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"babel-jest": "^22.4.1", "regenerator-runtime": "^0.11.1" + }, + "jest": { + "testURL": "http://localhost/" } } From 752f01fcfcf8036d016572f027f8ae05daa6ad27 Mon Sep 17 00:00:00 2001 From: Sebastian Sastoque Date: Fri, 15 Mar 2019 15:15:35 +0100 Subject: [PATCH 2/4] Modify rollup to include the banner at compiling --- rollup.config.js | 1 + 1 file changed, 1 insertion(+) diff --git a/rollup.config.js b/rollup.config.js index 48eac25..a6b2525 100644 --- a/rollup.config.js +++ b/rollup.config.js @@ -36,6 +36,7 @@ export default { }) ], output: { + banner: banner, name: 'powercalculator', file: 'dist/powercalculator.js', format: 'umd', From efa4c0db52e878d2ef66dc9784dad950bd964215 Mon Sep 17 00:00:00 2001 From: Sebastian Sastoque Date: Fri, 15 Mar 2019 15:18:34 +0100 Subject: [PATCH 3/4] Add multivariant option to power calculator Addind multivariant support for the power calculator by using the Sidak's correction. In the interface the users can select the number of variants and if they want to compare all variance with base or just one variant --- dist/powercalculator.css | 391 +- dist/powercalculator.js | 13174 +++++++++--------- src/components/impact-comp.vue | 1 + src/components/non-inferiority-comp.vue | 47 +- src/components/pc-block-field.vue | 13 +- src/components/sample-comp.vue | 20 +- src/js/math.js | 131 +- src/powercalculator.vue | 224 +- src/store/actions.js | 68 +- src/store/getters/data-format.js | 5 +- src/store/getters/math.js | 3 +- src/store/modules/attributes.js | 5 +- src/store/modules/non-inferiority.js | 46 + tests/effectsize.test.js | 26 +- tests/power.test.js | 18 +- tests/samplesize.test.js | 18 +- tests/samplesizefornoninferiority.test.js | 20 +- tests/store/gTest-impact.js | 208 +- tests/store/gTest-sample.js | 4 +- tests/store/non-inferiority-gTest-sample.js | 4 +- tests/store/non-inferiority-tTest-sample.js | 4 +- tests/store/tTest-impact.js | 4 +- tests/store/tTest-sample.js | 4 +- 23 files changed, 7531 insertions(+), 6907 deletions(-) diff --git a/dist/powercalculator.css b/dist/powercalculator.css index 7a88fc4..512fd00 100644 --- a/dist/powercalculator.css +++ b/dist/powercalculator.css @@ -21,22 +21,47 @@ /* layout */ .pc-main-header { display: grid; - grid-template-columns: min-content min-content auto min-content min-content; + grid-template-columns: 33.33% 33.33% 33.33%; grid-template-rows: auto; grid-template-areas: - "test-type calc-options title false-positive power"; + "controls-left title controls-right"; align-items: center; - margin: 25px 0 25px 10px; + margin: 25px 10px; +} +.pc-controls-left { + grid-area: controls-left; + display: grid; + grid-template-columns: min-content min-content min-content min-content; + grid-template-rows: auto; + grid-template-areas: + "test-type traffic comparison calc-options"; + align-items: center; +} +.pc-controls-right { + grid-area: controls-right; + display: grid; + grid-template-columns: auto min-content min-content; + grid-template-rows: auto; + grid-template-areas: + "variants false-positive power"; + align-items: center; + justify-items: end; } .pc-title { grid-area: title; font-size: 30px; text-align: center; } +.pc-traffic-mode { + grid-area: traffic; +} .pc-non-inf-label, .pc-test-type, .pc-false-positive, -.pc-power { +.pc-power, +.pc-traffic-mode, +.pc-variants, +.pc-comparison-mode { font-size: 0.8em; } .pc-test-type { @@ -45,21 +70,41 @@ .pc-non-inferiority { grid-area: calc-options; } +.pc-comparison-mode { + grid-area: comparison; +} +.pc-test-type-labels, +.pc-traffic-mode-labels, +.pc-non-inf-label, +.pc-comparison-mode-label { + white-space: nowrap; +} +.pc-non-inferiority , +.pc-test-type, +.pc-traffic-mode, +.pc-comparison-mode { + margin-left: 15px; +} .pc-test-type-tooltip-wrapper { display: inline-block; } -.pc-test-type-labels { - margin: 0 20px; +.pc-variants { + grid-area: variants; white-space: nowrap; + align-self: end; } .pc-false-positive { grid-area: false-positive; + margin-left: 15px; white-space: nowrap; + align-self: end; } .pc-power { grid-area: power; - margin-left: 10px; + margin-left: 15px; + margin-right: 15px; white-space: nowrap; + align-self: end; } .pc-blocks-wrapper { grid-area: pc-blocks-wrapper; @@ -117,54 +162,11 @@ .pc-block-to-calculate .pc-header { background: var(--dark-yellow); } - - -/* components/svg-graph.vue */ - -/* graph radio styles */ -.pc-graph-controls { - display: flex; - flex-direction: row; - padding: 30px 0 0 60px; - margin-bottom: 5px; - border-bottom: 1px solid var(--blue); -} -.pc-graph-radio-label { - position: relative; - font-size: 13px; - color: var(--blue); - margin-right: 20px; -} -.pc-graph-radio-input { - position: absolute; - visibility: hidden; -} -.pc-graph-radio-text { - display: block; - padding: 0 5px 15px; - position: relative; -} -.pc-graph-radio-input:checked + .pc-graph-radio-text::after { - content: ''; - position: absolute; - left: 0; - bottom: 0; - width: 100%; - height: 4px; - background: var(--blue); -} -/* graph layout */ -.pc-graph { - padding: 10px; - width: 100%; - box-sizing: border-box; +.pc-hidden { + display: none!important; } -/* components/svg-graph-tab-item.vue */ - -/*# sourceMappingURL=svg-graph-tab-item.vue.map */ - /* components/pc-block-field.vue */ .pc-value-field-wrapper { @@ -203,7 +205,8 @@ } .pc-non-inf-treshold-input, .pc-power-input, -.pc-false-positive-input { +.pc-false-positive-input, +.pc-variants-input { display: inline-block; vertical-align: middle; padding: 4px 8px; @@ -213,6 +216,9 @@ border-radius: 8px; font-size: inherit; } +.pc-variants-input { + width: 6.5em; +} .pc-top-fields-error { color: var(--red); } @@ -239,6 +245,141 @@ } +/* components/svg-graph.vue */ + +/* graph radio styles */ +.pc-graph-controls { + display: flex; + flex-direction: row; + padding: 30px 0 0 60px; + margin-bottom: 5px; + border-bottom: 1px solid var(--blue); +} +.pc-graph-radio-label { + position: relative; + font-size: 13px; + color: var(--blue); + margin-right: 20px; +} +.pc-graph-radio-input { + position: absolute; + visibility: hidden; +} +.pc-graph-radio-text { + display: block; + padding: 0 5px 15px; + position: relative; +} +.pc-graph-radio-input:checked + .pc-graph-radio-text::after { + content: ''; + position: absolute; + left: 0; + bottom: 0; + width: 100%; + height: 4px; + background: var(--blue); +} +/* graph layout */ +.pc-graph { + padding: 10px; + width: 100%; + box-sizing: border-box; +} + + +/* components/impact-comp.vue */ + +/*# sourceMappingURL=impact-comp.vue.map */ + +/* components/pc-tooltip.vue */ + +.pc-tooltip { + --tooltip-background-color: var(--black); + position: relative; +} +.tooltip-wrapper { + position: absolute; + left: 0; + top: calc(100% + 10px); + background: var(--tooltip-background-color); + color: var(--light-gray); + padding: 5px; + border-radius: 4px; + z-index: 10; + width: 400px; + white-space: normal; + pointer-events: none; +} +.tooltip-wrapper:after { + --size: 5px; + content: ''; + position: absolute; + left: 50px; + bottom: calc(100% - var(--size)); + transform: translate(-50%, 0) rotateZ(45deg); + border: var(--size) solid var(--tooltip-background-color); + border-right-color: transparent; + border-bottom-color: transparent; +} + + +/* components/non-inferiority-comp.vue */ + +.pc-non-inf-select { + --base-padding: 5px; + font-size: inherit; + line-height: 28px; + border: none; + display: block; + position: relative; + box-sizing: border-box; + width: 100%; + filter: drop-shadow(0 4px 2px rgba(0,0,0,0.1)); + border-radius: 5px; + background: var(--white); + padding: var(--base-padding); + overflow: hidden; + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; +} +.pc-non-inf-select:focus { + outline: 0; + box-shadow: inset 0 0 0 1px var(--dark-blue); +} +.pc-non-inf-select-wrapper { + position: relative; +} +.pc-non-inf-select-wrapper:after { + --border-size: 7px; + content: ''; + position: absolute; + right: 5px; + bottom: 0; + pointer-events: none; + border: var(--border-size) solid transparent; + border-top: var(--border-size) solid var(--gray); + transform: translateY(calc(-50% - var(--border-size)/2)); +} +.no-sub-title { + margin: 15px 0 10px 0; +} + + +/* components/non-inferiority.vue */ + +.pc-non-inf-label { + white-space: nowrap; +} +.pc-non-inf-treshold { + display: flex; + align-items: center; +} +.pc-non-inf-treshold-input { + margin-left: 5px; +} + + /* components/sample-comp.vue */ .pc-inputs[data-v-297fe272] { @@ -269,6 +410,9 @@ .pc-value-field--lockable.pc-value-field--locked .pc-value-field-wrapper[data-v-297fe272] { background: linear-gradient(0deg, var(--light-gray) 0%, var(--white) 100%); } +.pc-inputs-no-grid[data-v-297fe272] { + display: block; +} /* components/sample-comp.vue */ @@ -296,6 +440,32 @@ } +/* components/base-comp.vue */ + +.pc-input-left[data-v-0d0bef2b] { + position: relative; + z-index: 2; +} + + +/* components/base-comp.vue */ + +.pc-input-sd-rate { + margin-top: -10px; + z-index: 1; + position: relative; + text-align: center; +} +.pc-field-sdRate { + width: 90%; + display: inline-block; +} + + +/* components/svg-graph-tab-item.vue */ + +/*# sourceMappingURL=svg-graph-tab-item.vue.map */ + /* components/pc-block.vue */ .pc-block { @@ -391,118 +561,3 @@ z-index: 0; } - -/* components/impact-comp.vue */ - -/*# sourceMappingURL=impact-comp.vue.map */ - -/* components/base-comp.vue */ - -.pc-input-left[data-v-0d0bef2b] { - position: relative; - z-index: 2; -} - - -/* components/base-comp.vue */ - -.pc-input-sd-rate { - margin-top: -10px; - z-index: 1; - position: relative; - text-align: center; -} -.pc-field-sdRate { - width: 90%; - display: inline-block; -} - - -/* components/pc-tooltip.vue */ - -.pc-tooltip { - --tooltip-background-color: var(--black); - position: relative; -} -.tooltip-wrapper { - position: absolute; - left: 0; - top: calc(100% + 10px); - background: var(--tooltip-background-color); - color: var(--light-gray); - padding: 5px; - border-radius: 4px; - z-index: 10; - width: 400px; - white-space: normal; - pointer-events: none; -} -.tooltip-wrapper:after { - --size: 5px; - content: ''; - position: absolute; - left: 50px; - bottom: calc(100% - var(--size)); - transform: translate(-50%, 0) rotateZ(45deg); - border: var(--size) solid var(--tooltip-background-color); - border-right-color: transparent; - border-bottom-color: transparent; -} - - -/* components/non-inferiority.vue */ - -.pc-non-inf-label { - white-space: nowrap; -} -.pc-non-inf-treshold { - display: flex; - align-items: center; -} -.pc-non-inf-treshold-input { - margin-left: 5px; -} - - -/* components/non-inferiority-comp.vue */ - -.pc-non-inf-select { - --base-padding: 5px; - font-size: inherit; - line-height: 28px; - border: none; - display: block; - position: relative; - box-sizing: border-box; - width: 100%; - filter: drop-shadow(0 4px 2px rgba(0,0,0,0.1)); - border-radius: 5px; - background: var(--white); - padding: var(--base-padding); - overflow: hidden; - -webkit-appearance: none; - -moz-appearance: none; - appearance: none; -} -.pc-non-inf-select:focus { - outline: 0; - box-shadow: inset 0 0 0 1px var(--dark-blue); -} -.pc-non-inf-select-wrapper { - position: relative; -} -.pc-non-inf-select-wrapper:after { - --border-size: 7px; - content: ''; - position: absolute; - right: 5px; - bottom: 0; - pointer-events: none; - border: var(--border-size) solid transparent; - border-top: var(--border-size) solid var(--gray); - transform: translateY(calc(-50% - var(--border-size)/2)); -} -.no-sub-title { - margin: 15px 0 10px 0; -} - diff --git a/dist/powercalculator.js b/dist/powercalculator.js index 285c9ef..86ddaba 100644 --- a/dist/powercalculator.js +++ b/dist/powercalculator.js @@ -3,5444 +3,5402 @@ /** https://github.com/bookingcom/powercalculator */ (function (global, factory) { - typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : - typeof define === 'function' && define.amd ? define('powercalculator', factory) : - (global.powercalculator = factory()); -}(this, (function () { 'use strict'; + typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : + typeof define === 'function' && define.amd ? define('powercalculator', factory) : + (global = global || self, global.powercalculator = factory()); +}(this, function () { 'use strict'; -var _impact = { - getGraphYTicks () { - let impact = isNaN(this.impact) ? 0 : this.impact, - arr = [impact/1.50, impact/1.25, impact, impact*1.25, impact*1.50]; - return arr - }, - getGraphYTicksFormatted (y) { - let num = window.parseFloat(y); - if ((num % 1) !== 0) { - num = num.toFixed(2); - } + var _impact = { + getGraphYTicks () { + let impact = isNaN(this.impact) ? 0 : this.impact, + arr = [impact/1.50, impact/1.25, impact, impact*1.25, impact*1.50]; + return arr + }, + getGraphYTicksFormatted (y) { + let num = window.parseFloat(y); + if ((num % 1) !== 0) { + num = num.toFixed(2); + } - if (isNaN(num)) { - num = 0; - } + if (isNaN(num)) { + num = 0; + } - return `${num}%` - }, - updateClonedValues (clonedObj, value) { - clonedObj.effect_size = this.$store.getters.extractValue('impact', value); + return `${num}%` + }, + updateClonedValues (clonedObj, value) { + clonedObj.effect_size = this.$store.getters.extractValue('impact', value); - return clonedObj; - }, - getCurrentYValue () { - return this.impact - }, - getGraphXTicksFormatted (x) { - let result = x; + return clonedObj; + }, + getCurrentYValue () { + return this.impact + }, + getGraphXTicksFormatted (x) { + let result = x; - result += '%'; + result += '%'; - return result - }, -}; + return result + }, + }; -var commonjsGlobal = typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {}; + var commonjsGlobal = typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {}; + function createCommonjsModule(fn, module) { + return module = { exports: {} }, fn(module, module.exports), module.exports; + } + var jstat = createCommonjsModule(function (module, exports) { + (function (window, factory) { + { + module.exports = factory(); + } + })(commonjsGlobal, function () { + var jStat = (function(Math, undefined$1) { + // For quick reference. + var concat = Array.prototype.concat; + var slice = Array.prototype.slice; + var toString = Object.prototype.toString; + // Calculate correction for IEEE error + // TODO: This calculation can be improved. + function calcRdx(n, m) { + var val = n > m ? n : m; + return Math.pow(10, + 17 - ~~(Math.log(((val > 0) ? val : -val)) * Math.LOG10E)); + } -function createCommonjsModule(fn, module) { - return module = { exports: {} }, fn(module, module.exports), module.exports; -} -var jstat = createCommonjsModule(function (module, exports) { -(function (window, factory) { - { - module.exports = factory(); - } -})(commonjsGlobal, function () { -var jStat = (function(Math, undefined) { + var isArray = Array.isArray || function isArray(arg) { + return toString.call(arg) === '[object Array]'; + }; -// For quick reference. -var concat = Array.prototype.concat; -var slice = Array.prototype.slice; -var toString = Object.prototype.toString; -// Calculate correction for IEEE error -// TODO: This calculation can be improved. -function calcRdx(n, m) { - var val = n > m ? n : m; - return Math.pow(10, - 17 - ~~(Math.log(((val > 0) ? val : -val)) * Math.LOG10E)); -} + function isFunction(arg) { + return toString.call(arg) === '[object Function]'; + } -var isArray = Array.isArray || function isArray(arg) { - return toString.call(arg) === '[object Array]'; -}; + function isNumber(arg) { + return typeof arg === 'number' && arg === arg; + } -function isFunction(arg) { - return toString.call(arg) === '[object Function]'; -} + // Converts the jStat matrix to vector. + function toVector(arr) { + return concat.apply([], arr); + } -function isNumber(arg) { - return typeof arg === 'number' && arg === arg; -} + // The one and only jStat constructor. + function jStat() { + return new jStat._init(arguments); + } -// Converts the jStat matrix to vector. -function toVector(arr) { - return concat.apply([], arr); -} + // TODO: Remove after all references in src files have been removed. + jStat.fn = jStat.prototype; -// The one and only jStat constructor. -function jStat() { - return new jStat._init(arguments); -} + // By separating the initializer from the constructor it's easier to handle + // always returning a new instance whether "new" was used or not. + jStat._init = function _init(args) { + var i; + // If first argument is an array, must be vector or matrix. + if (isArray(args[0])) { + // Check if matrix. + if (isArray(args[0][0])) { + // See if a mapping function was also passed. + if (isFunction(args[1])) + args[0] = jStat.map(args[0], args[1]); + // Iterate over each is faster than this.push.apply(this, args[0]. + for (var i = 0; i < args[0].length; i++) + this[i] = args[0][i]; + this.length = args[0].length; -// TODO: Remove after all references in src files have been removed. -jStat.fn = jStat.prototype; + // Otherwise must be a vector. + } else { + this[0] = isFunction(args[1]) ? jStat.map(args[0], args[1]) : args[0]; + this.length = 1; + } + // If first argument is number, assume creation of sequence. + } else if (isNumber(args[0])) { + this[0] = jStat.seq.apply(null, args); + this.length = 1; -// By separating the initializer from the constructor it's easier to handle -// always returning a new instance whether "new" was used or not. -jStat._init = function _init(args) { - var i; + // Handle case when jStat object is passed to jStat. + } else if (args[0] instanceof jStat) { + // Duplicate the object and pass it back. + return jStat(args[0].toArray()); - // If first argument is an array, must be vector or matrix. - if (isArray(args[0])) { - // Check if matrix. - if (isArray(args[0][0])) { - // See if a mapping function was also passed. - if (isFunction(args[1])) - args[0] = jStat.map(args[0], args[1]); - // Iterate over each is faster than this.push.apply(this, args[0]. - for (var i = 0; i < args[0].length; i++) - this[i] = args[0][i]; - this.length = args[0].length; + // Unexpected argument value, return empty jStat object. + // TODO: This is strange behavior. Shouldn't this throw or some such to let + // the user know they had bad arguments? + } else { + this[0] = []; + this.length = 1; + } - // Otherwise must be a vector. - } else { - this[0] = isFunction(args[1]) ? jStat.map(args[0], args[1]) : args[0]; - this.length = 1; - } + return this; + }; + jStat._init.prototype = jStat.prototype; + jStat._init.constructor = jStat; + + + // Utility functions. + // TODO: for internal use only? + jStat.utils = { + calcRdx: calcRdx, + isArray: isArray, + isFunction: isFunction, + isNumber: isNumber, + toVector: toVector + }; - // If first argument is number, assume creation of sequence. - } else if (isNumber(args[0])) { - this[0] = jStat.seq.apply(null, args); - this.length = 1; - - // Handle case when jStat object is passed to jStat. - } else if (args[0] instanceof jStat) { - // Duplicate the object and pass it back. - return jStat(args[0].toArray()); - - // Unexpected argument value, return empty jStat object. - // TODO: This is strange behavior. Shouldn't this throw or some such to let - // the user know they had bad arguments? - } else { - this[0] = []; - this.length = 1; - } - - return this; -}; -jStat._init.prototype = jStat.prototype; -jStat._init.constructor = jStat; - - -// Utility functions. -// TODO: for internal use only? -jStat.utils = { - calcRdx: calcRdx, - isArray: isArray, - isFunction: isFunction, - isNumber: isNumber, - toVector: toVector -}; - - -// Easily extend the jStat object. -// TODO: is this seriously necessary? -jStat.extend = function extend(obj) { - var i, j; - - if (arguments.length === 1) { - for (j in obj) - jStat[j] = obj[j]; - return this; - } - - for (var i = 1; i < arguments.length; i++) { - for (j in arguments[i]) - obj[j] = arguments[i][j]; - } - - return obj; -}; - - -// Returns the number of rows in the matrix. -jStat.rows = function rows(arr) { - return arr.length || 1; -}; - - -// Returns the number of columns in the matrix. -jStat.cols = function cols(arr) { - return arr[0].length || 1; -}; - - -// Returns the dimensions of the object { rows: i, cols: j } -jStat.dimensions = function dimensions(arr) { - return { - rows: jStat.rows(arr), - cols: jStat.cols(arr) - }; -}; - - -// Returns a specified row as a vector or return a sub matrix by pick some rows -jStat.row = function row(arr, index) { - if (isArray(index)) { - return index.map(function(i) { - return jStat.row(arr, i); - }) - } - return arr[index]; -}; + // Easily extend the jStat object. + // TODO: is this seriously necessary? + jStat.extend = function extend(obj) { + var i, j; -// return row as array -// rowa([[1,2],[3,4]],0) -> [1,2] -jStat.rowa = function rowa(arr, i) { - return jStat.row(arr, i); -}; + if (arguments.length === 1) { + for (j in obj) + jStat[j] = obj[j]; + return this; + } + for (var i = 1; i < arguments.length; i++) { + for (j in arguments[i]) + obj[j] = arguments[i][j]; + } -// Returns the specified column as a vector or return a sub matrix by pick some -// columns -jStat.col = function col(arr, index) { - if (isArray(index)) { - var submat = jStat.arange(arr.length).map(function(i) { - return new Array(index.length); - }); - index.forEach(function(ind, i){ - jStat.arange(arr.length).forEach(function(j) { - submat[j][i] = arr[j][ind]; - }); - }); - return submat; - } - var column = new Array(arr.length); - for (var i = 0; i < arr.length; i++) - column[i] = [arr[i][index]]; - return column; -}; - - -// return column as array -// cola([[1,2],[3,4]],0) -> [1,3] -jStat.cola = function cola(arr, i) { - return jStat.col(arr, i).map(function(a){ return a[0] }); -}; - - -// Returns the diagonal of the matrix -jStat.diag = function diag(arr) { - var nrow = jStat.rows(arr); - var res = new Array(nrow); - for (var row = 0; row < nrow; row++) - res[row] = [arr[row][row]]; - return res; -}; - - -// Returns the anti-diagonal of the matrix -jStat.antidiag = function antidiag(arr) { - var nrow = jStat.rows(arr) - 1; - var res = new Array(nrow); - for (var i = 0; nrow >= 0; nrow--, i++) - res[i] = [arr[i][nrow]]; - return res; -}; - -// Transpose a matrix or array. -jStat.transpose = function transpose(arr) { - var obj = []; - var objArr, rows, cols, j, i; - - // Make sure arr is in matrix format. - if (!isArray(arr[0])) - arr = [arr]; - - rows = arr.length; - cols = arr[0].length; + return obj; + }; - for (var i = 0; i < cols; i++) { - objArr = new Array(rows); - for (j = 0; j < rows; j++) - objArr[j] = arr[j][i]; - obj.push(objArr); - } - // If obj is vector, return only single array. - return obj.length === 1 ? obj[0] : obj; -}; + // Returns the number of rows in the matrix. + jStat.rows = function rows(arr) { + return arr.length || 1; + }; -// Map a function to an array or array of arrays. -// "toAlter" is an internal variable. -jStat.map = function map(arr, func, toAlter) { - var row, nrow, ncol, res, col; + // Returns the number of columns in the matrix. + jStat.cols = function cols(arr) { + return arr[0].length || 1; + }; - if (!isArray(arr[0])) - arr = [arr]; - nrow = arr.length; - ncol = arr[0].length; - res = toAlter ? arr : new Array(nrow); + // Returns the dimensions of the object { rows: i, cols: j } + jStat.dimensions = function dimensions(arr) { + return { + rows: jStat.rows(arr), + cols: jStat.cols(arr) + }; + }; - for (row = 0; row < nrow; row++) { - // if the row doesn't exist, create it - if (!res[row]) - res[row] = new Array(ncol); - for (col = 0; col < ncol; col++) - res[row][col] = func(arr[row][col], row, col); - } - return res.length === 1 ? res[0] : res; -}; + // Returns a specified row as a vector or return a sub matrix by pick some rows + jStat.row = function row(arr, index) { + if (isArray(index)) { + return index.map(function(i) { + return jStat.row(arr, i); + }) + } + return arr[index]; + }; -// Cumulatively combine the elements of an array or array of arrays using a function. -jStat.cumreduce = function cumreduce(arr, func, toAlter) { - var row, nrow, ncol, res, col; + // return row as array + // rowa([[1,2],[3,4]],0) -> [1,2] + jStat.rowa = function rowa(arr, i) { + return jStat.row(arr, i); + }; - if (!isArray(arr[0])) - arr = [arr]; - nrow = arr.length; - ncol = arr[0].length; - res = toAlter ? arr : new Array(nrow); + // Returns the specified column as a vector or return a sub matrix by pick some + // columns + jStat.col = function col(arr, index) { + if (isArray(index)) { + var submat = jStat.arange(arr.length).map(function(i) { + return new Array(index.length); + }); + index.forEach(function(ind, i){ + jStat.arange(arr.length).forEach(function(j) { + submat[j][i] = arr[j][ind]; + }); + }); + return submat; + } + var column = new Array(arr.length); + for (var i = 0; i < arr.length; i++) + column[i] = [arr[i][index]]; + return column; + }; - for (row = 0; row < nrow; row++) { - // if the row doesn't exist, create it - if (!res[row]) - res[row] = new Array(ncol); - if (ncol > 0) - res[row][0] = arr[row][0]; - for (col = 1; col < ncol; col++) - res[row][col] = func(res[row][col-1], arr[row][col]); - } - return res.length === 1 ? res[0] : res; -}; + // return column as array + // cola([[1,2],[3,4]],0) -> [1,3] + jStat.cola = function cola(arr, i) { + return jStat.col(arr, i).map(function(a){ return a[0] }); + }; -// Destructively alter an array. -jStat.alter = function alter(arr, func) { - return jStat.map(arr, func, true); -}; + // Returns the diagonal of the matrix + jStat.diag = function diag(arr) { + var nrow = jStat.rows(arr); + var res = new Array(nrow); + for (var row = 0; row < nrow; row++) + res[row] = [arr[row][row]]; + return res; + }; -// Generate a rows x cols matrix according to the supplied function. -jStat.create = function create(rows, cols, func) { - var res = new Array(rows); - var i, j; - if (isFunction(cols)) { - func = cols; - cols = rows; - } + // Returns the anti-diagonal of the matrix + jStat.antidiag = function antidiag(arr) { + var nrow = jStat.rows(arr) - 1; + var res = new Array(nrow); + for (var i = 0; nrow >= 0; nrow--, i++) + res[i] = [arr[i][nrow]]; + return res; + }; - for (var i = 0; i < rows; i++) { - res[i] = new Array(cols); - for (j = 0; j < cols; j++) - res[i][j] = func(i, j); - } + // Transpose a matrix or array. + jStat.transpose = function transpose(arr) { + var obj = []; + var objArr, rows, cols, j, i; - return res; -}; + // Make sure arr is in matrix format. + if (!isArray(arr[0])) + arr = [arr]; + rows = arr.length; + cols = arr[0].length; -function retZero() { return 0; } + for (var i = 0; i < cols; i++) { + objArr = new Array(rows); + for (j = 0; j < rows; j++) + objArr[j] = arr[j][i]; + obj.push(objArr); + } + // If obj is vector, return only single array. + return obj.length === 1 ? obj[0] : obj; + }; -// Generate a rows x cols matrix of zeros. -jStat.zeros = function zeros(rows, cols) { - if (!isNumber(cols)) - cols = rows; - return jStat.create(rows, cols, retZero); -}; + // Map a function to an array or array of arrays. + // "toAlter" is an internal variable. + jStat.map = function map(arr, func, toAlter) { + var row, nrow, ncol, res, col; -function retOne() { return 1; } + if (!isArray(arr[0])) + arr = [arr]; + nrow = arr.length; + ncol = arr[0].length; + res = toAlter ? arr : new Array(nrow); -// Generate a rows x cols matrix of ones. -jStat.ones = function ones(rows, cols) { - if (!isNumber(cols)) - cols = rows; - return jStat.create(rows, cols, retOne); -}; + for (row = 0; row < nrow; row++) { + // if the row doesn't exist, create it + if (!res[row]) + res[row] = new Array(ncol); + for (col = 0; col < ncol; col++) + res[row][col] = func(arr[row][col], row, col); + } + return res.length === 1 ? res[0] : res; + }; -// Generate a rows x cols matrix of uniformly random numbers. -jStat.rand = function rand(rows, cols) { - if (!isNumber(cols)) - cols = rows; - return jStat.create(rows, cols, Math.random); -}; + // Cumulatively combine the elements of an array or array of arrays using a function. + jStat.cumreduce = function cumreduce(arr, func, toAlter) { + var row, nrow, ncol, res, col; -function retIdent(i, j) { return i === j ? 