29, 0.5: 25.34, 0.1: 35.56, 0.05: 38.89, 0.025: 41.92, 0.01: 45.64, 0.005: 48.29 }, 27: { 0.995: 11.81, 0.99: 12.88, 0.975: 14.57, 0.95: 16.15, 0.9: 18.11, 0.5: 26.34, 0.1: 36.74, 0.05: 40.11, 0.025: 43.19, 0.01: 46.96, 0.005: 49.65 }, 28: { 0.995: 12.46, 0.99: 13.57, 0.975: 15.31, 0.95: 16.93, 0.9: 18.94, 0.5: 27.34, 0.1: 37.92, 0.05: 41.34, 0.025: 44.46, 0.01: 48.28, 0.005: 50.99 }, 29: { 0.995: 13.12, 0.99: 14.26, 0.975: 16.05, 0.95: 17.71, 0.9: 19.77, 0.5: 28.34, 0.1: 39.09, 0.05: 42.56, 0.025: 45.72, 0.01: 49.59, 0.005: 52.34 }, 30: { 0.995: 13.79, 0.99: 14.95, 0.975: 16.79, 0.95: 18.49, 0.9: 20.60, 0.5: 29.34, 0.1: 40.26, 0.05: 43.77, 0.025: 46.98, 0.01: 50.89, 0.005: 53.67 }, 40: { 0.995: 20.71, 0.99: 22.16, 0.975: 24.43, 0.95: 26.51, 0.9: 29.05, 0.5: 39.34, 0.1: 51.81, 0.05: 55.76, 0.025: 59.34, 0.01: 63.69, 0.005: 66.77 }, 50: { 0.995: 27.99, 0.99: 29.71, 0.975: 32.36, 0.95: 34.76, 0.9: 37.69, 0.5: 49.33, 0.1: 63.17, 0.05: 67.50, 0.025: 71.42, 0.01: 76.15, 0.005: 79.49 }, 60: { 0.995: 35.53, 0.99: 37.48, 0.975: 40.48, 0.95: 43.19, 0.9: 46.46, 0.5: 59.33, 0.1: 74.40, 0.05: 79.08, 0.025: 83.30, 0.01: 88.38, 0.005: 91.95 }, 70: { 0.995: 43.28, 0.99: 45.44, 0.975: 48.76, 0.95: 51.74, 0.9: 55.33, 0.5: 69.33, 0.1: 85.53, 0.05: 90.53, 0.025: 95.02, 0.01: 100.42, 0.005: 104.22 }, 80: { 0.995: 51.17, 0.99: 53.54, 0.975: 57.15, 0.95: 60.39, 0.9: 64.28, 0.5: 79.33, 0.1: 96.58, 0.05: 101.88, 0.025: 106.63, 0.01: 112.33, 0.005: 116.32 }, 90: { 0.995: 59.20, 0.99: 61.75, 0.975: 65.65, 0.95: 69.13, 0.9: 73.29, 0.5: 89.33, 0.1: 107.57, 0.05: 113.14, 0.025: 118.14, 0.01: 124.12, 0.005: 128.30 }, 100: { 0.995: 67.33, 0.99: 70.06, 0.975: 74.22, 0.95: 77.93, 0.9: 82.36, 0.5: 99.33, 0.1: 118.50, 0.05: 124.34, 0.025: 129.56, 0.01: 135.81, 0.005: 140.17 } }; // # χ2 (Chi-Squared) Goodness-of-Fit Test // // The [χ2 (Chi-Squared) Goodness-of-Fit Test](http://en.wikipedia.org/wiki/Goodness_of_fit#Pearson.27s_chi-squared_test) // uses a measure of goodness of fit which is the sum of differences between observed and expected outcome frequencies // (that is, counts of observations), each squared and divided by the number of observations expected given the // hypothesized distribution. The resulting χ2 statistic, `chi_squared`, can be compared to the chi-squared distribution // to determine the goodness of fit. In order to determine the degrees of freedom of the chi-squared distribution, one // takes the total number of observed frequencies and subtracts the number of estimated parameters. The test statistic // follows, approximately, a chi-square distribution with (k − c) degrees of freedom where `k` is the number of non-empty // cells and `c` is the number of estimated parameters for the distribution. function chi_squared_goodness_of_fit(data, distribution_type, significance) { // Estimate from the sample data, a weighted mean. var input_mean = mean(data), // Calculated value of the χ2 statistic. chi_squared = 0, // Degrees of freedom, calculated as (number of class intervals - // number of hypothesized distribution parameters estimated - 1) degrees_of_freedom, // Number of hypothesized distribution parameters estimated, expected to be supplied in the distribution test. // Lose one degree of freedom for estimating `lambda` from the sample data. c = 1, // The hypothesized distribution. // Generate the hypothesized distribution. hypothesized_distribution = distribution_type(input_mean), observed_frequencies = [], expected_frequencies = [], k; // Create an array holding a histogram from the sample data, of // the form `{ value: numberOfOcurrences }` for (var i = 0; i < data.length; i++) { if (observed_frequencies[data[i]] === undefined) { observed_frequencies[data[i]] = 0; } observed_frequencies[data[i]]++; } // The histogram we created might be sparse - there might be gaps // between values. So we iterate through the histogram, making // sure that instead of undefined, gaps have 0 values. for (i = 0; i < observed_frequencies.length; i++) { Iif (observed_frequencies[i] === undefined) { observed_frequencies[i] = 0; } } // Create an array holding a histogram of expected data given the // sample size and hypothesized distribution. for (k in hypothesized_distribution) { if (k in observed_frequencies) { expected_frequencies[k] = hypothesized_distribution[k] * data.length; } } // Working backward through the expected frequencies, collapse classes // if less than three observations are expected for a class. // This transformation is applied to the observed frequencies as well. for (k = expected_frequencies.length - 1; k >= 0; k--) { if (expected_frequencies[k] < 3) { expected_frequencies[k - 1] += expected_frequencies[k]; expected_frequencies.pop(); observed_frequencies[k - 1] += observed_frequencies[k]; observed_frequencies.pop(); } } // Iterate through the squared differences between observed & expected // frequencies, accumulating the `chi_squared` statistic. for (k = 0; k < observed_frequencies.length; k++) { chi_squared += Math.pow( observed_frequencies[k] - expected_frequencies[k], 2) / expected_frequencies[k]; } // Calculate degrees of freedom for this test and look it up in the // `chi_squared_distribution_table` in order to // accept or reject the goodness-of-fit of the hypothesized distribution. degrees_of_freedom = observed_frequencies.length - c - 1; return chi_squared_distribution_table[degrees_of_freedom][significance] < chi_squared; } // # Mixin // // Mixin simple_statistics to a single Array instance if provided // or the Array native object if not. This is an optional // feature that lets you treat simple_statistics as a native feature // of Javascript. function mixin(array) { var support = !!(Object.defineProperty && Object.defineProperties); Iif (!support) throw new Error('without defineProperty, simple-statistics cannot be mixed in'); // only methods which work on basic arrays in a single step // are supported var arrayMethods = ['median', 'standard_deviation', 'sum', 'sample_skewness', 'mean', 'min', 'max', 'quantile', 'geometric_mean', 'harmonic_mean']; // create a closure with a method name so that a reference // like `arrayMethods[i]` doesn't follow the loop increment function wrap(method) { return function() { // cast any arguments into an array, since they're // natively objects var args = Array.prototype.slice.apply(arguments); // make the first argument the array itself args.unshift(this); // return the result of the ss method return ss[method].apply(ss, args); }; } // select object to extend var extending; if (array) { // create a shallow copy of the array so that our internal // operations do not change it by reference extending = array.slice(); } else { extending = Array.prototype; } // for each array function, define a function that gets // the array as the first argument. // We use [defineProperty](https://developer.mozilla.org/en-US/docs/JavaScript/Reference/Global_Objects/Object/defineProperty) // because it allows these properties to be non-enumerable: // `for (var in x)` loops will not run into problems with this // implementation. for (var i = 0; i < arrayMethods.length; i++) { Object.defineProperty(extending, arrayMethods[i], { value: wrap(arrayMethods[i]), configurable: true, enumerable: false, writable: true }); } return extending; } ss.linear_regression = linear_regression; ss.standard_deviation = standard_deviation; ss.r_squared = r_squared; ss.median = median; ss.mean = mean; ss.mode = mode; ss.min = min; ss.max = max; ss.sum = sum; ss.quantile = quantile; ss.quantile_sorted = quantile_sorted; ss.iqr = iqr; ss.mad = mad; ss.chunk = chunk; ss.shuffle = shuffle; ss.shuffle_in_place = shuffle_in_place; ss.sample_covariance = sample_covariance; ss.sample_correlation = sample_correlation; ss.sample_variance = sample_variance; ss.sample_standard_deviation = sample_standard_deviation; ss.sample_skewness = sample_skewness; ss.geometric_mean = geometric_mean; ss.harmonic_mean = harmonic_mean; ss.variance = variance; ss.t_test = t_test; ss.t_test_two_sample = t_test_two_sample; // jenks ss.jenksMatrices = jenksMatrices; ss.jenksBreaks = jenksBreaks; ss.jenks = jenks; ss.bayesian = bayesian; // Distribution-related methods ss.epsilon = epsilon; // We make ε available to the test suite. ss.factorial = factorial; ss.bernoulli_distribution = bernoulli_distribution; ss.binomial_distribution = binomial_distribution; ss.poisson_distribution = poisson_distribution; ss.chi_squared_goodness_of_fit = chi_squared_goodness_of_fit; // Normal distribution ss.z_score = z_score; ss.cumulative_std_normal_probability = cumulative_std_normal_probability; ss.standard_normal_table = standard_normal_table; // Alias this into its common name ss.average = mean; ss.interquartile_range = iqr; ss.mixin = mixin; ss.median_absolute_deviation = mad; })(this); |