1 : 0; } + if (!isArray(arr[0])) + arr = [arr]; + nrow = arr.length; + ncol = arr[0].length; + res = toAlter ? arr : new Array(nrow); -// Generate an identity matrix of size row x cols. -jStat.identity = function identity(rows, cols) { - if (!isNumber(cols)) - cols = rows; - return jStat.create(rows, cols, retIdent); -}; + for (row = 0; row < nrow; row++) { + // if the row doesn't exist, create it + if (!res[row]) + res[row] = new Array(ncol); + if (ncol > 0) + res[row][0] = arr[row][0]; + for (col = 1; col < ncol; col++) + res[row][col] = func(res[row][col-1], arr[row][col]); + } + return res.length === 1 ? res[0] : res; + }; -// Tests whether a matrix is symmetric -jStat.symmetric = function symmetric(arr) { - var size = arr.length; - var row, col; + // Destructively alter an array. + jStat.alter = function alter(arr, func) { + return jStat.map(arr, func, true); + }; - if (arr.length !== arr[0].length) - return false; - for (row = 0; row < size; row++) { - for (col = 0; col < size; col++) - if (arr[col][row] !== arr[row][col]) - return false; - } - - return true; -}; - - -// Set all values to zero. -jStat.clear = function clear(arr) { - return jStat.alter(arr, retZero); -}; - - -// Generate sequence. -jStat.seq = function seq(min, max, length, func) { - if (!isFunction(func)) - func = false; - - var arr = []; - var hival = calcRdx(min, max); - var step = (max * hival - min * hival) / ((length - 1) * hival); - var current = min; - var cnt; - - // Current is assigned using a technique to compensate for IEEE error. - // TODO: Needs better implementation. - for (cnt = 0; - current <= max && cnt < length; - cnt++, current = (min * hival + step * hival * cnt) / hival) { - arr.push((func ? func(current, cnt) : current)); - } - - return arr; -}; - - -// arange(5) -> [0,1,2,3,4] -// arange(1,5) -> [1,2,3,4] -// arange(5,1,-1) -> [5,4,3,2] -jStat.arange = function arange(start, end, step) { - var rl = []; - step = step || 1; - if (end === undefined) { - end = start; - start = 0; - } - if (start === end || step === 0) { - return []; - } - if (start < end && step < 0) { - return []; - } - if (start > end && step > 0) { - return []; - } - if (step > 0) { - for (i = start; i < end; i += step) { - rl.push(i); - } - } else { - for (i = start; i > end; i += step) { - rl.push(i); - } - } - return rl; -}; - - -// A=[[1,2,3],[4,5,6],[7,8,9]] -// slice(A,{row:{end:2},col:{start:1}}) -> [[2,3],[5,6]] -// slice(A,1,{start:1}) -> [5,6] -// as numpy code A[:2,1:] -jStat.slice = (function(){ - function _slice(list, start, end, step) { - // note it's not equal to range.map mode it's a bug - var i; - var rl = []; - var length = list.length; - if (start === undefined && end === undefined && step === undefined) { - return jStat.copy(list); - } + // Generate a rows x cols matrix according to the supplied function. + jStat.create = function create(rows, cols, func) { + var res = new Array(rows); + var i, j; - start = start || 0; - end = end || list.length; - start = start >= 0 ? start : length + start; - end = end >= 0 ? end : length + end; - step = step || 1; - if (start === end || step === 0) { - return []; - } - if (start < end && step < 0) { - return []; - } - if (start > end && step > 0) { - return []; - } - if (step > 0) { - for (i = start; i < end; i += step) { - rl.push(list[i]); + if (isFunction(cols)) { + func = cols; + cols = rows; } - } else { - for (i = start; i > end;i += step) { - rl.push(list[i]); + + for (var i = 0; i < rows; i++) { + res[i] = new Array(cols); + for (j = 0; j < cols; j++) + res[i][j] = func(i, j); } - } - return rl; - } - - function slice(list, rcSlice) { - rcSlice = rcSlice || {}; - if (isNumber(rcSlice.row)) { - if (isNumber(rcSlice.col)) - return list[rcSlice.row][rcSlice.col]; - var row = jStat.rowa(list, rcSlice.row); - var colSlice = rcSlice.col || {}; - return _slice(row, colSlice.start, colSlice.end, colSlice.step); - } - if (isNumber(rcSlice.col)) { - var col = jStat.cola(list, rcSlice.col); - var rowSlice = rcSlice.row || {}; - return _slice(col, rowSlice.start, rowSlice.end, rowSlice.step); - } + return res; + }; - var rowSlice = rcSlice.row || {}; - var colSlice = rcSlice.col || {}; - var rows = _slice(list, rowSlice.start, rowSlice.end, rowSlice.step); - return rows.map(function(row) { - return _slice(row, colSlice.start, colSlice.end, colSlice.step); - }); - } - - return slice; -}()); - - -// A=[[1,2,3],[4,5,6],[7,8,9]] -// sliceAssign(A,{row:{start:1},col:{start:1}},[[0,0],[0,0]]) -// A=[[1,2,3],[4,0,0],[7,0,0]] -jStat.sliceAssign = function sliceAssign(A, rcSlice, B) { - if (isNumber(rcSlice.row)) { - if (isNumber(rcSlice.col)) - return A[rcSlice.row][rcSlice.col] = B; - rcSlice.col = rcSlice.col || {}; - rcSlice.col.start = rcSlice.col.start || 0; - rcSlice.col.end = rcSlice.col.end || A[0].length; - rcSlice.col.step = rcSlice.col.step || 1; - var nl = jStat.arange(rcSlice.col.start, - Math.min(A.length, rcSlice.col.end), - rcSlice.col.step); - var m = rcSlice.row; - nl.forEach(function(n, i) { - A[m][n] = B[i]; - }); - return A; - } - - if (isNumber(rcSlice.col)) { - rcSlice.row = rcSlice.row || {}; - rcSlice.row.start = rcSlice.row.start || 0; - rcSlice.row.end = rcSlice.row.end || A.length; - rcSlice.row.step = rcSlice.row.step || 1; - var ml = jStat.arange(rcSlice.row.start, - Math.min(A[0].length, rcSlice.row.end), - rcSlice.row.step); - var n = rcSlice.col; - ml.forEach(function(m, j) { - A[m][n] = B[j]; - }); - return A; - } - - if (B[0].length === undefined) { - B = [B]; - } - rcSlice.row.start = rcSlice.row.start || 0; - rcSlice.row.end = rcSlice.row.end || A.length; - rcSlice.row.step = rcSlice.row.step || 1; - rcSlice.col.start = rcSlice.col.start || 0; - rcSlice.col.end = rcSlice.col.end || A[0].length; - rcSlice.col.step = rcSlice.col.step || 1; - var ml = jStat.arange(rcSlice.row.start, - Math.min(A.length, rcSlice.row.end), - rcSlice.row.step); - var nl = jStat.arange(rcSlice.col.start, - Math.min(A[0].length, rcSlice.col.end), - rcSlice.col.step); - ml.forEach(function(m, i) { - nl.forEach(function(n, j) { - A[m][n] = B[i][j]; - }); - }); - return A; -}; - - -// [1,2,3] -> -// [[1,0,0],[0,2,0],[0,0,3]] -jStat.diagonal = function diagonal(diagArray) { - var mat = jStat.zeros(diagArray.length, diagArray.length); - diagArray.forEach(function(t, i) { - mat[i][i] = t; - }); - return mat; -}; - - -// return copy of A -jStat.copy = function copy(A) { - return A.map(function(row) { - if (isNumber(row)) - return row; - return row.map(function(t) { - return t; - }); - }); -}; + function retZero() { return 0; } -// TODO: Go over this entire implementation. Seems a tragic waste of resources -// doing all this work. Instead, and while ugly, use new Function() to generate -// a custom function for each static method. -// Quick reference. -var jProto = jStat.prototype; + // Generate a rows x cols matrix of zeros. + jStat.zeros = function zeros(rows, cols) { + if (!isNumber(cols)) + cols = rows; + return jStat.create(rows, cols, retZero); + }; + -// Default length. -jProto.length = 0; + function retOne() { return 1; } -// For internal use only. -// TODO: Check if they're actually used, and if they are then rename them -// to _* -jProto.push = Array.prototype.push; -jProto.sort = Array.prototype.sort; -jProto.splice = Array.prototype.splice; -jProto.slice = Array.prototype.slice; + + // Generate a rows x cols matrix of ones. + jStat.ones = function ones(rows, cols) { + if (!isNumber(cols)) + cols = rows; + return jStat.create(rows, cols, retOne); + }; -// Return a clean array. -jProto.toArray = function toArray() { - return this.length > 1 ? slice.call(this) : slice.call(this)[0]; -}; + // Generate a rows x cols matrix of uniformly random numbers. + jStat.rand = function rand(rows, cols) { + if (!isNumber(cols)) + cols = rows; + return jStat.create(rows, cols, Math.random); + }; -// Map a function to a matrix or vector. -jProto.map = function map(func, toAlter) { - return jStat(jStat.map(this, func, toAlter)); -}; + function retIdent(i, j) { return i === j ? 1 : 0; } -// Cumulatively combine the elements of a matrix or vector using a function. -jProto.cumreduce = function cumreduce(func, toAlter) { - return jStat(jStat.cumreduce(this, func, toAlter)); -}; + // Generate an identity matrix of size row x cols. + jStat.identity = function identity(rows, cols) { + if (!isNumber(cols)) + cols = rows; + return jStat.create(rows, cols, retIdent); + }; -// Destructively alter an array. -jProto.alter = function alter(func) { - jStat.alter(this, func); - return this; -}; + // Tests whether a matrix is symmetric + jStat.symmetric = function symmetric(arr) { + var size = arr.length; + var row, col; + if (arr.length !== arr[0].length) + return false; -// Extend prototype with methods that have no argument. -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - jProto[passfunc] = function(func) { - var self = this, - results; - // Check for callback. - if (func) { - setTimeout(function() { - func.call(self, jProto[passfunc].call(self)); - }); - return this; + for (row = 0; row < size; row++) { + for (col = 0; col < size; col++) + if (arr[col][row] !== arr[row][col]) + return false; } - results = jStat[passfunc](this); - return isArray(results) ? jStat(results) : results; + + return true; }; - })(funcs[i]); -})('transpose clear symmetric rows cols dimensions diag antidiag'.split(' ')); - - -// Extend prototype with methods that have one argument. -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - jProto[passfunc] = function(index, func) { - var self = this; - // check for callback - if (func) { - setTimeout(function() { - func.call(self, jProto[passfunc].call(self, index)); - }); - return this; + + + // Set all values to zero. + jStat.clear = function clear(arr) { + return jStat.alter(arr, retZero); + }; + + + // Generate sequence. + jStat.seq = function seq(min, max, length, func) { + if (!isFunction(func)) + func = false; + + var arr = []; + var hival = calcRdx(min, max); + var step = (max * hival - min * hival) / ((length - 1) * hival); + var current = min; + var cnt; + + // Current is assigned using a technique to compensate for IEEE error. + // TODO: Needs better implementation. + for (cnt = 0; + current <= max && cnt < length; + cnt++, current = (min * hival + step * hival * cnt) / hival) { + arr.push((func ? func(current, cnt) : current)); } - return jStat(jStat[passfunc](this, index)); + + return arr; }; - })(funcs[i]); -})('row col'.split(' ')); - - -// Extend prototype with simple shortcut methods. -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - jProto[passfunc] = new Function( - 'return jStat(jStat.' + passfunc + '.apply(null, arguments));'); - })(funcs[i]); -})('create zeros ones rand identity'.split(' ')); - - -// Exposing jStat. -return jStat; - -}(Math)); -(function(jStat, Math) { - -var isFunction = jStat.utils.isFunction; - -// Ascending functions for sort -function ascNum(a, b) { return a - b; } - -function clip(arg, min, max) { - return Math.max(min, Math.min(arg, max)); -} - - -// sum of an array -jStat.sum = function sum(arr) { - var sum = 0; - var i = arr.length; - while (--i >= 0) - sum += arr[i]; - return sum; -}; - - -// sum squared -jStat.sumsqrd = function sumsqrd(arr) { - var sum = 0; - var i = arr.length; - while (--i >= 0) - sum += arr[i] * arr[i]; - return sum; -}; - - -// sum of squared errors of prediction (SSE) -jStat.sumsqerr = function sumsqerr(arr) { - var mean = jStat.mean(arr); - var sum = 0; - var i = arr.length; - var tmp; - while (--i >= 0) { - tmp = arr[i] - mean; - sum += tmp * tmp; - } - return sum; -}; - -// sum of an array in each row -jStat.sumrow = function sumrow(arr) { - var sum = 0; - var i = arr.length; - while (--i >= 0) - sum += arr[i]; - return sum; -}; - -// product of an array -jStat.product = function product(arr) { - var prod = 1; - var i = arr.length; - while (--i >= 0) - prod *= arr[i]; - return prod; -}; - - -// minimum value of an array -jStat.min = function min(arr) { - var low = arr[0]; - var i = 0; - while (++i < arr.length) - if (arr[i] < low) - low = arr[i]; - return low; -}; - - -// maximum value of an array -jStat.max = function max(arr) { - var high = arr[0]; - var i = 0; - while (++i < arr.length) - if (arr[i] > high) - high = arr[i]; - return high; -}; - - -// unique values of an array -jStat.unique = function unique(arr) { - var hash = {}, _arr = []; - for(var i = 0; i < arr.length; i++) { - if (!hash[arr[i]]) { - hash[arr[i]] = true; - _arr.push(arr[i]); - } - } - return _arr; -}; - - -// mean value of an array -jStat.mean = function mean(arr) { - return jStat.sum(arr) / arr.length; -}; - - -// mean squared error (MSE) -jStat.meansqerr = function meansqerr(arr) { - return jStat.sumsqerr(arr) / arr.length; -}; - - -// geometric mean of an array -jStat.geomean = function geomean(arr) { - return Math.pow(jStat.product(arr), 1 / arr.length); -}; - - -// median of an array -jStat.median = function median(arr) { - var arrlen = arr.length; - var _arr = arr.slice().sort(ascNum); - // check if array is even or odd, then return the appropriate - return !(arrlen & 1) - ? (_arr[(arrlen / 2) - 1 ] + _arr[(arrlen / 2)]) / 2 - : _arr[(arrlen / 2) | 0 ]; -}; - - -// cumulative sum of an array -jStat.cumsum = function cumsum(arr) { - return jStat.cumreduce(arr, function (a, b) { return a + b; }); -}; - - -// cumulative product of an array -jStat.cumprod = function cumprod(arr) { - return jStat.cumreduce(arr, function (a, b) { return a * b; }); -}; - - -// successive differences of a sequence -jStat.diff = function diff(arr) { - var diffs = []; - var arrLen = arr.length; - var i; - for (var i = 1; i < arrLen; i++) - diffs.push(arr[i] - arr[i - 1]); - return diffs; -}; - - -// ranks of an array -jStat.rank = function (arr) { - var arrlen = arr.length; - var sorted = arr.slice().sort(ascNum); - var ranks = new Array(arrlen); - for (var i = 0; i < arrlen; i++) { - var first = sorted.indexOf(arr[i]); - var last = sorted.lastIndexOf(arr[i]); - if (first === last) { - var val = first; - } else { - var val = (first + last) / 2; - } - ranks[i] = val + 1; - } - return ranks; -}; - - -// mode of an array -// if there are multiple modes of an array, return all of them -// is this the appropriate way of handling it? -jStat.mode = function mode(arr) { - var arrLen = arr.length; - var _arr = arr.slice().sort(ascNum); - var count = 1; - var maxCount = 0; - var numMaxCount = 0; - var mode_arr = []; - var i; - - for (var i = 0; i < arrLen; i++) { - if (_arr[i] === _arr[i + 1]) { - count++; - } else { - if (count > maxCount) { - mode_arr = [_arr[i]]; - maxCount = count; - numMaxCount = 0; + + + // arange(5) -> [0,1,2,3,4] + // arange(1,5) -> [1,2,3,4] + // arange(5,1,-1) -> [5,4,3,2] + jStat.arange = function arange(start, end, step) { + var rl = []; + step = step || 1; + if (end === undefined$1) { + end = start; + start = 0; } - // are there multiple max counts - else if (count === maxCount) { - mode_arr.push(_arr[i]); - numMaxCount++; + if (start === end || step === 0) { + return []; } - // resetting count for new value in array - count = 1; - } - } - - return numMaxCount === 0 ? mode_arr[0] : mode_arr; -}; - - -// range of an array -jStat.range = function range(arr) { - return jStat.max(arr) - jStat.min(arr); -}; - -// variance of an array -// flag = true indicates sample instead of population -jStat.variance = function variance(arr, flag) { - return jStat.sumsqerr(arr) / (arr.length - (flag ? 1 : 0)); -}; - -// pooled variance of an array of arrays -jStat.pooledvariance = function pooledvariance(arr) { - var sumsqerr = arr.reduce(function (a, samples) {return a + jStat.sumsqerr(samples);}, 0); - var count = arr.reduce(function (a, samples) {return a + samples.length;}, 0); - return sumsqerr / (count - arr.length); -}; - -// deviation of an array -jStat.deviation = function (arr) { - var mean = jStat.mean(arr); - var arrlen = arr.length; - var dev = new Array(arrlen); - for (var i = 0; i < arrlen; i++) { - dev[i] = arr[i] - mean; - } - return dev; -}; - -// standard deviation of an array -// flag = true indicates sample instead of population -jStat.stdev = function stdev(arr, flag) { - return Math.sqrt(jStat.variance(arr, flag)); -}; - -// pooled standard deviation of an array of arrays -jStat.pooledstdev = function pooledstdev(arr) { - return Math.sqrt(jStat.pooledvariance(arr)); -}; - -// mean deviation (mean absolute deviation) of an array -jStat.meandev = function meandev(arr) { - var mean = jStat.mean(arr); - var a = []; - for (var i = arr.length - 1; i >= 0; i--) { - a.push(Math.abs(arr[i] - mean)); - } - return jStat.mean(a); -}; - - -// median deviation (median absolute deviation) of an array -jStat.meddev = function meddev(arr) { - var median = jStat.median(arr); - var a = []; - for (var i = arr.length - 1; i >= 0; i--) { - a.push(Math.abs(arr[i] - median)); - } - return jStat.median(a); -}; - - -// coefficient of variation -jStat.coeffvar = function coeffvar(arr) { - return jStat.stdev(arr) / jStat.mean(arr); -}; - - -// quartiles of an array -jStat.quartiles = function quartiles(arr) { - var arrlen = arr.length; - var _arr = arr.slice().sort(ascNum); - return [ - _arr[ Math.round((arrlen) / 4) - 1 ], - _arr[ Math.round((arrlen) / 2) - 1 ], - _arr[ Math.round((arrlen) * 3 / 4) - 1 ] - ]; -}; - - -// Arbitary quantiles of an array. Direct port of the scipy.stats -// implementation by Pierre GF Gerard-Marchant. -jStat.quantiles = function quantiles(arr, quantilesArray, alphap, betap) { - var sortedArray = arr.slice().sort(ascNum); - var quantileVals = [quantilesArray.length]; - var n = arr.length; - var i, p, m, aleph, k, gamma; - - if (typeof alphap === 'undefined') - alphap = 3 / 8; - if (typeof betap === 'undefined') - betap = 3 / 8; - - for (var i = 0; i < quantilesArray.length; i++) { - p = quantilesArray[i]; - m = alphap + p * (1 - alphap - betap); - aleph = n * p + m; - k = Math.floor(clip(aleph, 1, n - 1)); - gamma = clip(aleph - k, 0, 1); - quantileVals[i] = (1 - gamma) * sortedArray[k - 1] + gamma * sortedArray[k]; - } - - return quantileVals; -}; - -// Returns the k-th percentile of values in a range, where k is in the -// range 0..1, exclusive. -jStat.percentile = function percentile(arr, k) { - var _arr = arr.slice().sort(ascNum); - var realIndex = k * (_arr.length - 1); - var index = parseInt(realIndex); - var frac = realIndex - index; - - if (index + 1 < _arr.length) { - return _arr[index] * (1 - frac) + _arr[index + 1] * frac; - } else { - return _arr[index]; - } -}; - - -// The percentile rank of score in a given array. Returns the percentage -// of all values in the input array that are less than (kind='strict') or -// less or equal than (kind='weak') score. Default is weak. -jStat.percentileOfScore = function percentileOfScore(arr, score, kind) { - var counter = 0; - var len = arr.length; - var strict = false; - var value, i; - - if (kind === 'strict') - strict = true; - - for (var i = 0; i < len; i++) { - value = arr[i]; - if ((strict && value < score) || - (!strict && value <= score)) { - counter++; - } - } - - return counter / len; -}; - - -// Histogram (bin count) data -jStat.histogram = function histogram(arr, bins) { - var first = jStat.min(arr); - var binCnt = bins || 4; - var binWidth = (jStat.max(arr) - first) / binCnt; - var len = arr.length; - var bins = []; - var i; - - for (var i = 0; i < binCnt; i++) - bins[i] = 0; - for (var i = 0; i < len; i++) - bins[Math.min(Math.floor(((arr[i] - first) / binWidth)), binCnt - 1)] += 1; - - return bins; -}; - - -// covariance of two arrays -jStat.covariance = function covariance(arr1, arr2) { - var u = jStat.mean(arr1); - var v = jStat.mean(arr2); - var arr1Len = arr1.length; - var sq_dev = new Array(arr1Len); - var i; - - for (var i = 0; i < arr1Len; i++) - sq_dev[i] = (arr1[i] - u) * (arr2[i] - v); - - return jStat.sum(sq_dev) / (arr1Len - 1); -}; - - -// (pearson's) population correlation coefficient, rho -jStat.corrcoeff = function corrcoeff(arr1, arr2) { - return jStat.covariance(arr1, arr2) / - jStat.stdev(arr1, 1) / - jStat.stdev(arr2, 1); -}; - - // (spearman's) rank correlation coefficient, sp -jStat.spearmancoeff = function (arr1, arr2) { - arr1 = jStat.rank(arr1); - arr2 = jStat.rank(arr2); - //return pearson's correlation of the ranks: - return jStat.corrcoeff(arr1, arr2); -}; - - -// statistical standardized moments (general form of skew/kurt) -jStat.stanMoment = function stanMoment(arr, n) { - var mu = jStat.mean(arr); - var sigma = jStat.stdev(arr); - var len = arr.length; - var skewSum = 0; - - for (var i = 0; i < len; i++) - skewSum += Math.pow((arr[i] - mu) / sigma, n); - - return skewSum / arr.length; -}; - -// (pearson's) moment coefficient of skewness -jStat.skewness = function skewness(arr) { - return jStat.stanMoment(arr, 3); -}; - -// (pearson's) (excess) kurtosis -jStat.kurtosis = function kurtosis(arr) { - return jStat.stanMoment(arr, 4) - 3; -}; - - -var jProto = jStat.prototype; - - -// Extend jProto with method for calculating cumulative sums and products. -// This differs from the similar extension below as cumsum and cumprod should -// not be run again in the case fullbool === true. -// If a matrix is passed, automatically assume operation should be done on the -// columns. -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - // If a matrix is passed, automatically assume operation should be done on - // the columns. - jProto[passfunc] = function(fullbool, func) { - var arr = []; - var i = 0; - var tmpthis = this; - // Assignment reassignation depending on how parameters were passed in. - if (isFunction(fullbool)) { - func = fullbool; - fullbool = false; + if (start < end && step < 0) { + return []; } - // Check if a callback was passed with the function. - if (func) { - setTimeout(function() { - func.call(tmpthis, jProto[passfunc].call(tmpthis, fullbool)); - }); - return this; + if (start > end && step > 0) { + return []; } - // Check if matrix and run calculations. - if (this.length > 1) { - tmpthis = fullbool === true ? this : this.transpose(); - for (; i < tmpthis.length; i++) - arr[i] = jStat[passfunc](tmpthis[i]); - return arr; + if (step > 0) { + for (i = start; i < end; i += step) { + rl.push(i); + } + } else { + for (i = start; i > end; i += step) { + rl.push(i); + } } - // Pass fullbool if only vector, not a matrix. for variance and stdev. - return jStat[passfunc](this[0], fullbool); + return rl; }; - })(funcs[i]); -})(('cumsum cumprod').split(' ')); -// Extend jProto with methods which don't require arguments and work on columns. -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - // If a matrix is passed, automatically assume operation should be done on - // the columns. - jProto[passfunc] = function(fullbool, func) { - var arr = []; - var i = 0; - var tmpthis = this; - // Assignment reassignation depending on how parameters were passed in. - if (isFunction(fullbool)) { - func = fullbool; - fullbool = false; - } - // Check if a callback was passed with the function. - if (func) { - setTimeout(function() { - func.call(tmpthis, jProto[passfunc].call(tmpthis, fullbool)); - }); - return this; - } - // Check if matrix and run calculations. - if (this.length > 1) { - if (passfunc !== 'sumrow') - tmpthis = fullbool === true ? this : this.transpose(); - for (; i < tmpthis.length; i++) - arr[i] = jStat[passfunc](tmpthis[i]); - return fullbool === true - ? jStat[passfunc](jStat.utils.toVector(arr)) - : arr; + // A=[[1,2,3],[4,5,6],[7,8,9]] + // slice(A,{row:{end:2},col:{start:1}}) -> [[2,3],[5,6]] + // slice(A,1,{start:1}) -> [5,6] + // as numpy code A[:2,1:] + jStat.slice = (function(){ + function _slice(list, start, end, step) { + // note it's not equal to range.map mode it's a bug + var i; + var rl = []; + var length = list.length; + if (start === undefined$1 && end === undefined$1 && step === undefined$1) { + return jStat.copy(list); + } + + start = start || 0; + end = end || list.length; + start = start >= 0 ? start : length + start; + end = end >= 0 ? end : length + end; + step = step || 1; + if (start === end || step === 0) { + return []; + } + if (start < end && step < 0) { + return []; + } + if (start > end && step > 0) { + return []; + } + if (step > 0) { + for (i = start; i < end; i += step) { + rl.push(list[i]); + } + } else { + for (i = start; i > end;i += step) { + rl.push(list[i]); + } + } + return rl; } - // Pass fullbool if only vector, not a matrix. for variance and stdev. - return jStat[passfunc](this[0], fullbool); - }; - })(funcs[i]); -})(('sum sumsqrd sumsqerr sumrow product min max unique mean meansqerr ' + - 'geomean median diff rank mode range variance deviation stdev meandev ' + - 'meddev coeffvar quartiles histogram skewness kurtosis').split(' ')); + function slice(list, rcSlice) { + rcSlice = rcSlice || {}; + if (isNumber(rcSlice.row)) { + if (isNumber(rcSlice.col)) + return list[rcSlice.row][rcSlice.col]; + var row = jStat.rowa(list, rcSlice.row); + var colSlice = rcSlice.col || {}; + return _slice(row, colSlice.start, colSlice.end, colSlice.step); + } -// Extend jProto with functions that take arguments. Operations on matrices are -// done on columns. -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - jProto[passfunc] = function() { - var arr = []; - var i = 0; - var tmpthis = this; - var args = Array.prototype.slice.call(arguments); - - // If the last argument is a function, we assume it's a callback; we - // strip the callback out and call the function again. - if (isFunction(args[args.length - 1])) { - var callbackFunction = args[args.length - 1]; - var argsToPass = args.slice(0, args.length - 1); - - setTimeout(function() { - callbackFunction.call(tmpthis, - jProto[passfunc].apply(tmpthis, argsToPass)); + if (isNumber(rcSlice.col)) { + var col = jStat.cola(list, rcSlice.col); + var rowSlice = rcSlice.row || {}; + return _slice(col, rowSlice.start, rowSlice.end, rowSlice.step); + } + + var rowSlice = rcSlice.row || {}; + var colSlice = rcSlice.col || {}; + var rows = _slice(list, rowSlice.start, rowSlice.end, rowSlice.step); + return rows.map(function(row) { + return _slice(row, colSlice.start, colSlice.end, colSlice.step); }); - return this; + } - // Otherwise we curry the function args and call normally. - } else { - var callbackFunction = undefined; - var curriedFunction = function curriedFunction(vector) { - return jStat[passfunc].apply(tmpthis, [vector].concat(args)); - }; + return slice; + }()); + + + // A=[[1,2,3],[4,5,6],[7,8,9]] + // sliceAssign(A,{row:{start:1},col:{start:1}},[[0,0],[0,0]]) + // A=[[1,2,3],[4,0,0],[7,0,0]] + jStat.sliceAssign = function sliceAssign(A, rcSlice, B) { + if (isNumber(rcSlice.row)) { + if (isNumber(rcSlice.col)) + return A[rcSlice.row][rcSlice.col] = B; + rcSlice.col = rcSlice.col || {}; + rcSlice.col.start = rcSlice.col.start || 0; + rcSlice.col.end = rcSlice.col.end || A[0].length; + rcSlice.col.step = rcSlice.col.step || 1; + var nl = jStat.arange(rcSlice.col.start, + Math.min(A.length, rcSlice.col.end), + rcSlice.col.step); + var m = rcSlice.row; + nl.forEach(function(n, i) { + A[m][n] = B[i]; + }); + return A; } - // If this is a matrix, run column-by-column. - if (this.length > 1) { - tmpthis = tmpthis.transpose(); - for (; i < tmpthis.length; i++) - arr[i] = curriedFunction(tmpthis[i]); - return arr; + if (isNumber(rcSlice.col)) { + rcSlice.row = rcSlice.row || {}; + rcSlice.row.start = rcSlice.row.start || 0; + rcSlice.row.end = rcSlice.row.end || A.length; + rcSlice.row.step = rcSlice.row.step || 1; + var ml = jStat.arange(rcSlice.row.start, + Math.min(A[0].length, rcSlice.row.end), + rcSlice.row.step); + var n = rcSlice.col; + ml.forEach(function(m, j) { + A[m][n] = B[j]; + }); + return A; } - // Otherwise run on the vector. - return curriedFunction(this[0]); - }; - })(funcs[i]); -})('quantiles percentileOfScore'.split(' ')); - -}(jStat, Math)); -// Special functions // -(function(jStat, Math) { - -// Log-gamma function -jStat.gammaln = function gammaln(x) { - var j = 0; - var cof = [ - 76.18009172947146, -86.50532032941677, 24.01409824083091, - -1.231739572450155, 0.1208650973866179e-2, -0.5395239384953e-5 - ]; - var ser = 1.000000000190015; - var xx, y, tmp; - tmp = (y = xx = x) + 5.5; - tmp -= (xx + 0.5) * Math.log(tmp); - for (; j < 6; j++) - ser += cof[j] / ++y; - return Math.log(2.5066282746310005 * ser / xx) - tmp; -}; - - -// gamma of x -jStat.gammafn = function gammafn(x) { - var p = [-1.716185138865495, 24.76565080557592, -379.80425647094563, - 629.3311553128184, 866.9662027904133, -31451.272968848367, - -36144.413418691176, 66456.14382024054 - ]; - var q = [-30.8402300119739, 315.35062697960416, -1015.1563674902192, - -3107.771671572311, 22538.118420980151, 4755.8462775278811, - -134659.9598649693, -115132.2596755535]; - var fact = false; - var n = 0; - var xden = 0; - var xnum = 0; - var y = x; - var i, z, yi, res; - if (y <= 0) { - res = y % 1 + 3.6e-16; - if (res) { - fact = (!(y & 1) ? 1 : -1) * Math.PI / Math.sin(Math.PI * res); - y = 1 - y; - } else { - return Infinity; - } - } - yi = y; - if (y < 1) { - z = y++; - } else { - z = (y -= n = (y | 0) - 1) - 1; - } - for (var i = 0; i < 8; ++i) { - xnum = (xnum + p[i]) * z; - xden = xden * z + q[i]; - } - res = xnum / xden + 1; - if (yi < y) { - res /= yi; - } else if (yi > y) { - for (var i = 0; i < n; ++i) { - res *= y; - y++; - } - } - if (fact) { - res = fact / res; - } - return res; -}; - - -// lower incomplete gamma function, which is usually typeset with a -// lower-case greek gamma as the function symbol -jStat.gammap = function gammap(a, x) { - return jStat.lowRegGamma(a, x) * jStat.gammafn(a); -}; - - -// The lower regularized incomplete gamma function, usually written P(a,x) -jStat.lowRegGamma = function lowRegGamma(a, x) { - var aln = jStat.gammaln(a); - var ap = a; - var sum = 1 / a; - var del = sum; - var b = x + 1 - a; - var c = 1 / 1.0e-30; - var d = 1 / b; - var h = d; - var i = 1; - // calculate maximum number of itterations required for a - var ITMAX = -~(Math.log((a >= 1) ? a : 1 / a) * 8.5 + a * 0.4 + 17); - var an; - - if (x < 0 || a <= 0) { - return NaN; - } else if (x < a + 1) { - for (; i <= ITMAX; i++) { - sum += del *= x / ++ap; - } - return (sum * Math.exp(-x + a * Math.log(x) - (aln))); - } - - for (; i <= ITMAX; i++) { - an = -i * (i - a); - b += 2; - d = an * d + b; - c = b + an / c; - d = 1 / d; - h *= d * c; - } - - return (1 - h * Math.exp(-x + a * Math.log(x) - (aln))); -}; - -// natural log factorial of n -jStat.factorialln = function factorialln(n) { - return n < 0 ? NaN : jStat.gammaln(n + 1); -}; - -// factorial of n -jStat.factorial = function factorial(n) { - return n < 0 ? NaN : jStat.gammafn(n + 1); -}; - -// combinations of n, m -jStat.combination = function combination(n, m) { - // make sure n or m don't exceed the upper limit of usable values - return (n > 170 || m > 170) - ? Math.exp(jStat.combinationln(n, m)) - : (jStat.factorial(n) / jStat.factorial(m)) / jStat.factorial(n - m); -}; - - -jStat.combinationln = function combinationln(n, m){ - return jStat.factorialln(n) - jStat.factorialln(m) - jStat.factorialln(n - m); -}; - - -// permutations of n, m -jStat.permutation = function permutation(n, m) { - return jStat.factorial(n) / jStat.factorial(n - m); -}; - - -// beta function -jStat.betafn = function betafn(x, y) { - // ensure arguments are positive - if (x <= 0 || y <= 0) - return undefined; - // make sure x + y doesn't exceed the upper limit of usable values - return (x + y > 170) - ? Math.exp(jStat.betaln(x, y)) - : jStat.gammafn(x) * jStat.gammafn(y) / jStat.gammafn(x + y); -}; - - -// natural logarithm of beta function -jStat.betaln = function betaln(x, y) { - return jStat.gammaln(x) + jStat.gammaln(y) - jStat.gammaln(x + y); -}; - - -// Evaluates the continued fraction for incomplete beta function by modified -// Lentz's method. -jStat.betacf = function betacf(x, a, b) { - var fpmin = 1e-30; - var m = 1; - var qab = a + b; - var qap = a + 1; - var qam = a - 1; - var c = 1; - var d = 1 - qab * x / qap; - var m2, aa, del, h; - - // These q's will be used in factors that occur in the coefficients - if (Math.abs(d) < fpmin) - d = fpmin; - d = 1 / d; - h = d; - - for (; m <= 100; m++) { - m2 = 2 * m; - aa = m * (b - m) * x / ((qam + m2) * (a + m2)); - // One step (the even one) of the recurrence - d = 1 + aa * d; - if (Math.abs(d) < fpmin) - d = fpmin; - c = 1 + aa / c; - if (Math.abs(c) < fpmin) - c = fpmin; - d = 1 / d; - h *= d * c; - aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2)); - // Next step of the recurrence (the odd one) - d = 1 + aa * d; - if (Math.abs(d) < fpmin) - d = fpmin; - c = 1 + aa / c; - if (Math.abs(c) < fpmin) - c = fpmin; - d = 1 / d; - del = d * c; - h *= del; - if (Math.abs(del - 1.0) < 3e-7) - break; - } - - return h; -}; - - -// Returns the inverse of the lower regularized inomplete gamma function -jStat.gammapinv = function gammapinv(p, a) { - var j = 0; - var a1 = a - 1; - var EPS = 1e-8; - var gln = jStat.gammaln(a); - var x, err, t, u, pp, lna1, afac; - - if (p >= 1) - return Math.max(100, a + 100 * Math.sqrt(a)); - if (p <= 0) - return 0; - if (a > 1) { - lna1 = Math.log(a1); - afac = Math.exp(a1 * (lna1 - 1) - gln); - pp = (p < 0.5) ? p : 1 - p; - t = Math.sqrt(-2 * Math.log(pp)); - x = (2.30753 + t * 0.27061) / (1 + t * (0.99229 + t * 0.04481)) - t; - if (p < 0.5) - x = -x; - x = Math.max(1e-3, - a * Math.pow(1 - 1 / (9 * a) - x / (3 * Math.sqrt(a)), 3)); - } else { - t = 1 - a * (0.253 + a * 0.12); - if (p < t) - x = Math.pow(p / t, 1 / a); - else - x = 1 - Math.log(1 - (p - t) / (1 - t)); - } - - for(; j < 12; j++) { - if (x <= 0) - return 0; - err = jStat.lowRegGamma(a, x) - p; - if (a > 1) - t = afac * Math.exp(-(x - a1) + a1 * (Math.log(x) - lna1)); - else - t = Math.exp(-x + a1 * Math.log(x) - gln); - u = err / t; - x -= (t = u / (1 - 0.5 * Math.min(1, u * ((a - 1) / x - 1)))); - if (x <= 0) - x = 0.5 * (x + t); - if (Math.abs(t) < EPS * x) - break; - } - - return x; -}; - - -// Returns the error function erf(x) -jStat.erf = function erf(x) { - var cof = [-1.3026537197817094, 6.4196979235649026e-1, 1.9476473204185836e-2, - -9.561514786808631e-3, -9.46595344482036e-4, 3.66839497852761e-4, - 4.2523324806907e-5, -2.0278578112534e-5, -1.624290004647e-6, - 1.303655835580e-6, 1.5626441722e-8, -8.5238095915e-8, - 6.529054439e-9, 5.059343495e-9, -9.91364156e-10, - -2.27365122e-10, 9.6467911e-11, 2.394038e-12, - -6.886027e-12, 8.94487e-13, 3.13092e-13, - -1.12708e-13, 3.81e-16, 7.106e-15, - -1.523e-15, -9.4e-17, 1.21e-16, - -2.8e-17]; - var j = cof.length - 1; - var isneg = false; - var d = 0; - var dd = 0; - var t, ty, tmp, res; - - if (x < 0) { - x = -x; - isneg = true; - } - - t = 2 / (2 + x); - ty = 4 * t - 2; - - for(; j > 0; j--) { - tmp = d; - d = ty * d - dd + cof[j]; - dd = tmp; - } - - res = t * Math.exp(-x * x + 0.5 * (cof[0] + ty * d) - dd); - return isneg ? res - 1 : 1 - res; -}; - - -// Returns the complmentary error function erfc(x) -jStat.erfc = function erfc(x) { - return 1 - jStat.erf(x); -}; - - -// Returns the inverse of the complementary error function -jStat.erfcinv = function erfcinv(p) { - var j = 0; - var x, err, t, pp; - if (p >= 2) - return -100; - if (p <= 0) - return 100; - pp = (p < 1) ? p : 2 - p; - t = Math.sqrt(-2 * Math.log(pp / 2)); - x = -0.70711 * ((2.30753 + t * 0.27061) / - (1 + t * (0.99229 + t * 0.04481)) - t); - for (; j < 2; j++) { - err = jStat.erfc(x) - pp; - x += err / (1.12837916709551257 * Math.exp(-x * x) - x * err); - } - return (p < 1) ? x : -x; -}; - - -// Returns the inverse of the incomplete beta function -jStat.ibetainv = function ibetainv(p, a, b) { - var EPS = 1e-8; - var a1 = a - 1; - var b1 = b - 1; - var j = 0; - var lna, lnb, pp, t, u, err, x, al, h, w, afac; - if (p <= 0) - return 0; - if (p >= 1) - return 1; - if (a >= 1 && b >= 1) { - pp = (p < 0.5) ? p : 1 - p; - t = Math.sqrt(-2 * Math.log(pp)); - x = (2.30753 + t * 0.27061) / (1 + t* (0.99229 + t * 0.04481)) - t; - if (p < 0.5) - x = -x; - al = (x * x - 3) / 6; - h = 2 / (1 / (2 * a - 1) + 1 / (2 * b - 1)); - w = (x * Math.sqrt(al + h) / h) - (1 / (2 * b - 1) - 1 / (2 * a - 1)) * - (al + 5 / 6 - 2 / (3 * h)); - x = a / (a + b * Math.exp(2 * w)); - } else { - lna = Math.log(a / (a + b)); - lnb = Math.log(b / (a + b)); - t = Math.exp(a * lna) / a; - u = Math.exp(b * lnb) / b; - w = t + u; - if (p < t / w) - x = Math.pow(a * w * p, 1 / a); - else - x = 1 - Math.pow(b * w * (1 - p), 1 / b); - } - afac = -jStat.gammaln(a) - jStat.gammaln(b) + jStat.gammaln(a + b); - for(; j < 10; j++) { - if (x === 0 || x === 1) - return x; - err = jStat.ibeta(x, a, b) - p; - t = Math.exp(a1 * Math.log(x) + b1 * Math.log(1 - x) + afac); - u = err / t; - x -= (t = u / (1 - 0.5 * Math.min(1, u * (a1 / x - b1 / (1 - x))))); - if (x <= 0) - x = 0.5 * (x + t); - if (x >= 1) - x = 0.5 * (x + t + 1); - if (Math.abs(t) < EPS * x && j > 0) - break; - } - return x; -}; - - -// Returns the incomplete beta function I_x(a,b) -jStat.ibeta = function ibeta(x, a, b) { - // Factors in front of the continued fraction. - var bt = (x === 0 || x === 1) ? 0 : - Math.exp(jStat.gammaln(a + b) - jStat.gammaln(a) - - jStat.gammaln(b) + a * Math.log(x) + b * - Math.log(1 - x)); - if (x < 0 || x > 1) - return false; - if (x < (a + 1) / (a + b + 2)) - // Use continued fraction directly. - return bt * jStat.betacf(x, a, b) / a; - // else use continued fraction after making the symmetry transformation. - return 1 - bt * jStat.betacf(1 - x, b, a) / b; -}; - - -// Returns a normal deviate (mu=0, sigma=1). -// If n and m are specified it returns a object of normal deviates. -jStat.randn = function randn(n, m) { - var u, v, x, y, q; - if (!m) - m = n; - if (n) - return jStat.create(n, m, function() { return jStat.randn(); }); - do { - u = Math.random(); - v = 1.7156 * (Math.random() - 0.5); - x = u - 0.449871; - y = Math.abs(v) + 0.386595; - q = x * x + y * (0.19600 * y - 0.25472 * x); - } while (q > 0.27597 && (q > 0.27846 || v * v > -4 * Math.log(u) * u * u)); - return v / u; -}; - - -// Returns a gamma deviate by the method of Marsaglia and Tsang. -jStat.randg = function randg(shape, n, m) { - var oalph = shape; - var a1, a2, u, v, x, mat; - if (!m) - m = n; - if (!shape) - shape = 1; - if (n) { - mat = jStat.zeros(n,m); - mat.alter(function() { return jStat.randg(shape); }); - return mat; - } - if (shape < 1) - shape += 1; - a1 = shape - 1 / 3; - a2 = 1 / Math.sqrt(9 * a1); - do { - do { - x = jStat.randn(); - v = 1 + a2 * x; - } while(v <= 0); - v = v * v * v; - u = Math.random(); - } while(u > 1 - 0.331 * Math.pow(x, 4) && - Math.log(u) > 0.5 * x*x + a1 * (1 - v + Math.log(v))); - // alpha > 1 - if (shape == oalph) - return a1 * v; - // alpha < 1 - do { - u = Math.random(); - } while(u === 0); - return Math.pow(u, 1 / oalph) * a1 * v; -}; - - -// making use of static methods on the instance -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - jStat.fn[passfunc] = function() { - return jStat( - jStat.map(this, function(value) { return jStat[passfunc](value); })); + if (B[0].length === undefined$1) { + B = [B]; + } + rcSlice.row.start = rcSlice.row.start || 0; + rcSlice.row.end = rcSlice.row.end || A.length; + rcSlice.row.step = rcSlice.row.step || 1; + rcSlice.col.start = rcSlice.col.start || 0; + rcSlice.col.end = rcSlice.col.end || A[0].length; + rcSlice.col.step = rcSlice.col.step || 1; + var ml = jStat.arange(rcSlice.row.start, + Math.min(A.length, rcSlice.row.end), + rcSlice.row.step); + var nl = jStat.arange(rcSlice.col.start, + Math.min(A[0].length, rcSlice.col.end), + rcSlice.col.step); + ml.forEach(function(m, i) { + nl.forEach(function(n, j) { + A[m][n] = B[i][j]; + }); + }); + return A; }; - })(funcs[i]); -})('gammaln gammafn factorial factorialln'.split(' ')); -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - jStat.fn[passfunc] = function() { - return jStat(jStat[passfunc].apply(null, arguments)); - }; - })(funcs[i]); -})('randn'.split(' ')); - -}(jStat, Math)); -(function(jStat, Math) { - -// generate all distribution instance methods -(function(list) { - for (var i = 0; i < list.length; i++) (function(func) { - // distribution instance method - jStat[func] = function(a, b, c) { - if (!(this instanceof arguments.callee)) - return new arguments.callee(a, b, c); - this._a = a; - this._b = b; - this._c = c; - return this; - }; - // distribution method to be used on a jStat instance - jStat.fn[func] = function(a, b, c) { - var newthis = jStat[func](a, b, c); - newthis.data = this; - return newthis; + // [1,2,3] -> + // [[1,0,0],[0,2,0],[0,0,3]] + jStat.diagonal = function diagonal(diagArray) { + var mat = jStat.zeros(diagArray.length, diagArray.length); + diagArray.forEach(function(t, i) { + mat[i][i] = t; + }); + return mat; }; - // sample instance method - jStat[func].prototype.sample = function(arr) { - var a = this._a; - var b = this._b; - var c = this._c; - if (arr) - return jStat.alter(arr, function() { - return jStat[func].sample(a, b, c); + + + // return copy of A + jStat.copy = function copy(A) { + return A.map(function(row) { + if (isNumber(row)) + return row; + return row.map(function(t) { + return t; }); - else - return jStat[func].sample(a, b, c); + }); }; - // generate the pdf, cdf and inv instance methods - (function(vals) { - for (var i = 0; i < vals.length; i++) (function(fnfunc) { - jStat[func].prototype[fnfunc] = function(x) { - var a = this._a; - var b = this._b; - var c = this._c; - if (!x && x !== 0) - x = this.data; - if (typeof x !== 'number') { - return jStat.fn.map.call(x, function(x) { - return jStat[func][fnfunc](x, a, b, c); - }); - } - return jStat[func][fnfunc](x, a, b, c); - }; - })(vals[i]); - })('pdf cdf inv'.split(' ')); - // generate the mean, median, mode and variance instance methods - (function(vals) { - for (var i = 0; i < vals.length; i++) (function(fnfunc) { - jStat[func].prototype[fnfunc] = function() { - return jStat[func][fnfunc](this._a, this._b, this._c); - }; - })(vals[i]); - })('mean median mode variance'.split(' ')); - })(list[i]); -})(( - 'beta centralF cauchy chisquare exponential gamma invgamma kumaraswamy ' + - 'laplace lognormal noncentralt normal pareto studentt weibull uniform ' + - 'binomial negbin hypgeom poisson triangular tukey arcsine' -).split(' ')); - - - -// extend beta function with static methods -jStat.extend(jStat.beta, { - pdf: function pdf(x, alpha, beta) { - // PDF is zero outside the support - if (x > 1 || x < 0) - return 0; - // PDF is one for the uniform case - if (alpha == 1 && beta == 1) - return 1; - - if (alpha < 512 && beta < 512) { - return (Math.pow(x, alpha - 1) * Math.pow(1 - x, beta - 1)) / - jStat.betafn(alpha, beta); - } else { - return Math.exp((alpha - 1) * Math.log(x) + - (beta - 1) * Math.log(1 - x) - - jStat.betaln(alpha, beta)); - } - }, - - cdf: function cdf(x, alpha, beta) { - return (x > 1 || x < 0) ? (x > 1) * 1 : jStat.ibeta(x, alpha, beta); - }, - - inv: function inv(x, alpha, beta) { - return jStat.ibetainv(x, alpha, beta); - }, - - mean: function mean(alpha, beta) { - return alpha / (alpha + beta); - }, - - median: function median(alpha, beta) { - return jStat.ibetainv(0.5, alpha, beta); - }, - - mode: function mode(alpha, beta) { - return (alpha - 1 ) / ( alpha + beta - 2); - }, - - // return a random sample - sample: function sample(alpha, beta) { - var u = jStat.randg(alpha); - return u / (u + jStat.randg(beta)); - }, - - variance: function variance(alpha, beta) { - return (alpha * beta) / (Math.pow(alpha + beta, 2) * (alpha + beta + 1)); - } -}); - -// extend F function with static methods -jStat.extend(jStat.centralF, { - // This implementation of the pdf function avoids float overflow - // See the way that R calculates this value: - // https://svn.r-project.org/R/trunk/src/nmath/df.c - pdf: function pdf(x, df1, df2) { - var p, q, f; - - if (x < 0) - return 0; - - if (df1 <= 2) { - if (x === 0 && df1 < 2) { - return Infinity; - } - if (x === 0 && df1 === 2) { - return 1; - } - return (1 / jStat.betafn(df1 / 2, df2 / 2)) * - Math.pow(df1 / df2, df1 / 2) * - Math.pow(x, (df1/2) - 1) * - Math.pow((1 + (df1 / df2) * x), -(df1 + df2) / 2); - } - p = (df1 * x) / (df2 + x * df1); - q = df2 / (df2 + x * df1); - f = df1 * q / 2.0; - return f * jStat.binomial.pdf((df1 - 2) / 2, (df1 + df2 - 2) / 2, p); - }, - - cdf: function cdf(x, df1, df2) { - if (x < 0) - return 0; - return jStat.ibeta((df1 * x) / (df1 * x + df2), df1 / 2, df2 / 2); - }, - - inv: function inv(x, df1, df2) { - return df2 / (df1 * (1 / jStat.ibetainv(x, df1 / 2, df2 / 2) - 1)); - }, - - mean: function mean(df1, df2) { - return (df2 > 2) ? df2 / (df2 - 2) : undefined; - }, - - mode: function mode(df1, df2) { - return (df1 > 2) ? (df2 * (df1 - 2)) / (df1 * (df2 + 2)) : undefined; - }, - - // return a random sample - sample: function sample(df1, df2) { - var x1 = jStat.randg(df1 / 2) * 2; - var x2 = jStat.randg(df2 / 2) * 2; - return (x1 / df1) / (x2 / df2); - }, - - variance: function variance(df1, df2) { - if (df2 <= 4) - return undefined; - return 2 * df2 * df2 * (df1 + df2 - 2) / - (df1 * (df2 - 2) * (df2 - 2) * (df2 - 4)); - } -}); - - -// extend cauchy function with static methods -jStat.extend(jStat.cauchy, { - pdf: function pdf(x, local, scale) { - if (scale < 0) { return 0; } - - return (scale / (Math.pow(x - local, 2) + Math.pow(scale, 2))) / Math.PI; - }, - - cdf: function cdf(x, local, scale) { - return Math.atan((x - local) / scale) / Math.PI + 0.5; - }, - - inv: function(p, local, scale) { - return local + scale * Math.tan(Math.PI * (p - 0.5)); - }, - - median: function median(local, scale) { - return local; - }, - - mode: function mode(local, scale) { - return local; - }, - - sample: function sample(local, scale) { - return jStat.randn() * - Math.sqrt(1 / (2 * jStat.randg(0.5))) * scale + local; - } -}); - - - -// extend chisquare function with static methods -jStat.extend(jStat.chisquare, { - pdf: function pdf(x, dof) { - if (x < 0) - return 0; - return (x === 0 && dof === 2) ? 0.5 : - Math.exp((dof / 2 - 1) * Math.log(x) - x / 2 - (dof / 2) * - Math.log(2) - jStat.gammaln(dof / 2)); - }, - - cdf: function cdf(x, dof) { - if (x < 0) - return 0; - return jStat.lowRegGamma(dof / 2, x / 2); - }, - - inv: function(p, dof) { - return 2 * jStat.gammapinv(p, 0.5 * dof); - }, - - mean : function(dof) { - return dof; - }, - - // TODO: this is an approximation (is there a better way?) - median: function median(dof) { - return dof * Math.pow(1 - (2 / (9 * dof)), 3); - }, - - mode: function mode(dof) { - return (dof - 2 > 0) ? dof - 2 : 0; - }, - - sample: function sample(dof) { - return jStat.randg(dof / 2) * 2; - }, - - variance: function variance(dof) { - return 2 * dof; - } -}); - - - -// extend exponential function with static methods -jStat.extend(jStat.exponential, { - pdf: function pdf(x, rate) { - return x < 0 ? 0 : rate * Math.exp(-rate * x); - }, - - cdf: function cdf(x, rate) { - return x < 0 ? 0 : 1 - Math.exp(-rate * x); - }, - - inv: function(p, rate) { - return -Math.log(1 - p) / rate; - }, - - mean : function(rate) { - return 1 / rate; - }, - - median: function (rate) { - return (1 / rate) * Math.log(2); - }, - - mode: function mode(rate) { - return 0; - }, - - sample: function sample(rate) { - return -1 / rate * Math.log(Math.random()); - }, - - variance : function(rate) { - return Math.pow(rate, -2); - } -}); - - - -// extend gamma function with static methods -jStat.extend(jStat.gamma, { - pdf: function pdf(x, shape, scale) { - if (x < 0) - return 0; - return (x === 0 && shape === 1) ? 1 / scale : - Math.exp((shape - 1) * Math.log(x) - x / scale - - jStat.gammaln(shape) - shape * Math.log(scale)); - }, - - cdf: function cdf(x, shape, scale) { - if (x < 0) - return 0; - return jStat.lowRegGamma(shape, x / scale); - }, - - inv: function(p, shape, scale) { - return jStat.gammapinv(p, shape) * scale; - }, - - mean : function(shape, scale) { - return shape * scale; - }, - - mode: function mode(shape, scale) { - if(shape > 1) return (shape - 1) * scale; - return undefined; - }, - - sample: function sample(shape, scale) { - return jStat.randg(shape) * scale; - }, - - variance: function variance(shape, scale) { - return shape * scale * scale; - } -}); - -// extend inverse gamma function with static methods -jStat.extend(jStat.invgamma, { - pdf: function pdf(x, shape, scale) { - if (x <= 0) - return 0; - return Math.exp(-(shape + 1) * Math.log(x) - scale / x - - jStat.gammaln(shape) + shape * Math.log(scale)); - }, - - cdf: function cdf(x, shape, scale) { - if (x <= 0) - return 0; - return 1 - jStat.lowRegGamma(shape, scale / x); - }, - - inv: function(p, shape, scale) { - return scale / jStat.gammapinv(1 - p, shape); - }, - - mean : function(shape, scale) { - return (shape > 1) ? scale / (shape - 1) : undefined; - }, - - mode: function mode(shape, scale) { - return scale / (shape + 1); - }, - - sample: function sample(shape, scale) { - return scale / jStat.randg(shape); - }, - - variance: function variance(shape, scale) { - if (shape <= 2) - return undefined; - return scale * scale / ((shape - 1) * (shape - 1) * (shape - 2)); - } -}); - - -// extend kumaraswamy function with static methods -jStat.extend(jStat.kumaraswamy, { - pdf: function pdf(x, alpha, beta) { - if (x === 0 && alpha === 1) - return beta; - else if (x === 1 && beta === 1) - return alpha; - return Math.exp(Math.log(alpha) + Math.log(beta) + (alpha - 1) * - Math.log(x) + (beta - 1) * - Math.log(1 - Math.pow(x, alpha))); - }, - - cdf: function cdf(x, alpha, beta) { - if (x < 0) - return 0; - else if (x > 1) - return 1; - return (1 - Math.pow(1 - Math.pow(x, alpha), beta)); - }, - - inv: function inv(p, alpha, beta) { - return Math.pow(1 - Math.pow(1 - p, 1 / beta), 1 / alpha); - }, - - mean : function(alpha, beta) { - return (beta * jStat.gammafn(1 + 1 / alpha) * - jStat.gammafn(beta)) / (jStat.gammafn(1 + 1 / alpha + beta)); - }, - - median: function median(alpha, beta) { - return Math.pow(1 - Math.pow(2, -1 / beta), 1 / alpha); - }, - - mode: function mode(alpha, beta) { - if (!(alpha >= 1 && beta >= 1 && (alpha !== 1 && beta !== 1))) - return undefined; - return Math.pow((alpha - 1) / (alpha * beta - 1), 1 / alpha); - }, - - variance: function variance(alpha, beta) { - throw new Error('variance not yet implemented'); - // TODO: complete this - } -}); - - - -// extend lognormal function with static methods -jStat.extend(jStat.lognormal, { - pdf: function pdf(x, mu, sigma) { - if (x <= 0) - return 0; - return Math.exp(-Math.log(x) - 0.5 * Math.log(2 * Math.PI) - - Math.log(sigma) - Math.pow(Math.log(x) - mu, 2) / - (2 * sigma * sigma)); - }, - - cdf: function cdf(x, mu, sigma) { - if (x < 0) - return 0; - return 0.5 + - (0.5 * jStat.erf((Math.log(x) - mu) / Math.sqrt(2 * sigma * sigma))); - }, - - inv: function(p, mu, sigma) { - return Math.exp(-1.41421356237309505 * sigma * jStat.erfcinv(2 * p) + mu); - }, - - mean: function mean(mu, sigma) { - return Math.exp(mu + sigma * sigma / 2); - }, - - median: function median(mu, sigma) { - return Math.exp(mu); - }, - - mode: function mode(mu, sigma) { - return Math.exp(mu - sigma * sigma); - }, - - sample: function sample(mu, sigma) { - return Math.exp(jStat.randn() * sigma + mu); - }, - - variance: function variance(mu, sigma) { - return (Math.exp(sigma * sigma) - 1) * Math.exp(2 * mu + sigma * sigma); - } -}); - - - -// extend noncentralt function with static methods -jStat.extend(jStat.noncentralt, { - pdf: function pdf(x, dof, ncp) { - var tol = 1e-14; - if (Math.abs(ncp) < tol) // ncp approx 0; use student-t - return jStat.studentt.pdf(x, dof) - - if (Math.abs(x) < tol) { // different formula for x == 0 - return Math.exp(jStat.gammaln((dof + 1) / 2) - ncp * ncp / 2 - - 0.5 * Math.log(Math.PI * dof) - jStat.gammaln(dof / 2)); - } - // formula for x != 0 - return dof / x * - (jStat.noncentralt.cdf(x * Math.sqrt(1 + 2 / dof), dof+2, ncp) - - jStat.noncentralt.cdf(x, dof, ncp)); - }, + // TODO: Go over this entire implementation. Seems a tragic waste of resources + // doing all this work. Instead, and while ugly, use new Function() to generate + // a custom function for each static method. - cdf: function cdf(x, dof, ncp) { - var tol = 1e-14; - var min_iterations = 200; + // Quick reference. + var jProto = jStat.prototype; - if (Math.abs(ncp) < tol) // ncp approx 0; use student-t - return jStat.studentt.cdf(x, dof); + // Default length. + jProto.length = 0; - // turn negative x into positive and flip result afterwards - var flip = false; - if (x < 0) { - flip = true; - ncp = -ncp; - } + // For internal use only. + // TODO: Check if they're actually used, and if they are then rename them + // to _* + jProto.push = Array.prototype.push; + jProto.sort = Array.prototype.sort; + jProto.splice = Array.prototype.splice; + jProto.slice = Array.prototype.slice; - var prob = jStat.normal.cdf(-ncp, 0, 1); - var value = tol + 1; - // use value at last two steps to determine convergence - var lastvalue = value; - var y = x * x / (x * x + dof); - var j = 0; - var p = Math.exp(-ncp * ncp / 2); - var q = Math.exp(-ncp * ncp / 2 - 0.5 * Math.log(2) - - jStat.gammaln(3 / 2)) * ncp; - while (j < min_iterations || lastvalue > tol || value > tol) { - lastvalue = value; - if (j > 0) { - p *= (ncp * ncp) / (2 * j); - q *= (ncp * ncp) / (2 * (j + 1 / 2)); - } - value = p * jStat.beta.cdf(y, j + 0.5, dof / 2) + - q * jStat.beta.cdf(y, j+1, dof/2); - prob += 0.5 * value; - j++; - } - return flip ? (1 - prob) : prob; - } -}); - - -// extend normal function with static methods -jStat.extend(jStat.normal, { - pdf: function pdf(x, mean, std) { - return Math.exp(-0.5 * Math.log(2 * Math.PI) - - Math.log(std) - Math.pow(x - mean, 2) / (2 * std * std)); - }, - - cdf: function cdf(x, mean, std) { - return 0.5 * (1 + jStat.erf((x - mean) / Math.sqrt(2 * std * std))); - }, - - inv: function(p, mean, std) { - return -1.41421356237309505 * std * jStat.erfcinv(2 * p) + mean; - }, - - mean : function(mean, std) { - return mean; - }, - - median: function median(mean, std) { - return mean; - }, + // Return a clean array. + jProto.toArray = function toArray() { + return this.length > 1 ? slice.call(this) : slice.call(this)[0]; + }; - mode: function (mean, std) { - return mean; - }, - - sample: function sample(mean, std) { - return jStat.randn() * std + mean; - }, - - variance : function(mean, std) { - return std * std; - } -}); - - - -// extend pareto function with static methods -jStat.extend(jStat.pareto, { - pdf: function pdf(x, scale, shape) { - if (x < scale) - return 0; - return (shape * Math.pow(scale, shape)) / Math.pow(x, shape + 1); - }, - - cdf: function cdf(x, scale, shape) { - if (x < scale) - return 0; - return 1 - Math.pow(scale / x, shape); - }, - - inv: function inv(p, scale, shape) { - return scale / Math.pow(1 - p, 1 / shape); - }, - - mean: function mean(scale, shape) { - if (shape <= 1) - return undefined; - return (shape * Math.pow(scale, shape)) / (shape - 1); - }, - - median: function median(scale, shape) { - return scale * (shape * Math.SQRT2); - }, - - mode: function mode(scale, shape) { - return scale; - }, - - variance : function(scale, shape) { - if (shape <= 2) - return undefined; - return (scale*scale * shape) / (Math.pow(shape - 1, 2) * (shape - 2)); - } -}); - - - -// extend studentt function with static methods -jStat.extend(jStat.studentt, { - pdf: function pdf(x, dof) { - dof = dof > 1e100 ? 1e100 : dof; - return (1/(Math.sqrt(dof) * jStat.betafn(0.5, dof/2))) * - Math.pow(1 + ((x * x) / dof), -((dof + 1) / 2)); - }, - - cdf: function cdf(x, dof) { - var dof2 = dof / 2; - return jStat.ibeta((x + Math.sqrt(x * x + dof)) / - (2 * Math.sqrt(x * x + dof)), dof2, dof2); - }, - - inv: function(p, dof) { - var x = jStat.ibetainv(2 * Math.min(p, 1 - p), 0.5 * dof, 0.5); - x = Math.sqrt(dof * (1 - x) / x); - return (p > 0.5) ? x : -x; - }, - - mean: function mean(dof) { - return (dof > 1) ? 0 : undefined; - }, - - median: function median(dof) { - return 0; - }, - - mode: function mode(dof) { - return 0; - }, - - sample: function sample(dof) { - return jStat.randn() * Math.sqrt(dof / (2 * jStat.randg(dof / 2))); - }, - - variance: function variance(dof) { - return (dof > 2) ? dof / (dof - 2) : (dof > 1) ? Infinity : undefined; - } -}); - - - -// extend weibull function with static methods -jStat.extend(jStat.weibull, { - pdf: function pdf(x, scale, shape) { - if (x < 0 || scale < 0 || shape < 0) - return 0; - return (shape / scale) * Math.pow((x / scale), (shape - 1)) * - Math.exp(-(Math.pow((x / scale), shape))); - }, - - cdf: function cdf(x, scale, shape) { - return x < 0 ? 0 : 1 - Math.exp(-Math.pow((x / scale), shape)); - }, - - inv: function(p, scale, shape) { - return scale * Math.pow(-Math.log(1 - p), 1 / shape); - }, - - mean : function(scale, shape) { - return scale * jStat.gammafn(1 + 1 / shape); - }, - - median: function median(scale, shape) { - return scale * Math.pow(Math.log(2), 1 / shape); - }, - - mode: function mode(scale, shape) { - if (shape <= 1) - return 0; - return scale * Math.pow((shape - 1) / shape, 1 / shape); - }, - - sample: function sample(scale, shape) { - return scale * Math.pow(-Math.log(Math.random()), 1 / shape); - }, - - variance: function variance(scale, shape) { - return scale * scale * jStat.gammafn(1 + 2 / shape) - - Math.pow(jStat.weibull.mean(scale, shape), 2); - } -}); - - - -// extend uniform function with static methods -jStat.extend(jStat.uniform, { - pdf: function pdf(x, a, b) { - return (x < a || x > b) ? 0 : 1 / (b - a); - }, - - cdf: function cdf(x, a, b) { - if (x < a) - return 0; - else if (x < b) - return (x - a) / (b - a); - return 1; - }, - - inv: function(p, a, b) { - return a + (p * (b - a)); - }, - - mean: function mean(a, b) { - return 0.5 * (a + b); - }, - - median: function median(a, b) { - return jStat.mean(a, b); - }, - - mode: function mode(a, b) { - throw new Error('mode is not yet implemented'); - }, - - sample: function sample(a, b) { - return (a / 2 + b / 2) + (b / 2 - a / 2) * (2 * Math.random() - 1); - }, - - variance: function variance(a, b) { - return Math.pow(b - a, 2) / 12; - } -}); - - - -// extend uniform function with static methods -jStat.extend(jStat.binomial, { - pdf: function pdf(k, n, p) { - return (p === 0 || p === 1) ? - ((n * p) === k ? 1 : 0) : - jStat.combination(n, k) * Math.pow(p, k) * Math.pow(1 - p, n - k); - }, - - cdf: function cdf(x, n, p) { - var binomarr = [], - k = 0; - if (x < 0) { - return 0; - } - if (x < n) { - for (; k <= x; k++) { - binomarr[ k ] = jStat.binomial.pdf(k, n, p); - } - return jStat.sum(binomarr); - } - return 1; - } -}); - - - -// extend uniform function with static methods -jStat.extend(jStat.negbin, { - pdf: function pdf(k, r, p) { - if (k !== k >>> 0) - return false; - if (k < 0) - return 0; - return jStat.combination(k + r - 1, r - 1) * - Math.pow(1 - p, k) * Math.pow(p, r); - }, - - cdf: function cdf(x, r, p) { - var sum = 0, - k = 0; - if (x < 0) return 0; - for (; k <= x; k++) { - sum += jStat.negbin.pdf(k, r, p); - } - return sum; - } -}); + // Map a function to a matrix or vector. + jProto.map = function map(func, toAlter) { + return jStat(jStat.map(this, func, toAlter)); + }; -// extend uniform function with static methods -jStat.extend(jStat.hypgeom, { - pdf: function pdf(k, N, m, n) { - // Hypergeometric PDF. + // Cumulatively combine the elements of a matrix or vector using a function. + jProto.cumreduce = function cumreduce(func, toAlter) { + return jStat(jStat.cumreduce(this, func, toAlter)); + }; - // A simplification of the CDF algorithm below. - // k = number of successes drawn - // N = population size - // m = number of successes in population - // n = number of items drawn from population + // Destructively alter an array. + jProto.alter = function alter(func) { + jStat.alter(this, func); + return this; + }; - if(k !== k | 0) { - return false; - } else if(k < 0 || k < m - (N - n)) { - // It's impossible to have this few successes drawn. - return 0; - } else if(k > n || k > m) { - // It's impossible to have this many successes drawn. - return 0; - } else if (m * 2 > N) { - // More than half the population is successes. - if(n * 2 > N) { - // More than half the population is sampled. + // Extend prototype with methods that have no argument. + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + jProto[passfunc] = function(func) { + var self = this, + results; + // Check for callback. + if (func) { + setTimeout(function() { + func.call(self, jProto[passfunc].call(self)); + }); + return this; + } + results = jStat[passfunc](this); + return isArray(results) ? jStat(results) : results; + }; + })(funcs[i]); + })('transpose clear symmetric rows cols dimensions diag antidiag'.split(' ')); + + + // Extend prototype with methods that have one argument. + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + jProto[passfunc] = function(index, func) { + var self = this; + // check for callback + if (func) { + setTimeout(function() { + func.call(self, jProto[passfunc].call(self, index)); + }); + return this; + } + return jStat(jStat[passfunc](this, index)); + }; + })(funcs[i]); + })('row col'.split(' ')); - return jStat.hypgeom.pdf(N - m - n + k, N, N - m, N - n) - } else { - // Half or less of the population is sampled. - return jStat.hypgeom.pdf(n - k, N, N - m, n); - } + // Extend prototype with simple shortcut methods. + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + jProto[passfunc] = new Function( + 'return jStat(jStat.' + passfunc + '.apply(null, arguments));'); + })(funcs[i]); + })('create zeros ones rand identity'.split(' ')); - } else if(n * 2 > N) { - // Half or less is successes. - return jStat.hypgeom.pdf(m - k, N, m, N - n); + // Exposing jStat. + return jStat; - } else if(m < n) { - // We want to have the number of things sampled to be less than the - // successes available. So swap the definitions of successful and sampled. - return jStat.hypgeom.pdf(k, N, n, m); - } else { - // If we get here, half or less of the population was sampled, half or - // less of it was successes, and we had fewer sampled things than - // successes. Now we can do this complicated iterative algorithm in an - // efficient way. + }(Math)); + (function(jStat, Math) { - // The basic premise of the algorithm is that we partially normalize our - // intermediate product to keep it in a numerically good region, and then - // finish the normalization at the end. + var isFunction = jStat.utils.isFunction; - // This variable holds the scaled probability of the current number of - // successes. - var scaledPDF = 1; + // Ascending functions for sort + function ascNum(a, b) { return a - b; } - // This keeps track of how much we have normalized. - var samplesDone = 0; + function clip(arg, min, max) { + return Math.max(min, Math.min(arg, max)); + } - for(var i = 0; i < k; i++) { - // For every possible number of successes up to that observed... - while(scaledPDF > 1 && samplesDone < n) { - // Intermediate result is growing too big. Apply some of the - // normalization to shrink everything. + // sum of an array + jStat.sum = function sum(arr) { + var sum = 0; + var i = arr.length; + while (--i >= 0) + sum += arr[i]; + return sum; + }; - scaledPDF *= 1 - (m / (N - samplesDone)); - // Say we've normalized by this sample already. - samplesDone++; - } + // sum squared + jStat.sumsqrd = function sumsqrd(arr) { + var sum = 0; + var i = arr.length; + while (--i >= 0) + sum += arr[i] * arr[i]; + return sum; + }; - // Work out the partially-normalized hypergeometric PDF for the next - // number of successes - scaledPDF *= (n - i) * (m - i) / ((i + 1) * (N - m - n + i + 1)); - } - for(; samplesDone < n; samplesDone++) { - // Apply all the rest of the normalization - scaledPDF *= 1 - (m / (N - samplesDone)); + // sum of squared errors of prediction (SSE) + jStat.sumsqerr = function sumsqerr(arr) { + var mean = jStat.mean(arr); + var sum = 0; + var i = arr.length; + var tmp; + while (--i >= 0) { + tmp = arr[i] - mean; + sum += tmp * tmp; } + return sum; + }; - // Bound answer sanely before returning. - return Math.min(1, Math.max(0, scaledPDF)); - } - }, + // sum of an array in each row + jStat.sumrow = function sumrow(arr) { + var sum = 0; + var i = arr.length; + while (--i >= 0) + sum += arr[i]; + return sum; + }; - cdf: function cdf(x, N, m, n) { - // Hypergeometric CDF. + // product of an array + jStat.product = function product(arr) { + var prod = 1; + var i = arr.length; + while (--i >= 0) + prod *= arr[i]; + return prod; + }; - // This algorithm is due to Prof. Thomas S. Ferguson, , - // and comes from his hypergeometric test calculator at - // . - // x = number of successes drawn - // N = population size - // m = number of successes in population - // n = number of items drawn from population + // minimum value of an array + jStat.min = function min(arr) { + var low = arr[0]; + var i = 0; + while (++i < arr.length) + if (arr[i] < low) + low = arr[i]; + return low; + }; - if(x < 0 || x < m - (N - n)) { - // It's impossible to have this few successes drawn or fewer. - return 0; - } else if(x >= n || x >= m) { - // We will always have this many successes or fewer. - return 1; - } else if (m * 2 > N) { - // More than half the population is successes. - if(n * 2 > N) { - // More than half the population is sampled. + // maximum value of an array + jStat.max = function max(arr) { + var high = arr[0]; + var i = 0; + while (++i < arr.length) + if (arr[i] > high) + high = arr[i]; + return high; + }; - return jStat.hypgeom.cdf(N - m - n + x, N, N - m, N - n) - } else { - // Half or less of the population is sampled. - return 1 - jStat.hypgeom.cdf(n - x - 1, N, N - m, n); + // unique values of an array + jStat.unique = function unique(arr) { + var hash = {}, _arr = []; + for(var i = 0; i < arr.length; i++) { + if (!hash[arr[i]]) { + hash[arr[i]] = true; + _arr.push(arr[i]); + } } + return _arr; + }; + + + // mean value of an array + jStat.mean = function mean(arr) { + return jStat.sum(arr) / arr.length; + }; - } else if(n * 2 > N) { - // Half or less is successes. - return 1 - jStat.hypgeom.cdf(m - x - 1, N, m, N - n); + // mean squared error (MSE) + jStat.meansqerr = function meansqerr(arr) { + return jStat.sumsqerr(arr) / arr.length; + }; - } else if(m < n) { - // We want to have the number of things sampled to be less than the - // successes available. So swap the definitions of successful and sampled. - return jStat.hypgeom.cdf(x, N, n, m); - } else { - // If we get here, half or less of the population was sampled, half or - // less of it was successes, and we had fewer sampled things than - // successes. Now we can do this complicated iterative algorithm in an - // efficient way. - // The basic premise of the algorithm is that we partially normalize our - // intermediate sum to keep it in a numerically good region, and then - // finish the normalization at the end. + // geometric mean of an array + jStat.geomean = function geomean(arr) { + return Math.pow(jStat.product(arr), 1 / arr.length); + }; - // Holds the intermediate, scaled total CDF. - var scaledCDF = 1; - // This variable holds the scaled probability of the current number of - // successes. - var scaledPDF = 1; + // median of an array + jStat.median = function median(arr) { + var arrlen = arr.length; + var _arr = arr.slice().sort(ascNum); + // check if array is even or odd, then return the appropriate + return !(arrlen & 1) + ? (_arr[(arrlen / 2) - 1 ] + _arr[(arrlen / 2)]) / 2 + : _arr[(arrlen / 2) | 0 ]; + }; - // This keeps track of how much we have normalized. - var samplesDone = 0; - for(var i = 0; i < x; i++) { - // For every possible number of successes up to that observed... + // cumulative sum of an array + jStat.cumsum = function cumsum(arr) { + return jStat.cumreduce(arr, function (a, b) { return a + b; }); + }; - while(scaledCDF > 1 && samplesDone < n) { - // Intermediate result is growing too big. Apply some of the - // normalization to shrink everything. - var factor = 1 - (m / (N - samplesDone)); + // cumulative product of an array + jStat.cumprod = function cumprod(arr) { + return jStat.cumreduce(arr, function (a, b) { return a * b; }); + }; - scaledPDF *= factor; - scaledCDF *= factor; - // Say we've normalized by this sample already. - samplesDone++; - } + // successive differences of a sequence + jStat.diff = function diff(arr) { + var diffs = []; + var arrLen = arr.length; + var i; + for (var i = 1; i < arrLen; i++) + diffs.push(arr[i] - arr[i - 1]); + return diffs; + }; - // Work out the partially-normalized hypergeometric PDF for the next - // number of successes - scaledPDF *= (n - i) * (m - i) / ((i + 1) * (N - m - n + i + 1)); - // Add to the CDF answer. - scaledCDF += scaledPDF; + // ranks of an array + jStat.rank = function (arr) { + var arrlen = arr.length; + var sorted = arr.slice().sort(ascNum); + var ranks = new Array(arrlen); + for (var i = 0; i < arrlen; i++) { + var first = sorted.indexOf(arr[i]); + var last = sorted.lastIndexOf(arr[i]); + if (first === last) { + var val = first; + } else { + var val = (first + last) / 2; + } + ranks[i] = val + 1; } + return ranks; + }; - for(; samplesDone < n; samplesDone++) { - // Apply all the rest of the normalization - scaledCDF *= 1 - (m / (N - samplesDone)); + + // mode of an array + // if there are multiple modes of an array, return all of them + // is this the appropriate way of handling it? + jStat.mode = function mode(arr) { + var arrLen = arr.length; + var _arr = arr.slice().sort(ascNum); + var count = 1; + var maxCount = 0; + var numMaxCount = 0; + var mode_arr = []; + var i; + + for (var i = 0; i < arrLen; i++) { + if (_arr[i] === _arr[i + 1]) { + count++; + } else { + if (count > maxCount) { + mode_arr = [_arr[i]]; + maxCount = count; + numMaxCount = 0; + } + // are there multiple max counts + else if (count === maxCount) { + mode_arr.push(_arr[i]); + numMaxCount++; + } + // resetting count for new value in array + count = 1; + } } - // Bound answer sanely before returning. - return Math.min(1, Math.max(0, scaledCDF)); - } - } -}); + return numMaxCount === 0 ? mode_arr[0] : mode_arr; + }; + // range of an array + jStat.range = function range(arr) { + return jStat.max(arr) - jStat.min(arr); + }; -// extend uniform function with static methods -jStat.extend(jStat.poisson, { - pdf: function pdf(k, l) { - if (l < 0 || (k % 1) !== 0 || k < 0) { - return 0; - } + // variance of an array + // flag = true indicates sample instead of population + jStat.variance = function variance(arr, flag) { + return jStat.sumsqerr(arr) / (arr.length - (flag ? 1 : 0)); + }; - return Math.pow(l, k) * Math.exp(-l) / jStat.factorial(k); - }, + // pooled variance of an array of arrays + jStat.pooledvariance = function pooledvariance(arr) { + var sumsqerr = arr.reduce(function (a, samples) {return a + jStat.sumsqerr(samples);}, 0); + var count = arr.reduce(function (a, samples) {return a + samples.length;}, 0); + return sumsqerr / (count - arr.length); + }; - cdf: function cdf(x, l) { - var sumarr = [], - k = 0; - if (x < 0) return 0; - for (; k <= x; k++) { - sumarr.push(jStat.poisson.pdf(k, l)); - } - return jStat.sum(sumarr); - }, - - mean : function(l) { - return l; - }, - - variance : function(l) { - return l; - }, - - sample: function sample(l) { - var p = 1, k = 0, L = Math.exp(-l); - do { - k++; - p *= Math.random(); - } while (p > L); - return k - 1; - } -}); - -// extend triangular function with static methods -jStat.extend(jStat.triangular, { - pdf: function pdf(x, a, b, c) { - if (b <= a || c < a || c > b) { - return NaN; - } else { - if (x < a || x > b) { - return 0; - } else if (x < c) { - return (2 * (x - a)) / ((b - a) * (c - a)); - } else if (x === c) { - return (2 / (b - a)); - } else { // x > c - return (2 * (b - x)) / ((b - a) * (b - c)); + // deviation of an array + jStat.deviation = function (arr) { + var mean = jStat.mean(arr); + var arrlen = arr.length; + var dev = new Array(arrlen); + for (var i = 0; i < arrlen; i++) { + dev[i] = arr[i] - mean; } - } - }, - - cdf: function cdf(x, a, b, c) { - if (b <= a || c < a || c > b) - return NaN; - if (x <= a) - return 0; - else if (x >= b) - return 1; - if (x <= c) - return Math.pow(x - a, 2) / ((b - a) * (c - a)); - else // x > c - return 1 - Math.pow(b - x, 2) / ((b - a) * (b - c)); - }, - - inv: function inv(p, a, b, c) { - if (b <= a || c < a || c > b) { - return NaN; - } else { - if (p <= ((c - a) / (b - a))) { - return a + (b - a) * Math.sqrt(p * ((c - a) / (b - a))); - } else { // p > ((c - a) / (b - a)) - return a + (b - a) * (1 - Math.sqrt((1 - p) * (1 - ((c - a) / (b - a))))); + return dev; + }; + + // standard deviation of an array + // flag = true indicates sample instead of population + jStat.stdev = function stdev(arr, flag) { + return Math.sqrt(jStat.variance(arr, flag)); + }; + + // pooled standard deviation of an array of arrays + jStat.pooledstdev = function pooledstdev(arr) { + return Math.sqrt(jStat.pooledvariance(arr)); + }; + + // mean deviation (mean absolute deviation) of an array + jStat.meandev = function meandev(arr) { + var mean = jStat.mean(arr); + var a = []; + for (var i = arr.length - 1; i >= 0; i--) { + a.push(Math.abs(arr[i] - mean)); } - } - }, + return jStat.mean(a); + }; - mean: function mean(a, b, c) { - return (a + b + c) / 3; - }, - median: function median(a, b, c) { - if (c <= (a + b) / 2) { - return b - Math.sqrt((b - a) * (b - c)) / Math.sqrt(2); - } else if (c > (a + b) / 2) { - return a + Math.sqrt((b - a) * (c - a)) / Math.sqrt(2); - } - }, - - mode: function mode(a, b, c) { - return c; - }, - - sample: function sample(a, b, c) { - var u = Math.random(); - if (u < ((c - a) / (b - a))) - return a + Math.sqrt(u * (b - a) * (c - a)) - return b - Math.sqrt((1 - u) * (b - a) * (b - c)); - }, - - variance: function variance(a, b, c) { - return (a * a + b * b + c * c - a * b - a * c - b * c) / 18; - } -}); - - -// extend arcsine function with static methods -jStat.extend(jStat.arcsine, { - pdf: function pdf(x, a, b) { - if (b <= a) return NaN; - - return (x <= a || x >= b) ? 0 : - (2 / Math.PI) * - Math.pow(Math.pow(b - a, 2) - - Math.pow(2 * x - a - b, 2), -0.5); - }, - - cdf: function cdf(x, a, b) { - if (x < a) - return 0; - else if (x < b) - return (2 / Math.PI) * Math.asin(Math.sqrt((x - a)/(b - a))); - return 1; - }, - - inv: function(p, a, b) { - return a + (0.5 - 0.5 * Math.cos(Math.PI * p)) * (b - a); - }, - - mean: function mean(a, b) { - if (b <= a) return NaN; - return (a + b) / 2; - }, - - median: function median(a, b) { - if (b <= a) return NaN; - return (a + b) / 2; - }, - - mode: function mode(a, b) { - throw new Error('mode is not yet implemented'); - }, - - sample: function sample(a, b) { - return ((a + b) / 2) + ((b - a) / 2) * - Math.sin(2 * Math.PI * jStat.uniform.sample(0, 1)); - }, - - variance: function variance(a, b) { - if (b <= a) return NaN; - return Math.pow(b - a, 2) / 8; - } -}); - - -function laplaceSign(x) { return x / Math.abs(x); } - -jStat.extend(jStat.laplace, { - pdf: function pdf(x, mu, b) { - return (b <= 0) ? 0 : (Math.exp(-Math.abs(x - mu) / b)) / (2 * b); - }, - - cdf: function cdf(x, mu, b) { - if (b <= 0) { return 0; } - - if(x < mu) { - return 0.5 * Math.exp((x - mu) / b); - } else { - return 1 - 0.5 * Math.exp(- (x - mu) / b); - } - }, - - mean: function(mu, b) { - return mu; - }, - - median: function(mu, b) { - return mu; - }, - - mode: function(mu, b) { - return mu; - }, - - variance: function(mu, b) { - return 2 * b * b; - }, - - sample: function sample(mu, b) { - var u = Math.random() - 0.5; - - return mu - (b * laplaceSign(u) * Math.log(1 - (2 * Math.abs(u)))); - } -}); - -function tukeyWprob(w, rr, cc) { - var nleg = 12; - var ihalf = 6; - - var C1 = -30; - var C2 = -50; - var C3 = 60; - var bb = 8; - var wlar = 3; - var wincr1 = 2; - var wincr2 = 3; - var xleg = [ - 0.981560634246719250690549090149, - 0.904117256370474856678465866119, - 0.769902674194304687036893833213, - 0.587317954286617447296702418941, - 0.367831498998180193752691536644, - 0.125233408511468915472441369464 - ]; - var aleg = [ - 0.047175336386511827194615961485, - 0.106939325995318430960254718194, - 0.160078328543346226334652529543, - 0.203167426723065921749064455810, - 0.233492536538354808760849898925, - 0.249147045813402785000562436043 - ]; - - var qsqz = w * 0.5; - - // if w >= 16 then the integral lower bound (occurs for c=20) - // is 0.99999999999995 so return a value of 1. - - if (qsqz >= bb) - return 1.0; - - // find (f(w/2) - 1) ^ cc - // (first term in integral of hartley's form). - - var pr_w = 2 * jStat.normal.cdf(qsqz, 0, 1, 1, 0) - 1; // erf(qsqz / M_SQRT2) - // if pr_w ^ cc < 2e-22 then set pr_w = 0 - if (pr_w >= Math.exp(C2 / cc)) - pr_w = Math.pow(pr_w, cc); - else - pr_w = 0.0; - - // if w is large then the second component of the - // integral is small, so fewer intervals are needed. - - var wincr; - if (w > wlar) - wincr = wincr1; - else - wincr = wincr2; - - // find the integral of second term of hartley's form - // for the integral of the range for equal-length - // intervals using legendre quadrature. limits of - // integration are from (w/2, 8). two or three - // equal-length intervals are used. - - // blb and bub are lower and upper limits of integration. - - var blb = qsqz; - var binc = (bb - qsqz) / wincr; - var bub = blb + binc; - var einsum = 0.0; - - // integrate over each interval - - var cc1 = cc - 1.0; - for (var wi = 1; wi <= wincr; wi++) { - var elsum = 0.0; - var a = 0.5 * (bub + blb); - - // legendre quadrature with order = nleg - - var b = 0.5 * (bub - blb); - - for (var jj = 1; jj <= nleg; jj++) { - var j, xx; - if (ihalf < jj) { - j = (nleg - jj) + 1; - xx = xleg[j-1]; - } else { - j = jj; - xx = -xleg[j-1]; + // median deviation (median absolute deviation) of an array + jStat.meddev = function meddev(arr) { + var median = jStat.median(arr); + var a = []; + for (var i = arr.length - 1; i >= 0; i--) { + a.push(Math.abs(arr[i] - median)); } - var c = b * xx; - var ac = a + c; + return jStat.median(a); + }; - // if exp(-qexpo/2) < 9e-14, - // then doesn't contribute to integral - var qexpo = ac * ac; - if (qexpo > C3) - break; + // coefficient of variation + jStat.coeffvar = function coeffvar(arr) { + return jStat.stdev(arr) / jStat.mean(arr); + }; + - var pplus = 2 * jStat.normal.cdf(ac, 0, 1, 1, 0); - var pminus= 2 * jStat.normal.cdf(ac, w, 1, 1, 0); + // quartiles of an array + jStat.quartiles = function quartiles(arr) { + var arrlen = arr.length; + var _arr = arr.slice().sort(ascNum); + return [ + _arr[ Math.round((arrlen) / 4) - 1 ], + _arr[ Math.round((arrlen) / 2) - 1 ], + _arr[ Math.round((arrlen) * 3 / 4) - 1 ] + ]; + }; - // if rinsum ^ (cc-1) < 9e-14, - // then doesn't contribute to integral - var rinsum = (pplus * 0.5) - (pminus * 0.5); - if (rinsum >= Math.exp(C1 / cc1)) { - rinsum = (aleg[j-1] * Math.exp(-(0.5 * qexpo))) * Math.pow(rinsum, cc1); - elsum += rinsum; + // Arbitary quantiles of an array. Direct port of the scipy.stats + // implementation by Pierre GF Gerard-Marchant. + jStat.quantiles = function quantiles(arr, quantilesArray, alphap, betap) { + var sortedArray = arr.slice().sort(ascNum); + var quantileVals = [quantilesArray.length]; + var n = arr.length; + var i, p, m, aleph, k, gamma; + + if (typeof alphap === 'undefined') + alphap = 3 / 8; + if (typeof betap === 'undefined') + betap = 3 / 8; + + for (var i = 0; i < quantilesArray.length; i++) { + p = quantilesArray[i]; + m = alphap + p * (1 - alphap - betap); + aleph = n * p + m; + k = Math.floor(clip(aleph, 1, n - 1)); + gamma = clip(aleph - k, 0, 1); + quantileVals[i] = (1 - gamma) * sortedArray[k - 1] + gamma * sortedArray[k]; } - } - elsum *= (((2.0 * b) * cc) / Math.sqrt(2 * Math.PI)); - einsum += elsum; - blb = bub; - bub += binc; - } - - // if pr_w ^ rr < 9e-14, then return 0 - pr_w += einsum; - if (pr_w <= Math.exp(C1 / rr)) - return 0; - - pr_w = Math.pow(pr_w, rr); - if (pr_w >= 1) // 1 was iMax was eps - return 1; - return pr_w; -} - -function tukeyQinv(p, c, v) { - var p0 = 0.322232421088; - var q0 = 0.993484626060e-01; - var p1 = -1.0; - var q1 = 0.588581570495; - var p2 = -0.342242088547; - var q2 = 0.531103462366; - var p3 = -0.204231210125; - var q3 = 0.103537752850; - var p4 = -0.453642210148e-04; - var q4 = 0.38560700634e-02; - var c1 = 0.8832; - var c2 = 0.2368; - var c3 = 1.214; - var c4 = 1.208; - var c5 = 1.4142; - var vmax = 120.0; - - var ps = 0.5 - 0.5 * p; - var yi = Math.sqrt(Math.log(1.0 / (ps * ps))); - var t = yi + (((( yi * p4 + p3) * yi + p2) * yi + p1) * yi + p0) - / (((( yi * q4 + q3) * yi + q2) * yi + q1) * yi + q0); - if (v < vmax) t += (t * t * t + t) / v / 4.0; - var q = c1 - c2 * t; - if (v < vmax) q += -c3 / v + c4 * t / v; - return t * (q * Math.log(c - 1.0) + c5); -} - -jStat.extend(jStat.tukey, { - cdf: function cdf(q, nmeans, df) { - // Identical implementation as the R ptukey() function as of commit 68947 - var rr = 1; - var cc = nmeans; - - var nlegq = 16; - var ihalfq = 8; - - var eps1 = -30.0; - var eps2 = 1.0e-14; - var dhaf = 100.0; - var dquar = 800.0; - var deigh = 5000.0; - var dlarg = 25000.0; - var ulen1 = 1.0; - var ulen2 = 0.5; - var ulen3 = 0.25; - var ulen4 = 0.125; - var xlegq = [ - 0.989400934991649932596154173450, - 0.944575023073232576077988415535, - 0.865631202387831743880467897712, - 0.755404408355003033895101194847, - 0.617876244402643748446671764049, - 0.458016777657227386342419442984, - 0.281603550779258913230460501460, - 0.950125098376374401853193354250e-1 - ]; - var alegq = [ - 0.271524594117540948517805724560e-1, - 0.622535239386478928628438369944e-1, - 0.951585116824927848099251076022e-1, - 0.124628971255533872052476282192, - 0.149595988816576732081501730547, - 0.169156519395002538189312079030, - 0.182603415044923588866763667969, - 0.189450610455068496285396723208 - ]; - - if (q <= 0) - return 0; - - // df must be > 1 - // there must be at least two values - - if (df < 2 || rr < 1 || cc < 2) return NaN; - - if (!Number.isFinite(q)) - return 1; - - if (df > dlarg) - return tukeyWprob(q, rr, cc); - - // calculate leading constant - - var f2 = df * 0.5; - var f2lf = ((f2 * Math.log(df)) - (df * Math.log(2))) - jStat.gammaln(f2); - var f21 = f2 - 1.0; - - // integral is divided into unit, half-unit, quarter-unit, or - // eighth-unit length intervals depending on the value of the - // degrees of freedom. - - var ff4 = df * 0.25; - var ulen; - if (df <= dhaf) ulen = ulen1; - else if (df <= dquar) ulen = ulen2; - else if (df <= deigh) ulen = ulen3; - else ulen = ulen4; - - f2lf += Math.log(ulen); - - // integrate over each subinterval - - var ans = 0.0; - - for (var i = 1; i <= 50; i++) { - var otsum = 0.0; - - // legendre quadrature with order = nlegq - // nodes (stored in xlegq) are symmetric around zero. - - var twa1 = (2 * i - 1) * ulen; - - for (var jj = 1; jj <= nlegq; jj++) { - var j, t1; - if (ihalfq < jj) { - j = jj - ihalfq - 1; - t1 = (f2lf + (f21 * Math.log(twa1 + (xlegq[j] * ulen)))) - - (((xlegq[j] * ulen) + twa1) * ff4); - } else { - j = jj - 1; - t1 = (f2lf + (f21 * Math.log(twa1 - (xlegq[j] * ulen)))) - + (((xlegq[j] * ulen) - twa1) * ff4); - } - // if exp(t1) < 9e-14, then doesn't contribute to integral - var qsqz; - if (t1 >= eps1) { - if (ihalfq < jj) { - qsqz = q * Math.sqrt(((xlegq[j] * ulen) + twa1) * 0.5); - } else { - qsqz = q * Math.sqrt(((-(xlegq[j] * ulen)) + twa1) * 0.5); - } + return quantileVals; + }; - // call wprob to find integral of range portion + // Returns the k-th percentile of values in a range, where k is in the + // range 0..1, exclusive. + jStat.percentile = function percentile(arr, k) { + var _arr = arr.slice().sort(ascNum); + var realIndex = k * (_arr.length - 1); + var index = parseInt(realIndex); + var frac = realIndex - index; - var wprb = tukeyWprob(qsqz, rr, cc); - var rotsum = (wprb * alegq[j]) * Math.exp(t1); - otsum += rotsum; - } - // end legendre integral for interval i - // L200: + if (index + 1 < _arr.length) { + return _arr[index] * (1 - frac) + _arr[index + 1] * frac; + } else { + return _arr[index]; } + }; - // if integral for interval i < 1e-14, then stop. - // However, in order to avoid small area under left tail, - // at least 1 / ulen intervals are calculated. - if (i * ulen >= 1.0 && otsum <= eps2) - break; - // end of interval i - // L330: + // The percentile rank of score in a given array. Returns the percentage + // of all values in the input array that are less than (kind='strict') or + // less or equal than (kind='weak') score. Default is weak. + jStat.percentileOfScore = function percentileOfScore(arr, score, kind) { + var counter = 0; + var len = arr.length; + var strict = false; + var value, i; - ans += otsum; - } + if (kind === 'strict') + strict = true; - if (otsum > eps2) { // not converged - throw new Error('tukey.cdf failed to converge'); - } - if (ans > 1) - ans = 1; - return ans; - }, + for (var i = 0; i < len; i++) { + value = arr[i]; + if ((strict && value < score) || + (!strict && value <= score)) { + counter++; + } + } + + return counter / len; + }; + + + // Histogram (bin count) data + jStat.histogram = function histogram(arr, bins) { + var first = jStat.min(arr); + var binCnt = bins || 4; + var binWidth = (jStat.max(arr) - first) / binCnt; + var len = arr.length; + var bins = []; + var i; + + for (var i = 0; i < binCnt; i++) + bins[i] = 0; + for (var i = 0; i < len; i++) + bins[Math.min(Math.floor(((arr[i] - first) / binWidth)), binCnt - 1)] += 1; + + return bins; + }; + + + // covariance of two arrays + jStat.covariance = function covariance(arr1, arr2) { + var u = jStat.mean(arr1); + var v = jStat.mean(arr2); + var arr1Len = arr1.length; + var sq_dev = new Array(arr1Len); + var i; + + for (var i = 0; i < arr1Len; i++) + sq_dev[i] = (arr1[i] - u) * (arr2[i] - v); + + return jStat.sum(sq_dev) / (arr1Len - 1); + }; + + + // (pearson's) population correlation coefficient, rho + jStat.corrcoeff = function corrcoeff(arr1, arr2) { + return jStat.covariance(arr1, arr2) / + jStat.stdev(arr1, 1) / + jStat.stdev(arr2, 1); + }; + + // (spearman's) rank correlation coefficient, sp + jStat.spearmancoeff = function (arr1, arr2) { + arr1 = jStat.rank(arr1); + arr2 = jStat.rank(arr2); + //return pearson's correlation of the ranks: + return jStat.corrcoeff(arr1, arr2); + }; + + + // statistical standardized moments (general form of skew/kurt) + jStat.stanMoment = function stanMoment(arr, n) { + var mu = jStat.mean(arr); + var sigma = jStat.stdev(arr); + var len = arr.length; + var skewSum = 0; + + for (var i = 0; i < len; i++) + skewSum += Math.pow((arr[i] - mu) / sigma, n); + + return skewSum / arr.length; + }; + + // (pearson's) moment coefficient of skewness + jStat.skewness = function skewness(arr) { + return jStat.stanMoment(arr, 3); + }; + + // (pearson's) (excess) kurtosis + jStat.kurtosis = function kurtosis(arr) { + return jStat.stanMoment(arr, 4) - 3; + }; + + + var jProto = jStat.prototype; + + + // Extend jProto with method for calculating cumulative sums and products. + // This differs from the similar extension below as cumsum and cumprod should + // not be run again in the case fullbool === true. + // If a matrix is passed, automatically assume operation should be done on the + // columns. + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + // If a matrix is passed, automatically assume operation should be done on + // the columns. + jProto[passfunc] = function(fullbool, func) { + var arr = []; + var i = 0; + var tmpthis = this; + // Assignment reassignation depending on how parameters were passed in. + if (isFunction(fullbool)) { + func = fullbool; + fullbool = false; + } + // Check if a callback was passed with the function. + if (func) { + setTimeout(function() { + func.call(tmpthis, jProto[passfunc].call(tmpthis, fullbool)); + }); + return this; + } + // Check if matrix and run calculations. + if (this.length > 1) { + tmpthis = fullbool === true ? this : this.transpose(); + for (; i < tmpthis.length; i++) + arr[i] = jStat[passfunc](tmpthis[i]); + return arr; + } + // Pass fullbool if only vector, not a matrix. for variance and stdev. + return jStat[passfunc](this[0], fullbool); + }; + })(funcs[i]); + })(('cumsum cumprod').split(' ')); + + + // Extend jProto with methods which don't require arguments and work on columns. + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + // If a matrix is passed, automatically assume operation should be done on + // the columns. + jProto[passfunc] = function(fullbool, func) { + var arr = []; + var i = 0; + var tmpthis = this; + // Assignment reassignation depending on how parameters were passed in. + if (isFunction(fullbool)) { + func = fullbool; + fullbool = false; + } + // Check if a callback was passed with the function. + if (func) { + setTimeout(function() { + func.call(tmpthis, jProto[passfunc].call(tmpthis, fullbool)); + }); + return this; + } + // Check if matrix and run calculations. + if (this.length > 1) { + if (passfunc !== 'sumrow') + tmpthis = fullbool === true ? this : this.transpose(); + for (; i < tmpthis.length; i++) + arr[i] = jStat[passfunc](tmpthis[i]); + return fullbool === true + ? jStat[passfunc](jStat.utils.toVector(arr)) + : arr; + } + // Pass fullbool if only vector, not a matrix. for variance and stdev. + return jStat[passfunc](this[0], fullbool); + }; + })(funcs[i]); + })(('sum sumsqrd sumsqerr sumrow product min max unique mean meansqerr ' + + 'geomean median diff rank mode range variance deviation stdev meandev ' + + 'meddev coeffvar quartiles histogram skewness kurtosis').split(' ')); + + + // Extend jProto with functions that take arguments. Operations on matrices are + // done on columns. + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + jProto[passfunc] = function() { + var arr = []; + var i = 0; + var tmpthis = this; + var args = Array.prototype.slice.call(arguments); + + // If the last argument is a function, we assume it's a callback; we + // strip the callback out and call the function again. + if (isFunction(args[args.length - 1])) { + var callbackFunction = args[args.length - 1]; + var argsToPass = args.slice(0, args.length - 1); + + setTimeout(function() { + callbackFunction.call(tmpthis, + jProto[passfunc].apply(tmpthis, argsToPass)); + }); + return this; + + // Otherwise we curry the function args and call normally. + } else { + var callbackFunction = undefined; + var curriedFunction = function curriedFunction(vector) { + return jStat[passfunc].apply(tmpthis, [vector].concat(args)); + }; + } + + // If this is a matrix, run column-by-column. + if (this.length > 1) { + tmpthis = tmpthis.transpose(); + for (; i < tmpthis.length; i++) + arr[i] = curriedFunction(tmpthis[i]); + return arr; + } + + // Otherwise run on the vector. + return curriedFunction(this[0]); + }; + })(funcs[i]); + })('quantiles percentileOfScore'.split(' ')); + + }(jStat, Math)); + // Special functions // + (function(jStat, Math) { + + // Log-gamma function + jStat.gammaln = function gammaln(x) { + var j = 0; + var cof = [ + 76.18009172947146, -86.50532032941677, 24.01409824083091, + -1.231739572450155, 0.1208650973866179e-2, -0.5395239384953e-5 + ]; + var ser = 1.000000000190015; + var xx, y, tmp; + tmp = (y = xx = x) + 5.5; + tmp -= (xx + 0.5) * Math.log(tmp); + for (; j < 6; j++) + ser += cof[j] / ++y; + return Math.log(2.5066282746310005 * ser / xx) - tmp; + }; + + + // gamma of x + jStat.gammafn = function gammafn(x) { + var p = [-1.716185138865495, 24.76565080557592, -379.80425647094563, + 629.3311553128184, 866.9662027904133, -31451.272968848367, + -36144.413418691176, 66456.14382024054 + ]; + var q = [-30.8402300119739, 315.35062697960416, -1015.1563674902192, + -3107.771671572311, 22538.118420980151, 4755.8462775278811, + -134659.9598649693, -115132.2596755535]; + var fact = false; + var n = 0; + var xden = 0; + var xnum = 0; + var y = x; + var i, z, yi, res; + if (y <= 0) { + res = y % 1 + 3.6e-16; + if (res) { + fact = (!(y & 1) ? 1 : -1) * Math.PI / Math.sin(Math.PI * res); + y = 1 - y; + } else { + return Infinity; + } + } + yi = y; + if (y < 1) { + z = y++; + } else { + z = (y -= n = (y | 0) - 1) - 1; + } + for (var i = 0; i < 8; ++i) { + xnum = (xnum + p[i]) * z; + xden = xden * z + q[i]; + } + res = xnum / xden + 1; + if (yi < y) { + res /= yi; + } else if (yi > y) { + for (var i = 0; i < n; ++i) { + res *= y; + y++; + } + } + if (fact) { + res = fact / res; + } + return res; + }; + + + // lower incomplete gamma function, which is usually typeset with a + // lower-case greek gamma as the function symbol + jStat.gammap = function gammap(a, x) { + return jStat.lowRegGamma(a, x) * jStat.gammafn(a); + }; + + + // The lower regularized incomplete gamma function, usually written P(a,x) + jStat.lowRegGamma = function lowRegGamma(a, x) { + var aln = jStat.gammaln(a); + var ap = a; + var sum = 1 / a; + var del = sum; + var b = x + 1 - a; + var c = 1 / 1.0e-30; + var d = 1 / b; + var h = d; + var i = 1; + // calculate maximum number of itterations required for a + var ITMAX = -~(Math.log((a >= 1) ? a : 1 / a) * 8.5 + a * 0.4 + 17); + var an; + + if (x < 0 || a <= 0) { + return NaN; + } else if (x < a + 1) { + for (; i <= ITMAX; i++) { + sum += del *= x / ++ap; + } + return (sum * Math.exp(-x + a * Math.log(x) - (aln))); + } + + for (; i <= ITMAX; i++) { + an = -i * (i - a); + b += 2; + d = an * d + b; + c = b + an / c; + d = 1 / d; + h *= d * c; + } + + return (1 - h * Math.exp(-x + a * Math.log(x) - (aln))); + }; + + // natural log factorial of n + jStat.factorialln = function factorialln(n) { + return n < 0 ? NaN : jStat.gammaln(n + 1); + }; + + // factorial of n + jStat.factorial = function factorial(n) { + return n < 0 ? NaN : jStat.gammafn(n + 1); + }; + + // combinations of n, m + jStat.combination = function combination(n, m) { + // make sure n or m don't exceed the upper limit of usable values + return (n > 170 || m > 170) + ? Math.exp(jStat.combinationln(n, m)) + : (jStat.factorial(n) / jStat.factorial(m)) / jStat.factorial(n - m); + }; + + + jStat.combinationln = function combinationln(n, m){ + return jStat.factorialln(n) - jStat.factorialln(m) - jStat.factorialln(n - m); + }; + + + // permutations of n, m + jStat.permutation = function permutation(n, m) { + return jStat.factorial(n) / jStat.factorial(n - m); + }; + + + // beta function + jStat.betafn = function betafn(x, y) { + // ensure arguments are positive + if (x <= 0 || y <= 0) + return undefined; + // make sure x + y doesn't exceed the upper limit of usable values + return (x + y > 170) + ? Math.exp(jStat.betaln(x, y)) + : jStat.gammafn(x) * jStat.gammafn(y) / jStat.gammafn(x + y); + }; + + + // natural logarithm of beta function + jStat.betaln = function betaln(x, y) { + return jStat.gammaln(x) + jStat.gammaln(y) - jStat.gammaln(x + y); + }; + + + // Evaluates the continued fraction for incomplete beta function by modified + // Lentz's method. + jStat.betacf = function betacf(x, a, b) { + var fpmin = 1e-30; + var m = 1; + var qab = a + b; + var qap = a + 1; + var qam = a - 1; + var c = 1; + var d = 1 - qab * x / qap; + var m2, aa, del, h; + + // These q's will be used in factors that occur in the coefficients + if (Math.abs(d) < fpmin) + d = fpmin; + d = 1 / d; + h = d; + + for (; m <= 100; m++) { + m2 = 2 * m; + aa = m * (b - m) * x / ((qam + m2) * (a + m2)); + // One step (the even one) of the recurrence + d = 1 + aa * d; + if (Math.abs(d) < fpmin) + d = fpmin; + c = 1 + aa / c; + if (Math.abs(c) < fpmin) + c = fpmin; + d = 1 / d; + h *= d * c; + aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2)); + // Next step of the recurrence (the odd one) + d = 1 + aa * d; + if (Math.abs(d) < fpmin) + d = fpmin; + c = 1 + aa / c; + if (Math.abs(c) < fpmin) + c = fpmin; + d = 1 / d; + del = d * c; + h *= del; + if (Math.abs(del - 1.0) < 3e-7) + break; + } + + return h; + }; + + + // Returns the inverse of the lower regularized inomplete gamma function + jStat.gammapinv = function gammapinv(p, a) { + var j = 0; + var a1 = a - 1; + var EPS = 1e-8; + var gln = jStat.gammaln(a); + var x, err, t, u, pp, lna1, afac; + + if (p >= 1) + return Math.max(100, a + 100 * Math.sqrt(a)); + if (p <= 0) + return 0; + if (a > 1) { + lna1 = Math.log(a1); + afac = Math.exp(a1 * (lna1 - 1) - gln); + pp = (p < 0.5) ? p : 1 - p; + t = Math.sqrt(-2 * Math.log(pp)); + x = (2.30753 + t * 0.27061) / (1 + t * (0.99229 + t * 0.04481)) - t; + if (p < 0.5) + x = -x; + x = Math.max(1e-3, + a * Math.pow(1 - 1 / (9 * a) - x / (3 * Math.sqrt(a)), 3)); + } else { + t = 1 - a * (0.253 + a * 0.12); + if (p < t) + x = Math.pow(p / t, 1 / a); + else + x = 1 - Math.log(1 - (p - t) / (1 - t)); + } + + for(; j < 12; j++) { + if (x <= 0) + return 0; + err = jStat.lowRegGamma(a, x) - p; + if (a > 1) + t = afac * Math.exp(-(x - a1) + a1 * (Math.log(x) - lna1)); + else + t = Math.exp(-x + a1 * Math.log(x) - gln); + u = err / t; + x -= (t = u / (1 - 0.5 * Math.min(1, u * ((a - 1) / x - 1)))); + if (x <= 0) + x = 0.5 * (x + t); + if (Math.abs(t) < EPS * x) + break; + } + + return x; + }; + + + // Returns the error function erf(x) + jStat.erf = function erf(x) { + var cof = [-1.3026537197817094, 6.4196979235649026e-1, 1.9476473204185836e-2, + -9.561514786808631e-3, -9.46595344482036e-4, 3.66839497852761e-4, + 4.2523324806907e-5, -2.0278578112534e-5, -1.624290004647e-6, + 1.303655835580e-6, 1.5626441722e-8, -8.5238095915e-8, + 6.529054439e-9, 5.059343495e-9, -9.91364156e-10, + -2.27365122e-10, 9.6467911e-11, 2.394038e-12, + -6.886027e-12, 8.94487e-13, 3.13092e-13, + -1.12708e-13, 3.81e-16, 7.106e-15, + -1.523e-15, -9.4e-17, 1.21e-16, + -2.8e-17]; + var j = cof.length - 1; + var isneg = false; + var d = 0; + var dd = 0; + var t, ty, tmp, res; + + if (x < 0) { + x = -x; + isneg = true; + } + + t = 2 / (2 + x); + ty = 4 * t - 2; + + for(; j > 0; j--) { + tmp = d; + d = ty * d - dd + cof[j]; + dd = tmp; + } + + res = t * Math.exp(-x * x + 0.5 * (cof[0] + ty * d) - dd); + return isneg ? res - 1 : 1 - res; + }; + + + // Returns the complmentary error function erfc(x) + jStat.erfc = function erfc(x) { + return 1 - jStat.erf(x); + }; + + + // Returns the inverse of the complementary error function + jStat.erfcinv = function erfcinv(p) { + var j = 0; + var x, err, t, pp; + if (p >= 2) + return -100; + if (p <= 0) + return 100; + pp = (p < 1) ? p : 2 - p; + t = Math.sqrt(-2 * Math.log(pp / 2)); + x = -0.70711 * ((2.30753 + t * 0.27061) / + (1 + t * (0.99229 + t * 0.04481)) - t); + for (; j < 2; j++) { + err = jStat.erfc(x) - pp; + x += err / (1.12837916709551257 * Math.exp(-x * x) - x * err); + } + return (p < 1) ? x : -x; + }; + + + // Returns the inverse of the incomplete beta function + jStat.ibetainv = function ibetainv(p, a, b) { + var EPS = 1e-8; + var a1 = a - 1; + var b1 = b - 1; + var j = 0; + var lna, lnb, pp, t, u, err, x, al, h, w, afac; + if (p <= 0) + return 0; + if (p >= 1) + return 1; + if (a >= 1 && b >= 1) { + pp = (p < 0.5) ? p : 1 - p; + t = Math.sqrt(-2 * Math.log(pp)); + x = (2.30753 + t * 0.27061) / (1 + t* (0.99229 + t * 0.04481)) - t; + if (p < 0.5) + x = -x; + al = (x * x - 3) / 6; + h = 2 / (1 / (2 * a - 1) + 1 / (2 * b - 1)); + w = (x * Math.sqrt(al + h) / h) - (1 / (2 * b - 1) - 1 / (2 * a - 1)) * + (al + 5 / 6 - 2 / (3 * h)); + x = a / (a + b * Math.exp(2 * w)); + } else { + lna = Math.log(a / (a + b)); + lnb = Math.log(b / (a + b)); + t = Math.exp(a * lna) / a; + u = Math.exp(b * lnb) / b; + w = t + u; + if (p < t / w) + x = Math.pow(a * w * p, 1 / a); + else + x = 1 - Math.pow(b * w * (1 - p), 1 / b); + } + afac = -jStat.gammaln(a) - jStat.gammaln(b) + jStat.gammaln(a + b); + for(; j < 10; j++) { + if (x === 0 || x === 1) + return x; + err = jStat.ibeta(x, a, b) - p; + t = Math.exp(a1 * Math.log(x) + b1 * Math.log(1 - x) + afac); + u = err / t; + x -= (t = u / (1 - 0.5 * Math.min(1, u * (a1 / x - b1 / (1 - x))))); + if (x <= 0) + x = 0.5 * (x + t); + if (x >= 1) + x = 0.5 * (x + t + 1); + if (Math.abs(t) < EPS * x && j > 0) + break; + } + return x; + }; + + + // Returns the incomplete beta function I_x(a,b) + jStat.ibeta = function ibeta(x, a, b) { + // Factors in front of the continued fraction. + var bt = (x === 0 || x === 1) ? 0 : + Math.exp(jStat.gammaln(a + b) - jStat.gammaln(a) - + jStat.gammaln(b) + a * Math.log(x) + b * + Math.log(1 - x)); + if (x < 0 || x > 1) + return false; + if (x < (a + 1) / (a + b + 2)) + // Use continued fraction directly. + return bt * jStat.betacf(x, a, b) / a; + // else use continued fraction after making the symmetry transformation. + return 1 - bt * jStat.betacf(1 - x, b, a) / b; + }; + + + // Returns a normal deviate (mu=0, sigma=1). + // If n and m are specified it returns a object of normal deviates. + jStat.randn = function randn(n, m) { + var u, v, x, y, q; + if (!m) + m = n; + if (n) + return jStat.create(n, m, function() { return jStat.randn(); }); + do { + u = Math.random(); + v = 1.7156 * (Math.random() - 0.5); + x = u - 0.449871; + y = Math.abs(v) + 0.386595; + q = x * x + y * (0.19600 * y - 0.25472 * x); + } while (q > 0.27597 && (q > 0.27846 || v * v > -4 * Math.log(u) * u * u)); + return v / u; + }; + + + // Returns a gamma deviate by the method of Marsaglia and Tsang. + jStat.randg = function randg(shape, n, m) { + var oalph = shape; + var a1, a2, u, v, x, mat; + if (!m) + m = n; + if (!shape) + shape = 1; + if (n) { + mat = jStat.zeros(n,m); + mat.alter(function() { return jStat.randg(shape); }); + return mat; + } + if (shape < 1) + shape += 1; + a1 = shape - 1 / 3; + a2 = 1 / Math.sqrt(9 * a1); + do { + do { + x = jStat.randn(); + v = 1 + a2 * x; + } while(v <= 0); + v = v * v * v; + u = Math.random(); + } while(u > 1 - 0.331 * Math.pow(x, 4) && + Math.log(u) > 0.5 * x*x + a1 * (1 - v + Math.log(v))); + // alpha > 1 + if (shape == oalph) + return a1 * v; + // alpha < 1 + do { + u = Math.random(); + } while(u === 0); + return Math.pow(u, 1 / oalph) * a1 * v; + }; + + + // making use of static methods on the instance + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + jStat.fn[passfunc] = function() { + return jStat( + jStat.map(this, function(value) { return jStat[passfunc](value); })); + }; + })(funcs[i]); + })('gammaln gammafn factorial factorialln'.split(' ')); + + + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + jStat.fn[passfunc] = function() { + return jStat(jStat[passfunc].apply(null, arguments)); + }; + })(funcs[i]); + })('randn'.split(' ')); + + }(jStat, Math)); + (function(jStat, Math) { + + // generate all distribution instance methods + (function(list) { + for (var i = 0; i < list.length; i++) (function(func) { + // distribution instance method + jStat[func] = function(a, b, c) { + if (!(this instanceof arguments.callee)) + return new arguments.callee(a, b, c); + this._a = a; + this._b = b; + this._c = c; + return this; + }; + // distribution method to be used on a jStat instance + jStat.fn[func] = function(a, b, c) { + var newthis = jStat[func](a, b, c); + newthis.data = this; + return newthis; + }; + // sample instance method + jStat[func].prototype.sample = function(arr) { + var a = this._a; + var b = this._b; + var c = this._c; + if (arr) + return jStat.alter(arr, function() { + return jStat[func].sample(a, b, c); + }); + else + return jStat[func].sample(a, b, c); + }; + // generate the pdf, cdf and inv instance methods + (function(vals) { + for (var i = 0; i < vals.length; i++) (function(fnfunc) { + jStat[func].prototype[fnfunc] = function(x) { + var a = this._a; + var b = this._b; + var c = this._c; + if (!x && x !== 0) + x = this.data; + if (typeof x !== 'number') { + return jStat.fn.map.call(x, function(x) { + return jStat[func][fnfunc](x, a, b, c); + }); + } + return jStat[func][fnfunc](x, a, b, c); + }; + })(vals[i]); + })('pdf cdf inv'.split(' ')); + // generate the mean, median, mode and variance instance methods + (function(vals) { + for (var i = 0; i < vals.length; i++) (function(fnfunc) { + jStat[func].prototype[fnfunc] = function() { + return jStat[func][fnfunc](this._a, this._b, this._c); + }; + })(vals[i]); + })('mean median mode variance'.split(' ')); + })(list[i]); + })(( + 'beta centralF cauchy chisquare exponential gamma invgamma kumaraswamy ' + + 'laplace lognormal noncentralt normal pareto studentt weibull uniform ' + + 'binomial negbin hypgeom poisson triangular tukey arcsine' + ).split(' ')); + + + + // extend beta function with static methods + jStat.extend(jStat.beta, { + pdf: function pdf(x, alpha, beta) { + // PDF is zero outside the support + if (x > 1 || x < 0) + return 0; + // PDF is one for the uniform case + if (alpha == 1 && beta == 1) + return 1; + + if (alpha < 512 && beta < 512) { + return (Math.pow(x, alpha - 1) * Math.pow(1 - x, beta - 1)) / + jStat.betafn(alpha, beta); + } else { + return Math.exp((alpha - 1) * Math.log(x) + + (beta - 1) * Math.log(1 - x) - + jStat.betaln(alpha, beta)); + } + }, + + cdf: function cdf(x, alpha, beta) { + return (x > 1 || x < 0) ? (x > 1) * 1 : jStat.ibeta(x, alpha, beta); + }, + + inv: function inv(x, alpha, beta) { + return jStat.ibetainv(x, alpha, beta); + }, + + mean: function mean(alpha, beta) { + return alpha / (alpha + beta); + }, + + median: function median(alpha, beta) { + return jStat.ibetainv(0.5, alpha, beta); + }, + + mode: function mode(alpha, beta) { + return (alpha - 1 ) / ( alpha + beta - 2); + }, + + // return a random sample + sample: function sample(alpha, beta) { + var u = jStat.randg(alpha); + return u / (u + jStat.randg(beta)); + }, + + variance: function variance(alpha, beta) { + return (alpha * beta) / (Math.pow(alpha + beta, 2) * (alpha + beta + 1)); + } + }); + + // extend F function with static methods + jStat.extend(jStat.centralF, { + // This implementation of the pdf function avoids float overflow + // See the way that R calculates this value: + // https://svn.r-project.org/R/trunk/src/nmath/df.c + pdf: function pdf(x, df1, df2) { + var p, q, f; + + if (x < 0) + return 0; + + if (df1 <= 2) { + if (x === 0 && df1 < 2) { + return Infinity; + } + if (x === 0 && df1 === 2) { + return 1; + } + return (1 / jStat.betafn(df1 / 2, df2 / 2)) * + Math.pow(df1 / df2, df1 / 2) * + Math.pow(x, (df1/2) - 1) * + Math.pow((1 + (df1 / df2) * x), -(df1 + df2) / 2); + } + + p = (df1 * x) / (df2 + x * df1); + q = df2 / (df2 + x * df1); + f = df1 * q / 2.0; + return f * jStat.binomial.pdf((df1 - 2) / 2, (df1 + df2 - 2) / 2, p); + }, + + cdf: function cdf(x, df1, df2) { + if (x < 0) + return 0; + return jStat.ibeta((df1 * x) / (df1 * x + df2), df1 / 2, df2 / 2); + }, + + inv: function inv(x, df1, df2) { + return df2 / (df1 * (1 / jStat.ibetainv(x, df1 / 2, df2 / 2) - 1)); + }, + + mean: function mean(df1, df2) { + return (df2 > 2) ? df2 / (df2 - 2) : undefined; + }, + + mode: function mode(df1, df2) { + return (df1 > 2) ? (df2 * (df1 - 2)) / (df1 * (df2 + 2)) : undefined; + }, + + // return a random sample + sample: function sample(df1, df2) { + var x1 = jStat.randg(df1 / 2) * 2; + var x2 = jStat.randg(df2 / 2) * 2; + return (x1 / df1) / (x2 / df2); + }, + + variance: function variance(df1, df2) { + if (df2 <= 4) + return undefined; + return 2 * df2 * df2 * (df1 + df2 - 2) / + (df1 * (df2 - 2) * (df2 - 2) * (df2 - 4)); + } + }); + + + // extend cauchy function with static methods + jStat.extend(jStat.cauchy, { + pdf: function pdf(x, local, scale) { + if (scale < 0) { return 0; } + + return (scale / (Math.pow(x - local, 2) + Math.pow(scale, 2))) / Math.PI; + }, + + cdf: function cdf(x, local, scale) { + return Math.atan((x - local) / scale) / Math.PI + 0.5; + }, + + inv: function(p, local, scale) { + return local + scale * Math.tan(Math.PI * (p - 0.5)); + }, + + median: function median(local, scale) { + return local; + }, + + mode: function mode(local, scale) { + return local; + }, + + sample: function sample(local, scale) { + return jStat.randn() * + Math.sqrt(1 / (2 * jStat.randg(0.5))) * scale + local; + } + }); + + + + // extend chisquare function with static methods + jStat.extend(jStat.chisquare, { + pdf: function pdf(x, dof) { + if (x < 0) + return 0; + return (x === 0 && dof === 2) ? 0.5 : + Math.exp((dof / 2 - 1) * Math.log(x) - x / 2 - (dof / 2) * + Math.log(2) - jStat.gammaln(dof / 2)); + }, + + cdf: function cdf(x, dof) { + if (x < 0) + return 0; + return jStat.lowRegGamma(dof / 2, x / 2); + }, + + inv: function(p, dof) { + return 2 * jStat.gammapinv(p, 0.5 * dof); + }, + + mean : function(dof) { + return dof; + }, + + // TODO: this is an approximation (is there a better way?) + median: function median(dof) { + return dof * Math.pow(1 - (2 / (9 * dof)), 3); + }, + + mode: function mode(dof) { + return (dof - 2 > 0) ? dof - 2 : 0; + }, + + sample: function sample(dof) { + return jStat.randg(dof / 2) * 2; + }, + + variance: function variance(dof) { + return 2 * dof; + } + }); + + + + // extend exponential function with static methods + jStat.extend(jStat.exponential, { + pdf: function pdf(x, rate) { + return x < 0 ? 0 : rate * Math.exp(-rate * x); + }, + + cdf: function cdf(x, rate) { + return x < 0 ? 0 : 1 - Math.exp(-rate * x); + }, + + inv: function(p, rate) { + return -Math.log(1 - p) / rate; + }, + + mean : function(rate) { + return 1 / rate; + }, + + median: function (rate) { + return (1 / rate) * Math.log(2); + }, + + mode: function mode(rate) { + return 0; + }, + + sample: function sample(rate) { + return -1 / rate * Math.log(Math.random()); + }, + + variance : function(rate) { + return Math.pow(rate, -2); + } + }); + + + + // extend gamma function with static methods + jStat.extend(jStat.gamma, { + pdf: function pdf(x, shape, scale) { + if (x < 0) + return 0; + return (x === 0 && shape === 1) ? 1 / scale : + Math.exp((shape - 1) * Math.log(x) - x / scale - + jStat.gammaln(shape) - shape * Math.log(scale)); + }, + + cdf: function cdf(x, shape, scale) { + if (x < 0) + return 0; + return jStat.lowRegGamma(shape, x / scale); + }, + + inv: function(p, shape, scale) { + return jStat.gammapinv(p, shape) * scale; + }, + + mean : function(shape, scale) { + return shape * scale; + }, + + mode: function mode(shape, scale) { + if(shape > 1) return (shape - 1) * scale; + return undefined; + }, + + sample: function sample(shape, scale) { + return jStat.randg(shape) * scale; + }, + + variance: function variance(shape, scale) { + return shape * scale * scale; + } + }); + + // extend inverse gamma function with static methods + jStat.extend(jStat.invgamma, { + pdf: function pdf(x, shape, scale) { + if (x <= 0) + return 0; + return Math.exp(-(shape + 1) * Math.log(x) - scale / x - + jStat.gammaln(shape) + shape * Math.log(scale)); + }, + + cdf: function cdf(x, shape, scale) { + if (x <= 0) + return 0; + return 1 - jStat.lowRegGamma(shape, scale / x); + }, + + inv: function(p, shape, scale) { + return scale / jStat.gammapinv(1 - p, shape); + }, + + mean : function(shape, scale) { + return (shape > 1) ? scale / (shape - 1) : undefined; + }, + + mode: function mode(shape, scale) { + return scale / (shape + 1); + }, + + sample: function sample(shape, scale) { + return scale / jStat.randg(shape); + }, + + variance: function variance(shape, scale) { + if (shape <= 2) + return undefined; + return scale * scale / ((shape - 1) * (shape - 1) * (shape - 2)); + } + }); + + + // extend kumaraswamy function with static methods + jStat.extend(jStat.kumaraswamy, { + pdf: function pdf(x, alpha, beta) { + if (x === 0 && alpha === 1) + return beta; + else if (x === 1 && beta === 1) + return alpha; + return Math.exp(Math.log(alpha) + Math.log(beta) + (alpha - 1) * + Math.log(x) + (beta - 1) * + Math.log(1 - Math.pow(x, alpha))); + }, + + cdf: function cdf(x, alpha, beta) { + if (x < 0) + return 0; + else if (x > 1) + return 1; + return (1 - Math.pow(1 - Math.pow(x, alpha), beta)); + }, + + inv: function inv(p, alpha, beta) { + return Math.pow(1 - Math.pow(1 - p, 1 / beta), 1 / alpha); + }, + + mean : function(alpha, beta) { + return (beta * jStat.gammafn(1 + 1 / alpha) * + jStat.gammafn(beta)) / (jStat.gammafn(1 + 1 / alpha + beta)); + }, + + median: function median(alpha, beta) { + return Math.pow(1 - Math.pow(2, -1 / beta), 1 / alpha); + }, + + mode: function mode(alpha, beta) { + if (!(alpha >= 1 && beta >= 1 && (alpha !== 1 && beta !== 1))) + return undefined; + return Math.pow((alpha - 1) / (alpha * beta - 1), 1 / alpha); + }, + + variance: function variance(alpha, beta) { + throw new Error('variance not yet implemented'); + // TODO: complete this + } + }); + + + + // extend lognormal function with static methods + jStat.extend(jStat.lognormal, { + pdf: function pdf(x, mu, sigma) { + if (x <= 0) + return 0; + return Math.exp(-Math.log(x) - 0.5 * Math.log(2 * Math.PI) - + Math.log(sigma) - Math.pow(Math.log(x) - mu, 2) / + (2 * sigma * sigma)); + }, + + cdf: function cdf(x, mu, sigma) { + if (x < 0) + return 0; + return 0.5 + + (0.5 * jStat.erf((Math.log(x) - mu) / Math.sqrt(2 * sigma * sigma))); + }, + + inv: function(p, mu, sigma) { + return Math.exp(-1.41421356237309505 * sigma * jStat.erfcinv(2 * p) + mu); + }, + + mean: function mean(mu, sigma) { + return Math.exp(mu + sigma * sigma / 2); + }, + + median: function median(mu, sigma) { + return Math.exp(mu); + }, + + mode: function mode(mu, sigma) { + return Math.exp(mu - sigma * sigma); + }, + + sample: function sample(mu, sigma) { + return Math.exp(jStat.randn() * sigma + mu); + }, + + variance: function variance(mu, sigma) { + return (Math.exp(sigma * sigma) - 1) * Math.exp(2 * mu + sigma * sigma); + } + }); + + + + // extend noncentralt function with static methods + jStat.extend(jStat.noncentralt, { + pdf: function pdf(x, dof, ncp) { + var tol = 1e-14; + if (Math.abs(ncp) < tol) // ncp approx 0; use student-t + return jStat.studentt.pdf(x, dof) + + if (Math.abs(x) < tol) { // different formula for x == 0 + return Math.exp(jStat.gammaln((dof + 1) / 2) - ncp * ncp / 2 - + 0.5 * Math.log(Math.PI * dof) - jStat.gammaln(dof / 2)); + } + + // formula for x != 0 + return dof / x * + (jStat.noncentralt.cdf(x * Math.sqrt(1 + 2 / dof), dof+2, ncp) - + jStat.noncentralt.cdf(x, dof, ncp)); + }, + + cdf: function cdf(x, dof, ncp) { + var tol = 1e-14; + var min_iterations = 200; + + if (Math.abs(ncp) < tol) // ncp approx 0; use student-t + return jStat.studentt.cdf(x, dof); + + // turn negative x into positive and flip result afterwards + var flip = false; + if (x < 0) { + flip = true; + ncp = -ncp; + } + + var prob = jStat.normal.cdf(-ncp, 0, 1); + var value = tol + 1; + // use value at last two steps to determine convergence + var lastvalue = value; + var y = x * x / (x * x + dof); + var j = 0; + var p = Math.exp(-ncp * ncp / 2); + var q = Math.exp(-ncp * ncp / 2 - 0.5 * Math.log(2) - + jStat.gammaln(3 / 2)) * ncp; + while (j < min_iterations || lastvalue > tol || value > tol) { + lastvalue = value; + if (j > 0) { + p *= (ncp * ncp) / (2 * j); + q *= (ncp * ncp) / (2 * (j + 1 / 2)); + } + value = p * jStat.beta.cdf(y, j + 0.5, dof / 2) + + q * jStat.beta.cdf(y, j+1, dof/2); + prob += 0.5 * value; + j++; + } + + return flip ? (1 - prob) : prob; + } + }); + + + // extend normal function with static methods + jStat.extend(jStat.normal, { + pdf: function pdf(x, mean, std) { + return Math.exp(-0.5 * Math.log(2 * Math.PI) - + Math.log(std) - Math.pow(x - mean, 2) / (2 * std * std)); + }, + + cdf: function cdf(x, mean, std) { + return 0.5 * (1 + jStat.erf((x - mean) / Math.sqrt(2 * std * std))); + }, + + inv: function(p, mean, std) { + return -1.41421356237309505 * std * jStat.erfcinv(2 * p) + mean; + }, + + mean : function(mean, std) { + return mean; + }, + + median: function median(mean, std) { + return mean; + }, + + mode: function (mean, std) { + return mean; + }, + + sample: function sample(mean, std) { + return jStat.randn() * std + mean; + }, + + variance : function(mean, std) { + return std * std; + } + }); + + + + // extend pareto function with static methods + jStat.extend(jStat.pareto, { + pdf: function pdf(x, scale, shape) { + if (x < scale) + return 0; + return (shape * Math.pow(scale, shape)) / Math.pow(x, shape + 1); + }, + + cdf: function cdf(x, scale, shape) { + if (x < scale) + return 0; + return 1 - Math.pow(scale / x, shape); + }, + + inv: function inv(p, scale, shape) { + return scale / Math.pow(1 - p, 1 / shape); + }, + + mean: function mean(scale, shape) { + if (shape <= 1) + return undefined; + return (shape * Math.pow(scale, shape)) / (shape - 1); + }, + + median: function median(scale, shape) { + return scale * (shape * Math.SQRT2); + }, + + mode: function mode(scale, shape) { + return scale; + }, + + variance : function(scale, shape) { + if (shape <= 2) + return undefined; + return (scale*scale * shape) / (Math.pow(shape - 1, 2) * (shape - 2)); + } + }); + + + + // extend studentt function with static methods + jStat.extend(jStat.studentt, { + pdf: function pdf(x, dof) { + dof = dof > 1e100 ? 1e100 : dof; + return (1/(Math.sqrt(dof) * jStat.betafn(0.5, dof/2))) * + Math.pow(1 + ((x * x) / dof), -((dof + 1) / 2)); + }, + + cdf: function cdf(x, dof) { + var dof2 = dof / 2; + return jStat.ibeta((x + Math.sqrt(x * x + dof)) / + (2 * Math.sqrt(x * x + dof)), dof2, dof2); + }, + + inv: function(p, dof) { + var x = jStat.ibetainv(2 * Math.min(p, 1 - p), 0.5 * dof, 0.5); + x = Math.sqrt(dof * (1 - x) / x); + return (p > 0.5) ? x : -x; + }, + + mean: function mean(dof) { + return (dof > 1) ? 0 : undefined; + }, + + median: function median(dof) { + return 0; + }, + + mode: function mode(dof) { + return 0; + }, + + sample: function sample(dof) { + return jStat.randn() * Math.sqrt(dof / (2 * jStat.randg(dof / 2))); + }, + + variance: function variance(dof) { + return (dof > 2) ? dof / (dof - 2) : (dof > 1) ? Infinity : undefined; + } + }); + + + + // extend weibull function with static methods + jStat.extend(jStat.weibull, { + pdf: function pdf(x, scale, shape) { + if (x < 0 || scale < 0 || shape < 0) + return 0; + return (shape / scale) * Math.pow((x / scale), (shape - 1)) * + Math.exp(-(Math.pow((x / scale), shape))); + }, + + cdf: function cdf(x, scale, shape) { + return x < 0 ? 0 : 1 - Math.exp(-Math.pow((x / scale), shape)); + }, + + inv: function(p, scale, shape) { + return scale * Math.pow(-Math.log(1 - p), 1 / shape); + }, + + mean : function(scale, shape) { + return scale * jStat.gammafn(1 + 1 / shape); + }, + + median: function median(scale, shape) { + return scale * Math.pow(Math.log(2), 1 / shape); + }, + + mode: function mode(scale, shape) { + if (shape <= 1) + return 0; + return scale * Math.pow((shape - 1) / shape, 1 / shape); + }, + + sample: function sample(scale, shape) { + return scale * Math.pow(-Math.log(Math.random()), 1 / shape); + }, + + variance: function variance(scale, shape) { + return scale * scale * jStat.gammafn(1 + 2 / shape) - + Math.pow(jStat.weibull.mean(scale, shape), 2); + } + }); + + + + // extend uniform function with static methods + jStat.extend(jStat.uniform, { + pdf: function pdf(x, a, b) { + return (x < a || x > b) ? 0 : 1 / (b - a); + }, + + cdf: function cdf(x, a, b) { + if (x < a) + return 0; + else if (x < b) + return (x - a) / (b - a); + return 1; + }, + + inv: function(p, a, b) { + return a + (p * (b - a)); + }, + + mean: function mean(a, b) { + return 0.5 * (a + b); + }, + + median: function median(a, b) { + return jStat.mean(a, b); + }, + + mode: function mode(a, b) { + throw new Error('mode is not yet implemented'); + }, + + sample: function sample(a, b) { + return (a / 2 + b / 2) + (b / 2 - a / 2) * (2 * Math.random() - 1); + }, + + variance: function variance(a, b) { + return Math.pow(b - a, 2) / 12; + } + }); + + + + // extend uniform function with static methods + jStat.extend(jStat.binomial, { + pdf: function pdf(k, n, p) { + return (p === 0 || p === 1) ? + ((n * p) === k ? 1 : 0) : + jStat.combination(n, k) * Math.pow(p, k) * Math.pow(1 - p, n - k); + }, + + cdf: function cdf(x, n, p) { + var binomarr = [], + k = 0; + if (x < 0) { + return 0; + } + if (x < n) { + for (; k <= x; k++) { + binomarr[ k ] = jStat.binomial.pdf(k, n, p); + } + return jStat.sum(binomarr); + } + return 1; + } + }); + + + + // extend uniform function with static methods + jStat.extend(jStat.negbin, { + pdf: function pdf(k, r, p) { + if (k !== k >>> 0) + return false; + if (k < 0) + return 0; + return jStat.combination(k + r - 1, r - 1) * + Math.pow(1 - p, k) * Math.pow(p, r); + }, + + cdf: function cdf(x, r, p) { + var sum = 0, + k = 0; + if (x < 0) return 0; + for (; k <= x; k++) { + sum += jStat.negbin.pdf(k, r, p); + } + return sum; + } + }); + + + + // extend uniform function with static methods + jStat.extend(jStat.hypgeom, { + pdf: function pdf(k, N, m, n) { + // Hypergeometric PDF. + + // A simplification of the CDF algorithm below. + + // k = number of successes drawn + // N = population size + // m = number of successes in population + // n = number of items drawn from population + + if(k !== k | 0) { + return false; + } else if(k < 0 || k < m - (N - n)) { + // It's impossible to have this few successes drawn. + return 0; + } else if(k > n || k > m) { + // It's impossible to have this many successes drawn. + return 0; + } else if (m * 2 > N) { + // More than half the population is successes. + + if(n * 2 > N) { + // More than half the population is sampled. + + return jStat.hypgeom.pdf(N - m - n + k, N, N - m, N - n) + } else { + // Half or less of the population is sampled. + + return jStat.hypgeom.pdf(n - k, N, N - m, n); + } + + } else if(n * 2 > N) { + // Half or less is successes. + + return jStat.hypgeom.pdf(m - k, N, m, N - n); + + } else if(m < n) { + // We want to have the number of things sampled to be less than the + // successes available. So swap the definitions of successful and sampled. + return jStat.hypgeom.pdf(k, N, n, m); + } else { + // If we get here, half or less of the population was sampled, half or + // less of it was successes, and we had fewer sampled things than + // successes. Now we can do this complicated iterative algorithm in an + // efficient way. + + // The basic premise of the algorithm is that we partially normalize our + // intermediate product to keep it in a numerically good region, and then + // finish the normalization at the end. + + // This variable holds the scaled probability of the current number of + // successes. + var scaledPDF = 1; + + // This keeps track of how much we have normalized. + var samplesDone = 0; + + for(var i = 0; i < k; i++) { + // For every possible number of successes up to that observed... + + while(scaledPDF > 1 && samplesDone < n) { + // Intermediate result is growing too big. Apply some of the + // normalization to shrink everything. + + scaledPDF *= 1 - (m / (N - samplesDone)); + + // Say we've normalized by this sample already. + samplesDone++; + } + + // Work out the partially-normalized hypergeometric PDF for the next + // number of successes + scaledPDF *= (n - i) * (m - i) / ((i + 1) * (N - m - n + i + 1)); + } + + for(; samplesDone < n; samplesDone++) { + // Apply all the rest of the normalization + scaledPDF *= 1 - (m / (N - samplesDone)); + } + + // Bound answer sanely before returning. + return Math.min(1, Math.max(0, scaledPDF)); + } + }, + + cdf: function cdf(x, N, m, n) { + // Hypergeometric CDF. + + // This algorithm is due to Prof. Thomas S. Ferguson, , + // and comes from his hypergeometric test calculator at + // . + + // x = number of successes drawn + // N = population size + // m = number of successes in population + // n = number of items drawn from population + + if(x < 0 || x < m - (N - n)) { + // It's impossible to have this few successes drawn or fewer. + return 0; + } else if(x >= n || x >= m) { + // We will always have this many successes or fewer. + return 1; + } else if (m * 2 > N) { + // More than half the population is successes. + + if(n * 2 > N) { + // More than half the population is sampled. + + return jStat.hypgeom.cdf(N - m - n + x, N, N - m, N - n) + } else { + // Half or less of the population is sampled. + + return 1 - jStat.hypgeom.cdf(n - x - 1, N, N - m, n); + } + + } else if(n * 2 > N) { + // Half or less is successes. + + return 1 - jStat.hypgeom.cdf(m - x - 1, N, m, N - n); + + } else if(m < n) { + // We want to have the number of things sampled to be less than the + // successes available. So swap the definitions of successful and sampled. + return jStat.hypgeom.cdf(x, N, n, m); + } else { + // If we get here, half or less of the population was sampled, half or + // less of it was successes, and we had fewer sampled things than + // successes. Now we can do this complicated iterative algorithm in an + // efficient way. + + // The basic premise of the algorithm is that we partially normalize our + // intermediate sum to keep it in a numerically good region, and then + // finish the normalization at the end. + + // Holds the intermediate, scaled total CDF. + var scaledCDF = 1; + + // This variable holds the scaled probability of the current number of + // successes. + var scaledPDF = 1; + + // This keeps track of how much we have normalized. + var samplesDone = 0; + + for(var i = 0; i < x; i++) { + // For every possible number of successes up to that observed... + + while(scaledCDF > 1 && samplesDone < n) { + // Intermediate result is growing too big. Apply some of the + // normalization to shrink everything. + + var factor = 1 - (m / (N - samplesDone)); + + scaledPDF *= factor; + scaledCDF *= factor; + + // Say we've normalized by this sample already. + samplesDone++; + } + + // Work out the partially-normalized hypergeometric PDF for the next + // number of successes + scaledPDF *= (n - i) * (m - i) / ((i + 1) * (N - m - n + i + 1)); + + // Add to the CDF answer. + scaledCDF += scaledPDF; + } + + for(; samplesDone < n; samplesDone++) { + // Apply all the rest of the normalization + scaledCDF *= 1 - (m / (N - samplesDone)); + } + + // Bound answer sanely before returning. + return Math.min(1, Math.max(0, scaledCDF)); + } + } + }); + + + + // extend uniform function with static methods + jStat.extend(jStat.poisson, { + pdf: function pdf(k, l) { + if (l < 0 || (k % 1) !== 0 || k < 0) { + return 0; + } + + return Math.pow(l, k) * Math.exp(-l) / jStat.factorial(k); + }, + + cdf: function cdf(x, l) { + var sumarr = [], + k = 0; + if (x < 0) return 0; + for (; k <= x; k++) { + sumarr.push(jStat.poisson.pdf(k, l)); + } + return jStat.sum(sumarr); + }, + + mean : function(l) { + return l; + }, + + variance : function(l) { + return l; + }, + + sample: function sample(l) { + var p = 1, k = 0, L = Math.exp(-l); + do { + k++; + p *= Math.random(); + } while (p > L); + return k - 1; + } + }); + + // extend triangular function with static methods + jStat.extend(jStat.triangular, { + pdf: function pdf(x, a, b, c) { + if (b <= a || c < a || c > b) { + return NaN; + } else { + if (x < a || x > b) { + return 0; + } else if (x < c) { + return (2 * (x - a)) / ((b - a) * (c - a)); + } else if (x === c) { + return (2 / (b - a)); + } else { // x > c + return (2 * (b - x)) / ((b - a) * (b - c)); + } + } + }, + + cdf: function cdf(x, a, b, c) { + if (b <= a || c < a || c > b) + return NaN; + if (x <= a) + return 0; + else if (x >= b) + return 1; + if (x <= c) + return Math.pow(x - a, 2) / ((b - a) * (c - a)); + else // x > c + return 1 - Math.pow(b - x, 2) / ((b - a) * (b - c)); + }, + + inv: function inv(p, a, b, c) { + if (b <= a || c < a || c > b) { + return NaN; + } else { + if (p <= ((c - a) / (b - a))) { + return a + (b - a) * Math.sqrt(p * ((c - a) / (b - a))); + } else { // p > ((c - a) / (b - a)) + return a + (b - a) * (1 - Math.sqrt((1 - p) * (1 - ((c - a) / (b - a))))); + } + } + }, + + mean: function mean(a, b, c) { + return (a + b + c) / 3; + }, + + median: function median(a, b, c) { + if (c <= (a + b) / 2) { + return b - Math.sqrt((b - a) * (b - c)) / Math.sqrt(2); + } else if (c > (a + b) / 2) { + return a + Math.sqrt((b - a) * (c - a)) / Math.sqrt(2); + } + }, + + mode: function mode(a, b, c) { + return c; + }, + + sample: function sample(a, b, c) { + var u = Math.random(); + if (u < ((c - a) / (b - a))) + return a + Math.sqrt(u * (b - a) * (c - a)) + return b - Math.sqrt((1 - u) * (b - a) * (b - c)); + }, + + variance: function variance(a, b, c) { + return (a * a + b * b + c * c - a * b - a * c - b * c) / 18; + } + }); + + + // extend arcsine function with static methods + jStat.extend(jStat.arcsine, { + pdf: function pdf(x, a, b) { + if (b <= a) return NaN; + + return (x <= a || x >= b) ? 0 : + (2 / Math.PI) * + Math.pow(Math.pow(b - a, 2) - + Math.pow(2 * x - a - b, 2), -0.5); + }, + + cdf: function cdf(x, a, b) { + if (x < a) + return 0; + else if (x < b) + return (2 / Math.PI) * Math.asin(Math.sqrt((x - a)/(b - a))); + return 1; + }, + + inv: function(p, a, b) { + return a + (0.5 - 0.5 * Math.cos(Math.PI * p)) * (b - a); + }, + + mean: function mean(a, b) { + if (b <= a) return NaN; + return (a + b) / 2; + }, + + median: function median(a, b) { + if (b <= a) return NaN; + return (a + b) / 2; + }, + + mode: function mode(a, b) { + throw new Error('mode is not yet implemented'); + }, + + sample: function sample(a, b) { + return ((a + b) / 2) + ((b - a) / 2) * + Math.sin(2 * Math.PI * jStat.uniform.sample(0, 1)); + }, + + variance: function variance(a, b) { + if (b <= a) return NaN; + return Math.pow(b - a, 2) / 8; + } + }); + + + function laplaceSign(x) { return x / Math.abs(x); } + + jStat.extend(jStat.laplace, { + pdf: function pdf(x, mu, b) { + return (b <= 0) ? 0 : (Math.exp(-Math.abs(x - mu) / b)) / (2 * b); + }, + + cdf: function cdf(x, mu, b) { + if (b <= 0) { return 0; } + + if(x < mu) { + return 0.5 * Math.exp((x - mu) / b); + } else { + return 1 - 0.5 * Math.exp(- (x - mu) / b); + } + }, - inv: function(p, nmeans, df) { - // Identical implementation as the R qtukey() function as of commit 68947 - var rr = 1; - var cc = nmeans; + mean: function(mu, b) { + return mu; + }, - var eps = 0.0001; - var maxiter = 50; + median: function(mu, b) { + return mu; + }, - // df must be > 1 ; there must be at least two values - if (df < 2 || rr < 1 || cc < 2) return NaN; + mode: function(mu, b) { + return mu; + }, - if (p < 0 || p > 1) return NaN; - if (p === 0) return 0; - if (p === 1) return Infinity; + variance: function(mu, b) { + return 2 * b * b; + }, - // Initial value + sample: function sample(mu, b) { + var u = Math.random() - 0.5; - var x0 = tukeyQinv(p, cc, df); + return mu - (b * laplaceSign(u) * Math.log(1 - (2 * Math.abs(u)))); + } + }); + + function tukeyWprob(w, rr, cc) { + var nleg = 12; + var ihalf = 6; + + var C1 = -30; + var C2 = -50; + var C3 = 60; + var bb = 8; + var wlar = 3; + var wincr1 = 2; + var wincr2 = 3; + var xleg = [ + 0.981560634246719250690549090149, + 0.904117256370474856678465866119, + 0.769902674194304687036893833213, + 0.587317954286617447296702418941, + 0.367831498998180193752691536644, + 0.125233408511468915472441369464 + ]; + var aleg = [ + 0.047175336386511827194615961485, + 0.106939325995318430960254718194, + 0.160078328543346226334652529543, + 0.203167426723065921749064455810, + 0.233492536538354808760849898925, + 0.249147045813402785000562436043 + ]; + + var qsqz = w * 0.5; + + // if w >= 16 then the integral lower bound (occurs for c=20) + // is 0.99999999999995 so return a value of 1. + + if (qsqz >= bb) + return 1.0; + + // find (f(w/2) - 1) ^ cc + // (first term in integral of hartley's form). + + var pr_w = 2 * jStat.normal.cdf(qsqz, 0, 1, 1, 0) - 1; // erf(qsqz / M_SQRT2) + // if pr_w ^ cc < 2e-22 then set pr_w = 0 + if (pr_w >= Math.exp(C2 / cc)) + pr_w = Math.pow(pr_w, cc); + else + pr_w = 0.0; + + // if w is large then the second component of the + // integral is small, so fewer intervals are needed. + + var wincr; + if (w > wlar) + wincr = wincr1; + else + wincr = wincr2; + + // find the integral of second term of hartley's form + // for the integral of the range for equal-length + // intervals using legendre quadrature. limits of + // integration are from (w/2, 8). two or three + // equal-length intervals are used. - // Find prob(value < x0) + // blb and bub are lower and upper limits of integration. - var valx0 = jStat.tukey.cdf(x0, nmeans, df) - p; + var blb = qsqz; + var binc = (bb - qsqz) / wincr; + var bub = blb + binc; + var einsum = 0.0; - // Find the second iterate and prob(value < x1). - // If the first iterate has probability value - // exceeding p then second iterate is 1 less than - // first iterate; otherwise it is 1 greater. + // integrate over each interval - var x1; - if (valx0 > 0.0) - x1 = Math.max(0.0, x0 - 1.0); - else - x1 = x0 + 1.0; - var valx1 = jStat.tukey.cdf(x1, nmeans, df) - p; + var cc1 = cc - 1.0; + for (var wi = 1; wi <= wincr; wi++) { + var elsum = 0.0; + var a = 0.5 * (bub + blb); - // Find new iterate + // legendre quadrature with order = nleg - var ans; - for(var iter = 1; iter < maxiter; iter++) { - ans = x1 - ((valx1 * (x1 - x0)) / (valx1 - valx0)); - valx0 = valx1; + var b = 0.5 * (bub - blb); - // New iterate must be >= 0 + for (var jj = 1; jj <= nleg; jj++) { + var j, xx; + if (ihalf < jj) { + j = (nleg - jj) + 1; + xx = xleg[j-1]; + } else { + j = jj; + xx = -xleg[j-1]; + } + var c = b * xx; + var ac = a + c; + + // if exp(-qexpo/2) < 9e-14, + // then doesn't contribute to integral + + var qexpo = ac * ac; + if (qexpo > C3) + break; + + var pplus = 2 * jStat.normal.cdf(ac, 0, 1, 1, 0); + var pminus= 2 * jStat.normal.cdf(ac, w, 1, 1, 0); - x0 = x1; - if (ans < 0.0) { - ans = 0.0; - valx1 = -p; + // if rinsum ^ (cc-1) < 9e-14, + // then doesn't contribute to integral + + var rinsum = (pplus * 0.5) - (pminus * 0.5); + if (rinsum >= Math.exp(C1 / cc1)) { + rinsum = (aleg[j-1] * Math.exp(-(0.5 * qexpo))) * Math.pow(rinsum, cc1); + elsum += rinsum; + } + } + elsum *= (((2.0 * b) * cc) / Math.sqrt(2 * Math.PI)); + einsum += elsum; + blb = bub; + bub += binc; } - // Find prob(value < new iterate) - valx1 = jStat.tukey.cdf(ans, nmeans, df) - p; - x1 = ans; + // if pr_w ^ rr < 9e-14, then return 0 + pr_w += einsum; + if (pr_w <= Math.exp(C1 / rr)) + return 0; + + pr_w = Math.pow(pr_w, rr); + if (pr_w >= 1) // 1 was iMax was eps + return 1; + return pr_w; + } + + function tukeyQinv(p, c, v) { + var p0 = 0.322232421088; + var q0 = 0.993484626060e-01; + var p1 = -1.0; + var q1 = 0.588581570495; + var p2 = -0.342242088547; + var q2 = 0.531103462366; + var p3 = -0.204231210125; + var q3 = 0.103537752850; + var p4 = -0.453642210148e-04; + var q4 = 0.38560700634e-02; + var c1 = 0.8832; + var c2 = 0.2368; + var c3 = 1.214; + var c4 = 1.208; + var c5 = 1.4142; + var vmax = 120.0; + + var ps = 0.5 - 0.5 * p; + var yi = Math.sqrt(Math.log(1.0 / (ps * ps))); + var t = yi + (((( yi * p4 + p3) * yi + p2) * yi + p1) * yi + p0) + / (((( yi * q4 + q3) * yi + q2) * yi + q1) * yi + q0); + if (v < vmax) t += (t * t * t + t) / v / 4.0; + var q = c1 - c2 * t; + if (v < vmax) q += -c3 / v + c4 * t / v; + return t * (q * Math.log(c - 1.0) + c5); + } + + jStat.extend(jStat.tukey, { + cdf: function cdf(q, nmeans, df) { + // Identical implementation as the R ptukey() function as of commit 68947 + var rr = 1; + var cc = nmeans; + + var nlegq = 16; + var ihalfq = 8; + + var eps1 = -30.0; + var eps2 = 1.0e-14; + var dhaf = 100.0; + var dquar = 800.0; + var deigh = 5000.0; + var dlarg = 25000.0; + var ulen1 = 1.0; + var ulen2 = 0.5; + var ulen3 = 0.25; + var ulen4 = 0.125; + var xlegq = [ + 0.989400934991649932596154173450, + 0.944575023073232576077988415535, + 0.865631202387831743880467897712, + 0.755404408355003033895101194847, + 0.617876244402643748446671764049, + 0.458016777657227386342419442984, + 0.281603550779258913230460501460, + 0.950125098376374401853193354250e-1 + ]; + var alegq = [ + 0.271524594117540948517805724560e-1, + 0.622535239386478928628438369944e-1, + 0.951585116824927848099251076022e-1, + 0.124628971255533872052476282192, + 0.149595988816576732081501730547, + 0.169156519395002538189312079030, + 0.182603415044923588866763667969, + 0.189450610455068496285396723208 + ]; + + if (q <= 0) + return 0; + + // df must be > 1 + // there must be at least two values + + if (df < 2 || rr < 1 || cc < 2) return NaN; + + if (!Number.isFinite(q)) + return 1; + + if (df > dlarg) + return tukeyWprob(q, rr, cc); + + // calculate leading constant + + var f2 = df * 0.5; + var f2lf = ((f2 * Math.log(df)) - (df * Math.log(2))) - jStat.gammaln(f2); + var f21 = f2 - 1.0; + + // integral is divided into unit, half-unit, quarter-unit, or + // eighth-unit length intervals depending on the value of the + // degrees of freedom. + + var ff4 = df * 0.25; + var ulen; + if (df <= dhaf) ulen = ulen1; + else if (df <= dquar) ulen = ulen2; + else if (df <= deigh) ulen = ulen3; + else ulen = ulen4; + + f2lf += Math.log(ulen); + + // integrate over each subinterval + + var ans = 0.0; + + for (var i = 1; i <= 50; i++) { + var otsum = 0.0; + + // legendre quadrature with order = nlegq + // nodes (stored in xlegq) are symmetric around zero. + + var twa1 = (2 * i - 1) * ulen; + + for (var jj = 1; jj <= nlegq; jj++) { + var j, t1; + if (ihalfq < jj) { + j = jj - ihalfq - 1; + t1 = (f2lf + (f21 * Math.log(twa1 + (xlegq[j] * ulen)))) + - (((xlegq[j] * ulen) + twa1) * ff4); + } else { + j = jj - 1; + t1 = (f2lf + (f21 * Math.log(twa1 - (xlegq[j] * ulen)))) + + (((xlegq[j] * ulen) - twa1) * ff4); + } + + // if exp(t1) < 9e-14, then doesn't contribute to integral + var qsqz; + if (t1 >= eps1) { + if (ihalfq < jj) { + qsqz = q * Math.sqrt(((xlegq[j] * ulen) + twa1) * 0.5); + } else { + qsqz = q * Math.sqrt(((-(xlegq[j] * ulen)) + twa1) * 0.5); + } + + // call wprob to find integral of range portion - // If the difference between two successive - // iterates is less than eps, stop + var wprb = tukeyWprob(qsqz, rr, cc); + var rotsum = (wprb * alegq[j]) * Math.exp(t1); + otsum += rotsum; + } + // end legendre integral for interval i + // L200: + } + + // if integral for interval i < 1e-14, then stop. + // However, in order to avoid small area under left tail, + // at least 1 / ulen intervals are calculated. + if (i * ulen >= 1.0 && otsum <= eps2) + break; - var xabs = Math.abs(x1 - x0); - if (xabs < eps) + // end of interval i + // L330: + + ans += otsum; + } + + if (otsum > eps2) { // not converged + throw new Error('tukey.cdf failed to converge'); + } + if (ans > 1) + ans = 1; return ans; - } + }, - throw new Error('tukey.inv failed to converge'); - } -}); + inv: function(p, nmeans, df) { + // Identical implementation as the R qtukey() function as of commit 68947 + var rr = 1; + var cc = nmeans; -}(jStat, Math)); -/* Provides functions for the solution of linear system of equations, integration, extrapolation, - * interpolation, eigenvalue problems, differential equations and PCA analysis. */ + var eps = 0.0001; + var maxiter = 50; -(function(jStat, Math) { + // df must be > 1 ; there must be at least two values + if (df < 2 || rr < 1 || cc < 2) return NaN; -var push = Array.prototype.push; -var isArray = jStat.utils.isArray; + if (p < 0 || p > 1) return NaN; + if (p === 0) return 0; + if (p === 1) return Infinity; -function isUsable(arg) { - return isArray(arg) || arg instanceof jStat; -} + // Initial value -jStat.extend({ + var x0 = tukeyQinv(p, cc, df); - // add a vector/matrix to a vector/matrix or scalar - add: function add(arr, arg) { - // check if arg is a vector or scalar - if (isUsable(arg)) { - if (!isUsable(arg[0])) arg = [ arg ]; - return jStat.map(arr, function(value, row, col) { - return value + arg[row][col]; - }); - } - return jStat.map(arr, function(value) { return value + arg; }); - }, - - // subtract a vector or scalar from the vector - subtract: function subtract(arr, arg) { - // check if arg is a vector or scalar - if (isUsable(arg)) { - if (!isUsable(arg[0])) arg = [ arg ]; - return jStat.map(arr, function(value, row, col) { - return value - arg[row][col] || 0; - }); - } - return jStat.map(arr, function(value) { return value - arg; }); - }, - - // matrix division - divide: function divide(arr, arg) { - if (isUsable(arg)) { - if (!isUsable(arg[0])) arg = [ arg ]; - return jStat.multiply(arr, jStat.inv(arg)); - } - return jStat.map(arr, function(value) { return value / arg; }); - }, - - // matrix multiplication - multiply: function multiply(arr, arg) { - var row, col, nrescols, sum, nrow, ncol, res, rescols; - // eg: arr = 2 arg = 3 -> 6 for res[0][0] statement closure - if (arr.length === undefined && arg.length === undefined) { - return arr * arg; + // Find prob(value < x0) + + var valx0 = jStat.tukey.cdf(x0, nmeans, df) - p; + + // Find the second iterate and prob(value < x1). + // If the first iterate has probability value + // exceeding p then second iterate is 1 less than + // first iterate; otherwise it is 1 greater. + + var x1; + if (valx0 > 0.0) + x1 = Math.max(0.0, x0 - 1.0); + else + x1 = x0 + 1.0; + var valx1 = jStat.tukey.cdf(x1, nmeans, df) - p; + + // Find new iterate + + var ans; + for(var iter = 1; iter < maxiter; iter++) { + ans = x1 - ((valx1 * (x1 - x0)) / (valx1 - valx0)); + valx0 = valx1; + + // New iterate must be >= 0 + + x0 = x1; + if (ans < 0.0) { + ans = 0.0; + valx1 = -p; + } + // Find prob(value < new iterate) + + valx1 = jStat.tukey.cdf(ans, nmeans, df) - p; + x1 = ans; + + // If the difference between two successive + // iterates is less than eps, stop + + var xabs = Math.abs(x1 - x0); + if (xabs < eps) + return ans; + } + + throw new Error('tukey.inv failed to converge'); + } + }); + + }(jStat, Math)); + /* Provides functions for the solution of linear system of equations, integration, extrapolation, + * interpolation, eigenvalue problems, differential equations and PCA analysis. */ + + (function(jStat, Math) { + + var push = Array.prototype.push; + var isArray = jStat.utils.isArray; + + function isUsable(arg) { + return isArray(arg) || arg instanceof jStat; } - nrow = arr.length, ncol = arr[0].length, res = jStat.zeros(nrow, nrescols = (isUsable(arg)) ? arg[0].length : ncol), rescols = 0; - if (isUsable(arg)) { - for (; rescols < nrescols; rescols++) { - for (row = 0; row < nrow; row++) { + + jStat.extend({ + + // add a vector/matrix to a vector/matrix or scalar + add: function add(arr, arg) { + // check if arg is a vector or scalar + if (isUsable(arg)) { + if (!isUsable(arg[0])) arg = [ arg ]; + return jStat.map(arr, function(value, row, col) { + return value + arg[row][col]; + }); + } + return jStat.map(arr, function(value) { return value + arg; }); + }, + + // subtract a vector or scalar from the vector + subtract: function subtract(arr, arg) { + // check if arg is a vector or scalar + if (isUsable(arg)) { + if (!isUsable(arg[0])) arg = [ arg ]; + return jStat.map(arr, function(value, row, col) { + return value - arg[row][col] || 0; + }); + } + return jStat.map(arr, function(value) { return value - arg; }); + }, + + // matrix division + divide: function divide(arr, arg) { + if (isUsable(arg)) { + if (!isUsable(arg[0])) arg = [ arg ]; + return jStat.multiply(arr, jStat.inv(arg)); + } + return jStat.map(arr, function(value) { return value / arg; }); + }, + + // matrix multiplication + multiply: function multiply(arr, arg) { + var row, col, nrescols, sum, nrow, ncol, res, rescols; + // eg: arr = 2 arg = 3 -> 6 for res[0][0] statement closure + if (arr.length === undefined && arg.length === undefined) { + return arr * arg; + } + nrow = arr.length, + ncol = arr[0].length, + res = jStat.zeros(nrow, nrescols = (isUsable(arg)) ? arg[0].length : ncol), + rescols = 0; + if (isUsable(arg)) { + for (; rescols < nrescols; rescols++) { + for (row = 0; row < nrow; row++) { + sum = 0; + for (col = 0; col < ncol; col++) + sum += arr[row][col] * arg[col][rescols]; + res[row][rescols] = sum; + } + } + return (nrow === 1 && rescols === 1) ? res[0][0] : res; + } + return jStat.map(arr, function(value) { return value * arg; }); + }, + + // outer([1,2,3],[4,5,6]) + // === + // [[1],[2],[3]] times [[4,5,6]] + // -> + // [[4,5,6],[8,10,12],[12,15,18]] + outer:function outer(A, B) { + return jStat.multiply(A.map(function(t){ return [t] }), [B]); + }, + + + // Returns the dot product of two matricies + dot: function dot(arr, arg) { + if (!isUsable(arr[0])) arr = [ arr ]; + if (!isUsable(arg[0])) arg = [ arg ]; + // convert column to row vector + var left = (arr[0].length === 1 && arr.length !== 1) ? jStat.transpose(arr) : arr, + right = (arg[0].length === 1 && arg.length !== 1) ? jStat.transpose(arg) : arg, + res = [], + row = 0, + nrow = left.length, + ncol = left[0].length, + sum, col; + for (; row < nrow; row++) { + res[row] = []; sum = 0; for (col = 0; col < ncol; col++) - sum += arr[row][col] * arg[col][rescols]; - res[row][rescols] = sum; + sum += left[row][col] * right[row][col]; + res[row] = sum; } - } - return (nrow === 1 && rescols === 1) ? res[0][0] : res; - } - return jStat.map(arr, function(value) { return value * arg; }); - }, - - // outer([1,2,3],[4,5,6]) - // === - // [[1],[2],[3]] times [[4,5,6]] - // -> - // [[4,5,6],[8,10,12],[12,15,18]] - outer:function outer(A, B) { - return jStat.multiply(A.map(function(t){ return [t] }), [B]); - }, - - - // Returns the dot product of two matricies - dot: function dot(arr, arg) { - if (!isUsable(arr[0])) arr = [ arr ]; - if (!isUsable(arg[0])) arg = [ arg ]; - // convert column to row vector - var left = (arr[0].length === 1 && arr.length !== 1) ? jStat.transpose(arr) : arr, - right = (arg[0].length === 1 && arg.length !== 1) ? jStat.transpose(arg) : arg, - res = [], - row = 0, - nrow = left.length, - ncol = left[0].length, - sum, col; - for (; row < nrow; row++) { - res[row] = []; - sum = 0; - for (col = 0; col < ncol; col++) - sum += left[row][col] * right[row][col]; - res[row] = sum; - } - return (res.length === 1) ? res[0] : res; - }, - - // raise every element by a scalar - pow: function pow(arr, arg) { - return jStat.map(arr, function(value) { return Math.pow(value, arg); }); - }, - - // exponentiate every element - exp: function exp(arr) { - return jStat.map(arr, function(value) { return Math.exp(value); }); - }, - - // generate the natural log of every element - log: function exp(arr) { - return jStat.map(arr, function(value) { return Math.log(value); }); - }, - - // generate the absolute values of the vector - abs: function abs(arr) { - return jStat.map(arr, function(value) { return Math.abs(value); }); - }, - - // computes the p-norm of the vector - // In the case that a matrix is passed, uses the first row as the vector - norm: function norm(arr, p) { - var nnorm = 0, - i = 0; - // check the p-value of the norm, and set for most common case - if (isNaN(p)) p = 2; - // check if multi-dimensional array, and make vector correction - if (isUsable(arr[0])) arr = arr[0]; - // vector norm - for (; i < arr.length; i++) { - nnorm += Math.pow(Math.abs(arr[i]), p); - } - return Math.pow(nnorm, 1 / p); - }, - - // computes the angle between two vectors in rads - // In case a matrix is passed, this uses the first row as the vector - angle: function angle(arr, arg) { - return Math.acos(jStat.dot(arr, arg) / (jStat.norm(arr) * jStat.norm(arg))); - }, - - // augment one matrix by another - // Note: this function returns a matrix, not a jStat object - aug: function aug(a, b) { - var newarr = []; - for (var i = 0; i < a.length; i++) { - newarr.push(a[i].slice()); - } - for (var i = 0; i < newarr.length; i++) { - push.apply(newarr[i], b[i]); - } - return newarr; - }, - - // The inv() function calculates the inverse of a matrix - // Create the inverse by augmenting the matrix by the identity matrix of the - // appropriate size, and then use G-J elimination on the augmented matrix. - inv: function inv(a) { - var rows = a.length; - var cols = a[0].length; - var b = jStat.identity(rows, cols); - var c = jStat.gauss_jordan(a, b); - var result = []; - var i = 0; - var j; - - //We need to copy the inverse portion to a new matrix to rid G-J artifacts - for (; i < rows; i++) { - result[i] = []; - for (j = cols; j < c[0].length; j++) - result[i][j - cols] = c[i][j]; - } - return result; - }, - - // calculate the determinant of a matrix - det: function det(a) { - var alen = a.length, - alend = alen * 2, - vals = new Array(alend), - rowshift = alen - 1, - colshift = alend - 1, - mrow = rowshift - alen + 1, - mcol = colshift, - i = 0, - result = 0, - j; - // check for special 2x2 case - if (alen === 2) { - return a[0][0] * a[1][1] - a[0][1] * a[1][0]; - } - for (; i < alend; i++) { - vals[i] = 1; - } - for (var i = 0; i < alen; i++) { - for (j = 0; j < alen; j++) { - vals[(mrow < 0) ? mrow + alen : mrow ] *= a[i][j]; - vals[(mcol < alen) ? mcol + alen : mcol ] *= a[i][j]; - mrow++; - mcol--; - } - mrow = --rowshift - alen + 1; - mcol = --colshift; - } - for (var i = 0; i < alen; i++) { - result += vals[i]; - } - for (; i < alend; i++) { - result -= vals[i]; - } - return result; - }, - - gauss_elimination: function gauss_elimination(a, b) { - var i = 0, - j = 0, - n = a.length, - m = a[0].length, - factor = 1, - sum = 0, - x = [], - maug, pivot, temp, k; - a = jStat.aug(a, b); - maug = a[0].length; - for(var i = 0; i < n; i++) { - pivot = a[i][i]; - j = i; - for (k = i + 1; k < m; k++) { - if (pivot < Math.abs(a[k][i])) { - pivot = a[k][i]; - j = k; + return (res.length === 1) ? res[0] : res; + }, + + // raise every element by a scalar + pow: function pow(arr, arg) { + return jStat.map(arr, function(value) { return Math.pow(value, arg); }); + }, + + // exponentiate every element + exp: function exp(arr) { + return jStat.map(arr, function(value) { return Math.exp(value); }); + }, + + // generate the natural log of every element + log: function exp(arr) { + return jStat.map(arr, function(value) { return Math.log(value); }); + }, + + // generate the absolute values of the vector + abs: function abs(arr) { + return jStat.map(arr, function(value) { return Math.abs(value); }); + }, + + // computes the p-norm of the vector + // In the case that a matrix is passed, uses the first row as the vector + norm: function norm(arr, p) { + var nnorm = 0, + i = 0; + // check the p-value of the norm, and set for most common case + if (isNaN(p)) p = 2; + // check if multi-dimensional array, and make vector correction + if (isUsable(arr[0])) arr = arr[0]; + // vector norm + for (; i < arr.length; i++) { + nnorm += Math.pow(Math.abs(arr[i]), p); } - } - if (j != i) { - for(k = 0; k < maug; k++) { - temp = a[i][k]; - a[i][k] = a[j][k]; - a[j][k] = temp; + return Math.pow(nnorm, 1 / p); + }, + + // computes the angle between two vectors in rads + // In case a matrix is passed, this uses the first row as the vector + angle: function angle(arr, arg) { + return Math.acos(jStat.dot(arr, arg) / (jStat.norm(arr) * jStat.norm(arg))); + }, + + // augment one matrix by another + // Note: this function returns a matrix, not a jStat object + aug: function aug(a, b) { + var newarr = []; + for (var i = 0; i < a.length; i++) { + newarr.push(a[i].slice()); } - } - for (j = i + 1; j < n; j++) { - factor = a[j][i] / a[i][i]; - for(k = i; k < maug; k++) { - a[j][k] = a[j][k] - factor * a[i][k]; + for (var i = 0; i < newarr.length; i++) { + push.apply(newarr[i], b[i]); } - } - } - for (var i = n - 1; i >= 0; i--) { - sum = 0; - for (j = i + 1; j<= n - 1; j++) { - sum = sum + x[j] * a[i][j]; - } - x[i] =(a[i][maug - 1] - sum) / a[i][i]; - } - return x; - }, - - gauss_jordan: function gauss_jordan(a, b) { - var m = jStat.aug(a, b), - h = m.length, - w = m[0].length; - var c = 0; - // find max pivot - for (var y = 0; y < h; y++) { - var maxrow = y; - for (var y2 = y+1; y2 < h; y2++) { - if (Math.abs(m[y2][y]) > Math.abs(m[maxrow][y])) - maxrow = y2; - } - var tmp = m[y]; - m[y] = m[maxrow]; - m[maxrow] = tmp; - for (var y2 = y+1; y2 < h; y2++) { - c = m[y2][y] / m[y][y]; - for (var x = y; x < w; x++) { - m[y2][x] -= m[y][x] * c; + return newarr; + }, + + // The inv() function calculates the inverse of a matrix + // Create the inverse by augmenting the matrix by the identity matrix of the + // appropriate size, and then use G-J elimination on the augmented matrix. + inv: function inv(a) { + var rows = a.length; + var cols = a[0].length; + var b = jStat.identity(rows, cols); + var c = jStat.gauss_jordan(a, b); + var result = []; + var i = 0; + var j; + + //We need to copy the inverse portion to a new matrix to rid G-J artifacts + for (; i < rows; i++) { + result[i] = []; + for (j = cols; j < c[0].length; j++) + result[i][j - cols] = c[i][j]; } - } - } - // backsubstitute - for (var y = h-1; y >= 0; y--) { - c = m[y][y]; - for (var y2 = 0; y2 < y; y2++) { - for (var x = w-1; x > y-1; x--) { - m[y2][x] -= m[y][x] * m[y2][y] / c; + return result; + }, + + // calculate the determinant of a matrix + det: function det(a) { + var alen = a.length, + alend = alen * 2, + vals = new Array(alend), + rowshift = alen - 1, + colshift = alend - 1, + mrow = rowshift - alen + 1, + mcol = colshift, + i = 0, + result = 0, + j; + // check for special 2x2 case + if (alen === 2) { + return a[0][0] * a[1][1] - a[0][1] * a[1][0]; + } + for (; i < alend; i++) { + vals[i] = 1; + } + for (var i = 0; i < alen; i++) { + for (j = 0; j < alen; j++) { + vals[(mrow < 0) ? mrow + alen : mrow ] *= a[i][j]; + vals[(mcol < alen) ? mcol + alen : mcol ] *= a[i][j]; + mrow++; + mcol--; + } + mrow = --rowshift - alen + 1; + mcol = --colshift; + } + for (var i = 0; i < alen; i++) { + result += vals[i]; + } + for (; i < alend; i++) { + result -= vals[i]; + } + return result; + }, + + gauss_elimination: function gauss_elimination(a, b) { + var i = 0, + j = 0, + n = a.length, + m = a[0].length, + factor = 1, + sum = 0, + x = [], + maug, pivot, temp, k; + a = jStat.aug(a, b); + maug = a[0].length; + for(var i = 0; i < n; i++) { + pivot = a[i][i]; + j = i; + for (k = i + 1; k < m; k++) { + if (pivot < Math.abs(a[k][i])) { + pivot = a[k][i]; + j = k; + } + } + if (j != i) { + for(k = 0; k < maug; k++) { + temp = a[i][k]; + a[i][k] = a[j][k]; + a[j][k] = temp; + } + } + for (j = i + 1; j < n; j++) { + factor = a[j][i] / a[i][i]; + for(k = i; k < maug; k++) { + a[j][k] = a[j][k] - factor * a[i][k]; + } + } + } + for (var i = n - 1; i >= 0; i--) { + sum = 0; + for (j = i + 1; j<= n - 1; j++) { + sum = sum + x[j] * a[i][j]; + } + x[i] =(a[i][maug - 1] - sum) / a[i][i]; + } + return x; + }, + + gauss_jordan: function gauss_jordan(a, b) { + var m = jStat.aug(a, b), + h = m.length, + w = m[0].length; + var c = 0; + // find max pivot + for (var y = 0; y < h; y++) { + var maxrow = y; + for (var y2 = y+1; y2 < h; y2++) { + if (Math.abs(m[y2][y]) > Math.abs(m[maxrow][y])) + maxrow = y2; + } + var tmp = m[y]; + m[y] = m[maxrow]; + m[maxrow] = tmp; + for (var y2 = y+1; y2 < h; y2++) { + c = m[y2][y] / m[y][y]; + for (var x = y; x < w; x++) { + m[y2][x] -= m[y][x] * c; + } + } + } + // backsubstitute + for (var y = h-1; y >= 0; y--) { + c = m[y][y]; + for (var y2 = 0; y2 < y; y2++) { + for (var x = w-1; x > y-1; x--) { + m[y2][x] -= m[y][x] * m[y2][y] / c; + } + } + m[y][y] /= c; + for (var x = h; x < w; x++) { + m[y][x] /= c; + } + } + return m; + }, + + // solve equation + // Ax=b + // A is upper triangular matrix + // A=[[1,2,3],[0,4,5],[0,6,7]] + // b=[1,2,3] + // triaUpSolve(A,b) // -> [2.666,0.1666,1.666] + // if you use matrix style + // A=[[1,2,3],[0,4,5],[0,6,7]] + // b=[[1],[2],[3]] + // will return [[2.666],[0.1666],[1.666]] + triaUpSolve: function triaUpSolve(A, b) { + var size = A[0].length; + var x = jStat.zeros(1, size)[0]; + var parts; + var matrix_mode = false; + + if (b[0].length != undefined) { + b = b.map(function(i){ return i[0] }); + matrix_mode = true; } - } - m[y][y] /= c; - for (var x = h; x < w; x++) { - m[y][x] /= c; - } - } - return m; - }, - - // solve equation - // Ax=b - // A is upper triangular matrix - // A=[[1,2,3],[0,4,5],[0,6,7]] - // b=[1,2,3] - // triaUpSolve(A,b) // -> [2.666,0.1666,1.666] - // if you use matrix style - // A=[[1,2,3],[0,4,5],[0,6,7]] - // b=[[1],[2],[3]] - // will return [[2.666],[0.1666],[1.666]] - triaUpSolve: function triaUpSolve(A, b) { - var size = A[0].length; - var x = jStat.zeros(1, size)[0]; - var parts; - var matrix_mode = false; - - if (b[0].length != undefined) { - b = b.map(function(i){ return i[0] }); - matrix_mode = true; - } - jStat.arange(size - 1, -1, -1).forEach(function(i) { - parts = jStat.arange(i + 1, size).map(function(j) { - return x[j] * A[i][j]; - }); - x[i] = (b[i] - jStat.sum(parts)) / A[i][i]; - }); + jStat.arange(size - 1, -1, -1).forEach(function(i) { + parts = jStat.arange(i + 1, size).map(function(j) { + return x[j] * A[i][j]; + }); + x[i] = (b[i] - jStat.sum(parts)) / A[i][i]; + }); - if (matrix_mode) - return x.map(function(i){ return [i] }); - return x; - }, - - triaLowSolve: function triaLowSolve(A, b) { - // like to triaUpSolve but A is lower triangular matrix - var size = A[0].length; - var x = jStat.zeros(1, size)[0]; - var parts; - - var matrix_mode=false; - if (b[0].length != undefined) { - b = b.map(function(i){ return i[0] }); - matrix_mode = true; - } + if (matrix_mode) + return x.map(function(i){ return [i] }); + return x; + }, + + triaLowSolve: function triaLowSolve(A, b) { + // like to triaUpSolve but A is lower triangular matrix + var size = A[0].length; + var x = jStat.zeros(1, size)[0]; + var parts; + + var matrix_mode=false; + if (b[0].length != undefined) { + b = b.map(function(i){ return i[0] }); + matrix_mode = true; + } - jStat.arange(size).forEach(function(i) { - parts = jStat.arange(i).map(function(j) { - return A[i][j] * x[j]; - }); - x[i] = (b[i] - jStat.sum(parts)) / A[i][i]; - }); + jStat.arange(size).forEach(function(i) { + parts = jStat.arange(i).map(function(j) { + return A[i][j] * x[j]; + }); + x[i] = (b[i] - jStat.sum(parts)) / A[i][i]; + }); - if (matrix_mode) - return x.map(function(i){ return [i] }); - return x; - }, - - - // A -> [L,U] - // A=LU - // L is lower triangular matrix - // U is upper triangular matrix - lu: function lu(A) { - var size = A.length; - //var L=jStat.diagonal(jStat.ones(1,size)[0]); - var L = jStat.identity(size); - var R = jStat.zeros(A.length, A[0].length); - var parts; - jStat.arange(size).forEach(function(t) { - R[0][t] = A[0][t]; - }); - jStat.arange(1, size).forEach(function(l) { - jStat.arange(l).forEach(function(i) { - parts = jStat.arange(i).map(function(jj) { - return L[l][jj] * R[jj][i]; + if (matrix_mode) + return x.map(function(i){ return [i] }); + return x; + }, + + + // A -> [L,U] + // A=LU + // L is lower triangular matrix + // U is upper triangular matrix + lu: function lu(A) { + var size = A.length; + //var L=jStat.diagonal(jStat.ones(1,size)[0]); + var L = jStat.identity(size); + var R = jStat.zeros(A.length, A[0].length); + var parts; + jStat.arange(size).forEach(function(t) { + R[0][t] = A[0][t]; }); - L[l][i] = (A[l][i] - jStat.sum(parts)) / R[i][i]; - }); - jStat.arange(l, size).forEach(function(j) { - parts = jStat.arange(l).map(function(jj) { - return L[l][jj] * R[jj][j]; + jStat.arange(1, size).forEach(function(l) { + jStat.arange(l).forEach(function(i) { + parts = jStat.arange(i).map(function(jj) { + return L[l][jj] * R[jj][i]; + }); + L[l][i] = (A[l][i] - jStat.sum(parts)) / R[i][i]; + }); + jStat.arange(l, size).forEach(function(j) { + parts = jStat.arange(l).map(function(jj) { + return L[l][jj] * R[jj][j]; + }); + R[l][j] = A[i][j] - jStat.sum(parts); + }); }); - R[l][j] = A[i][j] - jStat.sum(parts); - }); - }); - return [L, R]; - }, - - // A -> T - // A=TT' - // T is lower triangular matrix - cholesky: function cholesky(A) { - var size = A.length; - var T = jStat.zeros(A.length, A[0].length); - var parts; - jStat.arange(size).forEach(function(i) { - parts = jStat.arange(i).map(function(t) { - return Math.pow(T[i][t],2); - }); - T[i][i] = Math.sqrt(A[i][i] - jStat.sum(parts)); - jStat.arange(i + 1, size).forEach(function(j) { - parts = jStat.arange(i).map(function(t) { - return T[i][t] * T[j][t]; + return [L, R]; + }, + + // A -> T + // A=TT' + // T is lower triangular matrix + cholesky: function cholesky(A) { + var size = A.length; + var T = jStat.zeros(A.length, A[0].length); + var parts; + jStat.arange(size).forEach(function(i) { + parts = jStat.arange(i).map(function(t) { + return Math.pow(T[i][t],2); + }); + T[i][i] = Math.sqrt(A[i][i] - jStat.sum(parts)); + jStat.arange(i + 1, size).forEach(function(j) { + parts = jStat.arange(i).map(function(t) { + return T[i][t] * T[j][t]; + }); + T[j][i] = (A[i][j] - jStat.sum(parts)) / T[i][i]; + }); }); - T[j][i] = (A[i][j] - jStat.sum(parts)) / T[i][i]; - }); - }); - return T; - }, - - - gauss_jacobi: function gauss_jacobi(a, b, x, r) { - var i = 0; - var j = 0; - var n = a.length; - var l = []; - var u = []; - var d = []; - var xv, c, h, xk; - for (; i < n; i++) { - l[i] = []; - u[i] = []; - d[i] = []; - for (j = 0; j < n; j++) { - if (i > j) { - l[i][j] = a[i][j]; - u[i][j] = d[i][j] = 0; - } else if (i < j) { - u[i][j] = a[i][j]; - l[i][j] = d[i][j] = 0; - } else { - d[i][j] = a[i][j]; - l[i][j] = u[i][j] = 0; + return T; + }, + + + gauss_jacobi: function gauss_jacobi(a, b, x, r) { + var i = 0; + var j = 0; + var n = a.length; + var l = []; + var u = []; + var d = []; + var xv, c, h, xk; + for (; i < n; i++) { + l[i] = []; + u[i] = []; + d[i] = []; + for (j = 0; j < n; j++) { + if (i > j) { + l[i][j] = a[i][j]; + u[i][j] = d[i][j] = 0; + } else if (i < j) { + u[i][j] = a[i][j]; + l[i][j] = d[i][j] = 0; + } else { + d[i][j] = a[i][j]; + l[i][j] = u[i][j] = 0; + } + } + } + h = jStat.multiply(jStat.multiply(jStat.inv(d), jStat.add(l, u)), -1); + c = jStat.multiply(jStat.inv(d), b); + xv = x; + xk = jStat.add(jStat.multiply(h, x), c); + i = 2; + while (Math.abs(jStat.norm(jStat.subtract(xk,xv))) > r) { + xv = xk; + xk = jStat.add(jStat.multiply(h, xv), c); + i++; + } + return xk; + }, + + gauss_seidel: function gauss_seidel(a, b, x, r) { + var i = 0; + var n = a.length; + var l = []; + var u = []; + var d = []; + var j, xv, c, h, xk; + for (; i < n; i++) { + l[i] = []; + u[i] = []; + d[i] = []; + for (j = 0; j < n; j++) { + if (i > j) { + l[i][j] = a[i][j]; + u[i][j] = d[i][j] = 0; + } else if (i < j) { + u[i][j] = a[i][j]; + l[i][j] = d[i][j] = 0; + } else { + d[i][j] = a[i][j]; + l[i][j] = u[i][j] = 0; + } + } + } + h = jStat.multiply(jStat.multiply(jStat.inv(jStat.add(d, l)), u), -1); + c = jStat.multiply(jStat.inv(jStat.add(d, l)), b); + xv = x; + xk = jStat.add(jStat.multiply(h, x), c); + i = 2; + while (Math.abs(jStat.norm(jStat.subtract(xk, xv))) > r) { + xv = xk; + xk = jStat.add(jStat.multiply(h, xv), c); + i = i + 1; + } + return xk; + }, + + SOR: function SOR(a, b, x, r, w) { + var i = 0; + var n = a.length; + var l = []; + var u = []; + var d = []; + var j, xv, c, h, xk; + for (; i < n; i++) { + l[i] = []; + u[i] = []; + d[i] = []; + for (j = 0; j < n; j++) { + if (i > j) { + l[i][j] = a[i][j]; + u[i][j] = d[i][j] = 0; + } else if (i < j) { + u[i][j] = a[i][j]; + l[i][j] = d[i][j] = 0; + } else { + d[i][j] = a[i][j]; + l[i][j] = u[i][j] = 0; + } + } + } + h = jStat.multiply(jStat.inv(jStat.add(d, jStat.multiply(l, w))), + jStat.subtract(jStat.multiply(d, 1 - w), + jStat.multiply(u, w))); + c = jStat.multiply(jStat.multiply(jStat.inv(jStat.add(d, + jStat.multiply(l, w))), b), w); + xv = x; + xk = jStat.add(jStat.multiply(h, x), c); + i = 2; + while (Math.abs(jStat.norm(jStat.subtract(xk, xv))) > r) { + xv = xk; + xk = jStat.add(jStat.multiply(h, xv), c); + i++; + } + return xk; + }, + + householder: function householder(a) { + var m = a.length; + var n = a[0].length; + var i = 0; + var w = []; + var p = []; + var alpha, r, k, j, factor; + for (; i < m - 1; i++) { + alpha = 0; + for (j = i + 1; j < n; j++) + alpha += (a[j][i] * a[j][i]); + factor = (a[i + 1][i] > 0) ? -1 : 1; + alpha = factor * Math.sqrt(alpha); + r = Math.sqrt((((alpha * alpha) - a[i + 1][i] * alpha) / 2)); + w = jStat.zeros(m, 1); + w[i + 1][0] = (a[i + 1][i] - alpha) / (2 * r); + for (k = i + 2; k < m; k++) w[k][0] = a[k][i] / (2 * r); + p = jStat.subtract(jStat.identity(m, n), + jStat.multiply(jStat.multiply(w, jStat.transpose(w)), 2)); + a = jStat.multiply(p, jStat.multiply(a, p)); + } + return a; + }, + + // A -> [Q,R] + // Q is orthogonal matrix + // R is upper triangular + QR: (function() { + // x -> Q + // find a orthogonal matrix Q st. + // Qx=y + // y is [||x||,0,0,...] + + // quick ref + var sum = jStat.sum; + var range = jStat.arange; + + function qr2(x) { + // quick impletation + // https://www.stat.wisc.edu/~larget/math496/qr.html + + var n = x.length; + var p = x[0].length; + + x = jStat.copy(x); + r = jStat.zeros(p, p); + + var i,j,k; + for(j = 0; j < p; j++){ + r[j][j] = Math.sqrt(sum(range(n).map(function(i){ + return x[i][j] * x[i][j]; + }))); + for(i = 0; i < n; i++){ + x[i][j] = x[i][j] / r[j][j]; + } + for(k = j+1; k < p; k++){ + r[j][k] = sum(range(n).map(function(i){ + return x[i][j] * x[i][k]; + })); + for(i = 0; i < n; i++){ + x[i][k] = x[i][k] - x[i][j]*r[j][k]; + } + } + } + return [x, r]; + } + + return qr2; + }()), + + lstsq: (function(A, b) { + // solve least squard problem for Ax=b as QR decomposition way if b is + // [[b1],[b2],[b3]] form will return [[x1],[x2],[x3]] array form solution + // else b is [b1,b2,b3] form will return [x1,x2,x3] array form solution + function R_I(A) { + A = jStat.copy(A); + var size = A.length; + var I = jStat.identity(size); + jStat.arange(size - 1, -1, -1).forEach(function(i) { + jStat.sliceAssign( + I, { row: i }, jStat.divide(jStat.slice(I, { row: i }), A[i][i])); + jStat.sliceAssign( + A, { row: i }, jStat.divide(jStat.slice(A, { row: i }), A[i][i])); + jStat.arange(i).forEach(function(j) { + var c = jStat.multiply(A[j][i], -1); + var Aj = jStat.slice(A, { row: j }); + var cAi = jStat.multiply(jStat.slice(A, { row: i }), c); + jStat.sliceAssign(A, { row: j }, jStat.add(Aj, cAi)); + var Ij = jStat.slice(I, { row: j }); + var cIi = jStat.multiply(jStat.slice(I, { row: i }), c); + jStat.sliceAssign(I, { row: j }, jStat.add(Ij, cIi)); + }); + }); + return I; + } + + function qr_solve(A, b){ + var array_mode = false; + if (b[0].length === undefined) { + // [c1,c2,c3] mode + b = b.map(function(x){ return [x] }); + array_mode = true; + } + var QR = jStat.QR(A); + var Q = QR[0]; + var R = QR[1]; + var attrs = A[0].length; + var Q1 = jStat.slice(Q,{col:{end:attrs}}); + var R1 = jStat.slice(R,{row:{end:attrs}}); + var RI = R_I(R1); + var Q2 = jStat.transpose(Q1); + + if(Q2[0].length === undefined){ + Q2 = [Q2]; // The confusing jStat.multifly implementation threat nature process again. + } + + var x = jStat.multiply(jStat.multiply(RI, Q2), b); + + if(x.length === undefined){ + x = [[x]]; // The confusing jStat.multifly implementation threat nature process again. + } + + + if (array_mode) + return x.map(function(i){ return i[0] }); + return x; + } + + return qr_solve; + }()), + + jacobi: function jacobi(a) { + var condition = 1; + var n = a.length; + var e = jStat.identity(n, n); + var ev = []; + var b, i, j, p, q, maxim, theta, s; + // condition === 1 only if tolerance is not reached + while (condition === 1) { + maxim = a[0][1]; + p = 0; + q = 1; + for (var i = 0; i < n; i++) { + for (j = 0; j < n; j++) { + if (i != j) { + if (maxim < Math.abs(a[i][j])) { + maxim = Math.abs(a[i][j]); + p = i; + q = j; + } + } + } + } + if (a[p][p] === a[q][q]) + theta = (a[p][q] > 0) ? Math.PI / 4 : -Math.PI / 4; + else + theta = Math.atan(2 * a[p][q] / (a[p][p] - a[q][q])) / 2; + s = jStat.identity(n, n); + s[p][p] = Math.cos(theta); + s[p][q] = -Math.sin(theta); + s[q][p] = Math.sin(theta); + s[q][q] = Math.cos(theta); + // eigen vector matrix + e = jStat.multiply(e, s); + b = jStat.multiply(jStat.multiply(jStat.inv(s), a), s); + a = b; + condition = 0; + for (var i = 1; i < n; i++) { + for (j = 1; j < n; j++) { + if (i != j && Math.abs(a[i][j]) > 0.001) { + condition = 1; + } + } + } + } + for (var i = 0; i < n; i++) ev.push(a[i][i]); + //returns both the eigenvalue and eigenmatrix + return [e, ev]; + }, + + rungekutta: function rungekutta(f, h, p, t_j, u_j, order) { + var k1, k2, u_j1, k3, k4; + if (order === 2) { + while (t_j <= p) { + k1 = h * f(t_j, u_j); + k2 = h * f(t_j + h, u_j + k1); + u_j1 = u_j + (k1 + k2) / 2; + u_j = u_j1; + t_j = t_j + h; + } + } + if (order === 4) { + while (t_j <= p) { + k1 = h * f(t_j, u_j); + k2 = h * f(t_j + h / 2, u_j + k1 / 2); + k3 = h * f(t_j + h / 2, u_j + k2 / 2); + k4 = h * f(t_j +h, u_j + k3); + u_j1 = u_j + (k1 + 2 * k2 + 2 * k3 + k4) / 6; + u_j = u_j1; + t_j = t_j + h; + } + } + return u_j; + }, + + romberg: function romberg(f, a, b, order) { + var i = 0; + var h = (b - a) / 2; + var x = []; + var h1 = []; + var g = []; + var m, a1, j, k, I; + while (i < order / 2) { + I = f(a); + for (j = a, k = 0; j <= b; j = j + h, k++) x[k] = j; + m = x.length; + for (j = 1; j < m - 1; j++) { + I += (((j % 2) !== 0) ? 4 : 2) * f(x[j]); + } + I = (h / 3) * (I + f(b)); + g[i] = I; + h /= 2; + i++; + } + a1 = g.length; + m = 1; + while (a1 !== 1) { + for (j = 0; j < a1 - 1; j++) + h1[j] = ((Math.pow(4, m)) * g[j + 1] - g[j]) / (Math.pow(4, m) - 1); + a1 = h1.length; + g = h1; + h1 = []; + m++; + } + return g; + }, + + richardson: function richardson(X, f, x, h) { + function pos(X, x) { + var i = 0; + var n = X.length; + var p; + for (; i < n; i++) + if (X[i] === x) p = i; + return p; + } + var n = X.length, + h_min = Math.abs(x - X[pos(X, x) + 1]), + i = 0, + g = [], + h1 = [], + y1, y2, m, a, j; + while (h >= h_min) { + y1 = pos(X, x + h); + y2 = pos(X, x); + g[i] = (f[y1] - 2 * f[y2] + f[2 * y2 - y1]) / (h * h); + h /= 2; + i++; + } + a = g.length; + m = 1; + while (a != 1) { + for (j = 0; j < a - 1; j++) + h1[j] = ((Math.pow(4, m)) * g[j + 1] - g[j]) / (Math.pow(4, m) - 1); + a = h1.length; + g = h1; + h1 = []; + m++; + } + return g; + }, + + simpson: function simpson(f, a, b, n) { + var h = (b - a) / n; + var I = f(a); + var x = []; + var j = a; + var k = 0; + var i = 1; + var m; + for (; j <= b; j = j + h, k++) + x[k] = j; + m = x.length; + for (; i < m - 1; i++) { + I += ((i % 2 !== 0) ? 4 : 2) * f(x[i]); + } + return (h / 3) * (I + f(b)); + }, + + hermite: function hermite(X, F, dF, value) { + var n = X.length; + var p = 0; + var i = 0; + var l = []; + var dl = []; + var A = []; + var B = []; + var j; + for (; i < n; i++) { + l[i] = 1; + for (j = 0; j < n; j++) { + if (i != j) l[i] *= (value - X[j]) / (X[i] - X[j]); + } + dl[i] = 0; + for (j = 0; j < n; j++) { + if (i != j) dl[i] += 1 / (X [i] - X[j]); + } + A[i] = (1 - 2 * (value - X[i]) * dl[i]) * (l[i] * l[i]); + B[i] = (value - X[i]) * (l[i] * l[i]); + p += (A[i] * F[i] + B[i] * dF[i]); + } + return p; + }, + + lagrange: function lagrange(X, F, value) { + var p = 0; + var i = 0; + var j, l; + var n = X.length; + for (; i < n; i++) { + l = F[i]; + for (j = 0; j < n; j++) { + // calculating the lagrange polynomial L_i + if (i != j) l *= (value - X[j]) / (X[i] - X[j]); + } + // adding the lagrange polynomials found above + p += l; + } + return p; + }, + + cubic_spline: function cubic_spline(X, F, value) { + var n = X.length; + var i = 0, j; + var A = []; + var B = []; + var alpha = []; + var c = []; + var h = []; + var b = []; + var d = []; + for (; i < n - 1; i++) + h[i] = X[i + 1] - X[i]; + alpha[0] = 0; + for (var i = 1; i < n - 1; i++) { + alpha[i] = (3 / h[i]) * (F[i + 1] - F[i]) - + (3 / h[i-1]) * (F[i] - F[i-1]); + } + for (var i = 1; i < n - 1; i++) { + A[i] = []; + B[i] = []; + A[i][i-1] = h[i-1]; + A[i][i] = 2 * (h[i - 1] + h[i]); + A[i][i+1] = h[i]; + B[i][0] = alpha[i]; + } + c = jStat.multiply(jStat.inv(A), B); + for (j = 0; j < n - 1; j++) { + b[j] = (F[j + 1] - F[j]) / h[j] - h[j] * (c[j + 1][0] + 2 * c[j][0]) / 3; + d[j] = (c[j + 1][0] - c[j][0]) / (3 * h[j]); + } + for (j = 0; j < n; j++) { + if (X[j] > value) break; + } + j -= 1; + return F[j] + (value - X[j]) * b[j] + jStat.sq(value-X[j]) * + c[j] + (value - X[j]) * jStat.sq(value - X[j]) * d[j]; + }, + + gauss_quadrature: function gauss_quadrature() { + throw new Error('gauss_quadrature not yet implemented'); + }, + + PCA: function PCA(X) { + var m = X.length; + var n = X[0].length; + var i = 0; + var j, temp1; + var u = []; + var D = []; + var result = []; + var temp2 = []; + var Y = []; + var Bt = []; + var B = []; + var C = []; + var V = []; + var Vt = []; + for (var i = 0; i < m; i++) { + u[i] = jStat.sum(X[i]) / n; + } + for (var i = 0; i < n; i++) { + B[i] = []; + for(j = 0; j < m; j++) { + B[i][j] = X[j][i] - u[j]; + } + } + B = jStat.transpose(B); + for (var i = 0; i < m; i++) { + C[i] = []; + for (j = 0; j < m; j++) { + C[i][j] = (jStat.dot([B[i]], [B[j]])) / (n - 1); + } + } + result = jStat.jacobi(C); + V = result[0]; + D = result[1]; + Vt = jStat.transpose(V); + for (var i = 0; i < D.length; i++) { + for (j = i; j < D.length; j++) { + if(D[i] < D[j]) { + temp1 = D[i]; + D[i] = D[j]; + D[j] = temp1; + temp2 = Vt[i]; + Vt[i] = Vt[j]; + Vt[j] = temp2; + } + } + } + Bt = jStat.transpose(B); + for (var i = 0; i < m; i++) { + Y[i] = []; + for (j = 0; j < Bt.length; j++) { + Y[i][j] = jStat.dot([Vt[i]], [Bt[j]]); + } } + return [X, D, Vt, Y]; } - } - h = jStat.multiply(jStat.multiply(jStat.inv(d), jStat.add(l, u)), -1); - c = jStat.multiply(jStat.inv(d), b); - xv = x; - xk = jStat.add(jStat.multiply(h, x), c); - i = 2; - while (Math.abs(jStat.norm(jStat.subtract(xk,xv))) > r) { - xv = xk; - xk = jStat.add(jStat.multiply(h, xv), c); - i++; - } - return xk; - }, - - gauss_seidel: function gauss_seidel(a, b, x, r) { - var i = 0; - var n = a.length; - var l = []; - var u = []; - var d = []; - var j, xv, c, h, xk; - for (; i < n; i++) { - l[i] = []; - u[i] = []; - d[i] = []; - for (j = 0; j < n; j++) { - if (i > j) { - l[i][j] = a[i][j]; - u[i][j] = d[i][j] = 0; - } else if (i < j) { - u[i][j] = a[i][j]; - l[i][j] = d[i][j] = 0; + }); + + // extend jStat.fn with methods that require one argument + (function(funcs) { + for (var i = 0; i < funcs.length; i++) (function(passfunc) { + jStat.fn[passfunc] = function(arg, func) { + var tmpthis = this; + // check for callback + if (func) { + setTimeout(function() { + func.call(tmpthis, jStat.fn[passfunc].call(tmpthis, arg)); + }, 15); + return this; + } + if (typeof jStat[passfunc](this, arg) === 'number') + return jStat[passfunc](this, arg); + else + return jStat(jStat[passfunc](this, arg)); + }; + }(funcs[i])); + }('add divide multiply subtract dot pow exp log abs norm angle'.split(' '))); + + }(jStat, Math)); + (function(jStat, Math) { + + var slice = [].slice; + var isNumber = jStat.utils.isNumber; + var isArray = jStat.utils.isArray; + + // flag==true denotes use of sample standard deviation + // Z Statistics + jStat.extend({ + // 2 different parameter lists: + // (value, mean, sd) + // (value, array, flag) + zscore: function zscore() { + var args = slice.call(arguments); + if (isNumber(args[1])) { + return (args[0] - args[1]) / args[2]; + } + return (args[0] - jStat.mean(args[1])) / jStat.stdev(args[1], args[2]); + }, + + // 3 different paramter lists: + // (value, mean, sd, sides) + // (zscore, sides) + // (value, array, sides, flag) + ztest: function ztest() { + var args = slice.call(arguments); + var z; + if (isArray(args[1])) { + // (value, array, sides, flag) + z = jStat.zscore(args[0],args[1],args[3]); + return (args[2] === 1) ? + (jStat.normal.cdf(-Math.abs(z), 0, 1)) : + (jStat.normal.cdf(-Math.abs(z), 0, 1)*2); } else { - d[i][j] = a[i][j]; - l[i][j] = u[i][j] = 0; + if (args.length > 2) { + // (value, mean, sd, sides) + z = jStat.zscore(args[0],args[1],args[2]); + return (args[3] === 1) ? + (jStat.normal.cdf(-Math.abs(z),0,1)) : + (jStat.normal.cdf(-Math.abs(z),0,1)* 2); + } else { + // (zscore, sides) + z = args[0]; + return (args[1] === 1) ? + (jStat.normal.cdf(-Math.abs(z),0,1)) : + (jStat.normal.cdf(-Math.abs(z),0,1)*2); + } } } - } - h = jStat.multiply(jStat.multiply(jStat.inv(jStat.add(d, l)), u), -1); - c = jStat.multiply(jStat.inv(jStat.add(d, l)), b); - xv = x; - xk = jStat.add(jStat.multiply(h, x), c); - i = 2; - while (Math.abs(jStat.norm(jStat.subtract(xk, xv))) > r) { - xv = xk; - xk = jStat.add(jStat.multiply(h, xv), c); - i = i + 1; - } - return xk; - }, - - SOR: function SOR(a, b, x, r, w) { - var i = 0; - var n = a.length; - var l = []; - var u = []; - var d = []; - var j, xv, c, h, xk; - for (; i < n; i++) { - l[i] = []; - u[i] = []; - d[i] = []; - for (j = 0; j < n; j++) { - if (i > j) { - l[i][j] = a[i][j]; - u[i][j] = d[i][j] = 0; - } else if (i < j) { - u[i][j] = a[i][j]; - l[i][j] = d[i][j] = 0; - } else { - d[i][j] = a[i][j]; - l[i][j] = u[i][j] = 0; + }); + + jStat.extend(jStat.fn, { + zscore: function zscore(value, flag) { + return (value - this.mean()) / this.stdev(flag); + }, + + ztest: function ztest(value, sides, flag) { + var zscore = Math.abs(this.zscore(value, flag)); + return (sides === 1) ? + (jStat.normal.cdf(-zscore, 0, 1)) : + (jStat.normal.cdf(-zscore, 0, 1) * 2); + } + }); + + // T Statistics + jStat.extend({ + // 2 parameter lists + // (value, mean, sd, n) + // (value, array) + tscore: function tscore() { + var args = slice.call(arguments); + return (args.length === 4) ? + ((args[0] - args[1]) / (args[2] / Math.sqrt(args[3]))) : + ((args[0] - jStat.mean(args[1])) / + (jStat.stdev(args[1], true) / Math.sqrt(args[1].length))); + }, + + // 3 different paramter lists: + // (value, mean, sd, n, sides) + // (tscore, n, sides) + // (value, array, sides) + ttest: function ttest() { + var args = slice.call(arguments); + var tscore; + if (args.length === 5) { + tscore = Math.abs(jStat.tscore(args[0], args[1], args[2], args[3])); + return (args[4] === 1) ? + (jStat.studentt.cdf(-tscore, args[3]-1)) : + (jStat.studentt.cdf(-tscore, args[3]-1)*2); + } + if (isNumber(args[1])) { + tscore = Math.abs(args[0]); + return (args[2] == 1) ? + (jStat.studentt.cdf(-tscore, args[1]-1)) : + (jStat.studentt.cdf(-tscore, args[1]-1) * 2); } + tscore = Math.abs(jStat.tscore(args[0], args[1])); + return (args[2] == 1) ? + (jStat.studentt.cdf(-tscore, args[1].length-1)) : + (jStat.studentt.cdf(-tscore, args[1].length-1) * 2); } - } - h = jStat.multiply(jStat.inv(jStat.add(d, jStat.multiply(l, w))), - jStat.subtract(jStat.multiply(d, 1 - w), - jStat.multiply(u, w))); - c = jStat.multiply(jStat.multiply(jStat.inv(jStat.add(d, - jStat.multiply(l, w))), b), w); - xv = x; - xk = jStat.add(jStat.multiply(h, x), c); - i = 2; - while (Math.abs(jStat.norm(jStat.subtract(xk, xv))) > r) { - xv = xk; - xk = jStat.add(jStat.multiply(h, xv), c); - i++; - } - return xk; - }, - - householder: function householder(a) { - var m = a.length; - var n = a[0].length; - var i = 0; - var w = []; - var p = []; - var alpha, r, k, j, factor; - for (; i < m - 1; i++) { - alpha = 0; - for (j = i + 1; j < n; j++) - alpha += (a[j][i] * a[j][i]); - factor = (a[i + 1][i] > 0) ? -1 : 1; - alpha = factor * Math.sqrt(alpha); - r = Math.sqrt((((alpha * alpha) - a[i + 1][i] * alpha) / 2)); - w = jStat.zeros(m, 1); - w[i + 1][0] = (a[i + 1][i] - alpha) / (2 * r); - for (k = i + 2; k < m; k++) w[k][0] = a[k][i] / (2 * r); - p = jStat.subtract(jStat.identity(m, n), - jStat.multiply(jStat.multiply(w, jStat.transpose(w)), 2)); - a = jStat.multiply(p, jStat.multiply(a, p)); - } - return a; - }, - - // A -> [Q,R] - // Q is orthogonal matrix - // R is upper triangular - QR: (function() { - // x -> Q - // find a orthogonal matrix Q st. - // Qx=y - // y is [||x||,0,0,...] - - // quick ref - var sum = jStat.sum; - var range = jStat.arange; - - function qr2(x) { - // quick impletation - // https://www.stat.wisc.edu/~larget/math496/qr.html - - var n = x.length; - var p = x[0].length; - - x = jStat.copy(x); - r = jStat.zeros(p, p); - - var i,j,k; - for(j = 0; j < p; j++){ - r[j][j] = Math.sqrt(sum(range(n).map(function(i){ - return x[i][j] * x[i][j]; - }))); - for(i = 0; i < n; i++){ - x[i][j] = x[i][j] / r[j][j]; - } - for(k = j+1; k < p; k++){ - r[j][k] = sum(range(n).map(function(i){ - return x[i][j] * x[i][k]; - })); - for(i = 0; i < n; i++){ - x[i][k] = x[i][k] - x[i][j]*r[j][k]; + }); + + jStat.extend(jStat.fn, { + tscore: function tscore(value) { + return (value - this.mean()) / (this.stdev(true) / Math.sqrt(this.cols())); + }, + + ttest: function ttest(value, sides) { + return (sides === 1) ? + (1 - jStat.studentt.cdf(Math.abs(this.tscore(value)), this.cols()-1)) : + (jStat.studentt.cdf(-Math.abs(this.tscore(value)), this.cols()-1)*2); + } + }); + + // F Statistics + jStat.extend({ + // Paramter list is as follows: + // (array1, array2, array3, ...) + // or it is an array of arrays + // array of arrays conversion + anovafscore: function anovafscore() { + var args = slice.call(arguments), + expVar, sample, sampMean, sampSampMean, tmpargs, unexpVar, i, j; + if (args.length === 1) { + tmpargs = new Array(args[0].length); + for (var i = 0; i < args[0].length; i++) { + tmpargs[i] = args[0][i]; + } + args = tmpargs; + } + // 2 sample case + if (args.length === 2) { + return jStat.variance(args[0]) / jStat.variance(args[1]); + } + // Builds sample array + sample = new Array(); + for (var i = 0; i < args.length; i++) { + sample = sample.concat(args[i]); + } + sampMean = jStat.mean(sample); + // Computes the explained variance + expVar = 0; + for (var i = 0; i < args.length; i++) { + expVar = expVar + args[i].length * Math.pow(jStat.mean(args[i]) - sampMean, 2); + } + expVar /= (args.length - 1); + // Computes unexplained variance + unexpVar = 0; + for (var i = 0; i < args.length; i++) { + sampSampMean = jStat.mean(args[i]); + for (j = 0; j < args[i].length; j++) { + unexpVar += Math.pow(args[i][j] - sampSampMean, 2); } } + unexpVar /= (sample.length - args.length); + return expVar / unexpVar; + }, + + // 2 different paramter setups + // (array1, array2, array3, ...) + // (anovafscore, df1, df2) + anovaftest: function anovaftest() { + var args = slice.call(arguments), + df1, df2, n, i; + if (isNumber(args[0])) { + return 1 - jStat.centralF.cdf(args[0], args[1], args[2]); + } + anovafscore = jStat.anovafscore(args); + df1 = args.length - 1; + n = 0; + for (var i = 0; i < args.length; i++) { + n = n + args[i].length; + } + df2 = n - df1 - 1; + return 1 - jStat.centralF.cdf(anovafscore, df1, df2); + }, + + ftest: function ftest(fscore, df1, df2) { + return 1 - jStat.centralF.cdf(fscore, df1, df2); } - return [x, r]; - } + }); - return qr2; - }()), - - lstsq: (function(A, b) { - // solve least squard problem for Ax=b as QR decomposition way if b is - // [[b1],[b2],[b3]] form will return [[x1],[x2],[x3]] array form solution - // else b is [b1,b2,b3] form will return [x1,x2,x3] array form solution - function R_I(A) { - A = jStat.copy(A); - var size = A.length; - var I = jStat.identity(size); - jStat.arange(size - 1, -1, -1).forEach(function(i) { - jStat.sliceAssign( - I, { row: i }, jStat.divide(jStat.slice(I, { row: i }), A[i][i])); - jStat.sliceAssign( - A, { row: i }, jStat.divide(jStat.slice(A, { row: i }), A[i][i])); - jStat.arange(i).forEach(function(j) { - var c = jStat.multiply(A[j][i], -1); - var Aj = jStat.slice(A, { row: j }); - var cAi = jStat.multiply(jStat.slice(A, { row: i }), c); - jStat.sliceAssign(A, { row: j }, jStat.add(Aj, cAi)); - var Ij = jStat.slice(I, { row: j }); - var cIi = jStat.multiply(jStat.slice(I, { row: i }), c); - jStat.sliceAssign(I, { row: j }, jStat.add(Ij, cIi)); - }); - }); - return I; - } + jStat.extend(jStat.fn, { + anovafscore: function anovafscore() { + return jStat.anovafscore(this.toArray()); + }, - function qr_solve(A, b){ - var array_mode = false; - if (b[0].length === undefined) { - // [c1,c2,c3] mode - b = b.map(function(x){ return [x] }); - array_mode = true; + anovaftes: function anovaftes() { + var n = 0; + var i; + for (var i = 0; i < this.length; i++) { + n = n + this[i].length; + } + return jStat.ftest(this.anovafscore(), this.length - 1, n - this.length); } - var QR = jStat.QR(A); - var Q = QR[0]; - var R = QR[1]; - var attrs = A[0].length; - var Q1 = jStat.slice(Q,{col:{end:attrs}}); - var R1 = jStat.slice(R,{row:{end:attrs}}); - var RI = R_I(R1); - var Q2 = jStat.transpose(Q1); - - if(Q2[0].length === undefined){ - Q2 = [Q2]; // The confusing jStat.multifly implementation threat nature process again. - } + }); - var x = jStat.multiply(jStat.multiply(RI, Q2), b); + // Tukey's range test + jStat.extend({ + // 2 parameter lists + // (mean1, mean2, n1, n2, sd) + // (array1, array2, sd) + qscore: function qscore() { + var args = slice.call(arguments); + var mean1, mean2, n1, n2, sd; + if (isNumber(args[0])) { + mean1 = args[0]; + mean2 = args[1]; + n1 = args[2]; + n2 = args[3]; + sd = args[4]; + } else { + mean1 = jStat.mean(args[0]); + mean2 = jStat.mean(args[1]); + n1 = args[0].length; + n2 = args[1].length; + sd = args[2]; + } + return Math.abs(mean1 - mean2) / (sd * Math.sqrt((1 / n1 + 1 / n2) / 2)); + }, + + // 3 different parameter lists: + // (qscore, n, k) + // (mean1, mean2, n1, n2, sd, n, k) + // (array1, array2, sd, n, k) + qtest: function qtest() { + var args = slice.call(arguments); + + var qscore; + if (args.length === 3) { + qscore = args[0]; + args = args.slice(1); + } else if (args.length === 7) { + qscore = jStat.qscore(args[0], args[1], args[2], args[3], args[4]); + args = args.slice(5); + } else { + qscore = jStat.qscore(args[0], args[1], args[2]); + args = args.slice(3); + } - if(x.length === undefined){ - x = [[x]]; // The confusing jStat.multifly implementation threat nature process again. - } + var n = args[0]; + var k = args[1]; + return 1 - jStat.tukey.cdf(qscore, k, n - k); + }, - if (array_mode) - return x.map(function(i){ return i[0] }); - return x; - } + tukeyhsd: function tukeyhsd(arrays) { + var sd = jStat.pooledstdev(arrays); + var means = arrays.map(function (arr) {return jStat.mean(arr);}); + var n = arrays.reduce(function (n, arr) {return n + arr.length;}, 0); - return qr_solve; - }()), - - jacobi: function jacobi(a) { - var condition = 1; - var n = a.length; - var e = jStat.identity(n, n); - var ev = []; - var b, i, j, p, q, maxim, theta, s; - // condition === 1 only if tolerance is not reached - while (condition === 1) { - maxim = a[0][1]; - p = 0; - q = 1; - for (var i = 0; i < n; i++) { - for (j = 0; j < n; j++) { - if (i != j) { - if (maxim < Math.abs(a[i][j])) { - maxim = Math.abs(a[i][j]); - p = i; - q = j; + var results = []; + for (var i = 0; i < arrays.length; ++i) { + for (var j = i + 1; j < arrays.length; ++j) { + var p = jStat.qtest(means[i], means[j], arrays[i].length, arrays[j].length, sd, n, arrays.length); + results.push([[i, j], p]); } - } } + + return results; } - if (a[p][p] === a[q][q]) - theta = (a[p][q] > 0) ? Math.PI / 4 : -Math.PI / 4; - else - theta = Math.atan(2 * a[p][q] / (a[p][p] - a[q][q])) / 2; - s = jStat.identity(n, n); - s[p][p] = Math.cos(theta); - s[p][q] = -Math.sin(theta); - s[q][p] = Math.sin(theta); - s[q][q] = Math.cos(theta); - // eigen vector matrix - e = jStat.multiply(e, s); - b = jStat.multiply(jStat.multiply(jStat.inv(s), a), s); - a = b; - condition = 0; - for (var i = 1; i < n; i++) { - for (j = 1; j < n; j++) { - if (i != j && Math.abs(a[i][j]) > 0.001) { - condition = 1; - } + }); + + // Error Bounds + jStat.extend({ + // 2 different parameter setups + // (value, alpha, sd, n) + // (value, alpha, array) + normalci: function normalci() { + var args = slice.call(arguments), + ans = new Array(2), + change; + if (args.length === 4) { + change = Math.abs(jStat.normal.inv(args[1] / 2, 0, 1) * + args[2] / Math.sqrt(args[3])); + } else { + change = Math.abs(jStat.normal.inv(args[1] / 2, 0, 1) * + jStat.stdev(args[2]) / Math.sqrt(args[2].length)); } - } - } - for (var i = 0; i < n; i++) ev.push(a[i][i]); - //returns both the eigenvalue and eigenmatrix - return [e, ev]; - }, - - rungekutta: function rungekutta(f, h, p, t_j, u_j, order) { - var k1, k2, u_j1, k3, k4; - if (order === 2) { - while (t_j <= p) { - k1 = h * f(t_j, u_j); - k2 = h * f(t_j + h, u_j + k1); - u_j1 = u_j + (k1 + k2) / 2; - u_j = u_j1; - t_j = t_j + h; - } - } - if (order === 4) { - while (t_j <= p) { - k1 = h * f(t_j, u_j); - k2 = h * f(t_j + h / 2, u_j + k1 / 2); - k3 = h * f(t_j + h / 2, u_j + k2 / 2); - k4 = h * f(t_j +h, u_j + k3); - u_j1 = u_j + (k1 + 2 * k2 + 2 * k3 + k4) / 6; - u_j = u_j1; - t_j = t_j + h; - } - } - return u_j; - }, - - romberg: function romberg(f, a, b, order) { - var i = 0; - var h = (b - a) / 2; - var x = []; - var h1 = []; - var g = []; - var m, a1, j, k, I; - while (i < order / 2) { - I = f(a); - for (j = a, k = 0; j <= b; j = j + h, k++) x[k] = j; - m = x.length; - for (j = 1; j < m - 1; j++) { - I += (((j % 2) !== 0) ? 4 : 2) * f(x[j]); - } - I = (h / 3) * (I + f(b)); - g[i] = I; - h /= 2; - i++; - } - a1 = g.length; - m = 1; - while (a1 !== 1) { - for (j = 0; j < a1 - 1; j++) - h1[j] = ((Math.pow(4, m)) * g[j + 1] - g[j]) / (Math.pow(4, m) - 1); - a1 = h1.length; - g = h1; - h1 = []; - m++; - } - return g; - }, + ans[0] = args[0] - change; + ans[1] = args[0] + change; + return ans; + }, + + // 2 different parameter setups + // (value, alpha, sd, n) + // (value, alpha, array) + tci: function tci() { + var args = slice.call(arguments), + ans = new Array(2), + change; + if (args.length === 4) { + change = Math.abs(jStat.studentt.inv(args[1] / 2, args[3] - 1) * + args[2] / Math.sqrt(args[3])); + } else { + change = Math.abs(jStat.studentt.inv(args[1] / 2, args[2].length - 1) * + jStat.stdev(args[2], true) / Math.sqrt(args[2].length)); + } + ans[0] = args[0] - change; + ans[1] = args[0] + change; + return ans; + }, - richardson: function richardson(X, f, x, h) { - function pos(X, x) { - var i = 0; - var n = X.length; - var p; - for (; i < n; i++) - if (X[i] === x) p = i; - return p; - } - var n = X.length, - h_min = Math.abs(x - X[pos(X, x) + 1]), - i = 0, - g = [], - h1 = [], - y1, y2, m, a, j; - while (h >= h_min) { - y1 = pos(X, x + h); - y2 = pos(X, x); - g[i] = (f[y1] - 2 * f[y2] + f[2 * y2 - y1]) / (h * h); - h /= 2; - i++; - } - a = g.length; - m = 1; - while (a != 1) { - for (j = 0; j < a - 1; j++) - h1[j] = ((Math.pow(4, m)) * g[j + 1] - g[j]) / (Math.pow(4, m) - 1); - a = h1.length; - g = h1; - h1 = []; - m++; - } - return g; - }, - - simpson: function simpson(f, a, b, n) { - var h = (b - a) / n; - var I = f(a); - var x = []; - var j = a; - var k = 0; - var i = 1; - var m; - for (; j <= b; j = j + h, k++) - x[k] = j; - m = x.length; - for (; i < m - 1; i++) { - I += ((i % 2 !== 0) ? 4 : 2) * f(x[i]); - } - return (h / 3) * (I + f(b)); - }, - - hermite: function hermite(X, F, dF, value) { - var n = X.length; - var p = 0; - var i = 0; - var l = []; - var dl = []; - var A = []; - var B = []; - var j; - for (; i < n; i++) { - l[i] = 1; - for (j = 0; j < n; j++) { - if (i != j) l[i] *= (value - X[j]) / (X[i] - X[j]); - } - dl[i] = 0; - for (j = 0; j < n; j++) { - if (i != j) dl[i] += 1 / (X [i] - X[j]); - } - A[i] = (1 - 2 * (value - X[i]) * dl[i]) * (l[i] * l[i]); - B[i] = (value - X[i]) * (l[i] * l[i]); - p += (A[i] * F[i] + B[i] * dF[i]); - } - return p; - }, - - lagrange: function lagrange(X, F, value) { - var p = 0; - var i = 0; - var j, l; - var n = X.length; - for (; i < n; i++) { - l = F[i]; - for (j = 0; j < n; j++) { - // calculating the lagrange polynomial L_i - if (i != j) l *= (value - X[j]) / (X[i] - X[j]); + significant: function significant(pvalue, alpha) { + return pvalue < alpha; } - // adding the lagrange polynomials found above - p += l; - } - return p; - }, - - cubic_spline: function cubic_spline(X, F, value) { - var n = X.length; - var i = 0, j; - var A = []; - var B = []; - var alpha = []; - var c = []; - var h = []; - var b = []; - var d = []; - for (; i < n - 1; i++) - h[i] = X[i + 1] - X[i]; - alpha[0] = 0; - for (var i = 1; i < n - 1; i++) { - alpha[i] = (3 / h[i]) * (F[i + 1] - F[i]) - - (3 / h[i-1]) * (F[i] - F[i-1]); - } - for (var i = 1; i < n - 1; i++) { - A[i] = []; - B[i] = []; - A[i][i-1] = h[i-1]; - A[i][i] = 2 * (h[i - 1] + h[i]); - A[i][i+1] = h[i]; - B[i][0] = alpha[i]; - } - c = jStat.multiply(jStat.inv(A), B); - for (j = 0; j < n - 1; j++) { - b[j] = (F[j + 1] - F[j]) / h[j] - h[j] * (c[j + 1][0] + 2 * c[j][0]) / 3; - d[j] = (c[j + 1][0] - c[j][0]) / (3 * h[j]); - } - for (j = 0; j < n; j++) { - if (X[j] > value) break; - } - j -= 1; - return F[j] + (value - X[j]) * b[j] + jStat.sq(value-X[j]) * - c[j] + (value - X[j]) * jStat.sq(value - X[j]) * d[j]; - }, - - gauss_quadrature: function gauss_quadrature() { - throw new Error('gauss_quadrature not yet implemented'); - }, - - PCA: function PCA(X) { - var m = X.length; - var n = X[0].length; - var i = 0; - var j, temp1; - var u = []; - var D = []; - var result = []; - var temp2 = []; - var Y = []; - var Bt = []; - var B = []; - var C = []; - var V = []; - var Vt = []; - for (var i = 0; i < m; i++) { - u[i] = jStat.sum(X[i]) / n; - } - for (var i = 0; i < n; i++) { - B[i] = []; - for(j = 0; j < m; j++) { - B[i][j] = X[j][i] - u[j]; + }); + + jStat.extend(jStat.fn, { + normalci: function normalci(value, alpha) { + return jStat.normalci(value, alpha, this.toArray()); + }, + + tci: function tci(value, alpha) { + return jStat.tci(value, alpha, this.toArray()); } - } - B = jStat.transpose(B); - for (var i = 0; i < m; i++) { - C[i] = []; - for (j = 0; j < m; j++) { - C[i][j] = (jStat.dot([B[i]], [B[j]])) / (n - 1); + }); + + // internal method for calculating the z-score for a difference of proportions test + function differenceOfProportions(p1, n1, p2, n2) { + if (p1 > 1 || p2 > 1 || p1 <= 0 || p2 <= 0) { + throw new Error("Proportions should be greater than 0 and less than 1") } + var pooled = (p1 * n1 + p2 * n2) / (n1 + n2); + var se = Math.sqrt(pooled * (1 - pooled) * ((1/n1) + (1/n2))); + return (p1 - p2) / se; } - result = jStat.jacobi(C); - V = result[0]; - D = result[1]; - Vt = jStat.transpose(V); - for (var i = 0; i < D.length; i++) { - for (j = i; j < D.length; j++) { - if(D[i] < D[j]) { - temp1 = D[i]; - D[i] = D[j]; - D[j] = temp1; - temp2 = Vt[i]; - Vt[i] = Vt[j]; - Vt[j] = temp2; - } + + // Difference of Proportions + jStat.extend(jStat.fn, { + oneSidedDifferenceOfProportions: function oneSidedDifferenceOfProportions(p1, n1, p2, n2) { + var z = differenceOfProportions(p1, n1, p2, n2); + return jStat.ztest(z, 1); + }, + + twoSidedDifferenceOfProportions: function twoSidedDifferenceOfProportions(p1, n1, p2, n2) { + var z = differenceOfProportions(p1, n1, p2, n2); + return jStat.ztest(z, 2); } - } - Bt = jStat.transpose(B); - for (var i = 0; i < m; i++) { - Y[i] = []; - for (j = 0; j < Bt.length; j++) { - Y[i][j] = jStat.dot([Vt[i]], [Bt[j]]); + }); + + }(jStat, Math)); + jStat.models = (function(){ + + function sub_regress(endog, exog) { + return ols(endog, exog); } - } - return [X, D, Vt, Y]; - } -}); - -// extend jStat.fn with methods that require one argument -(function(funcs) { - for (var i = 0; i < funcs.length; i++) (function(passfunc) { - jStat.fn[passfunc] = function(arg, func) { - var tmpthis = this; - // check for callback - if (func) { - setTimeout(function() { - func.call(tmpthis, jStat.fn[passfunc].call(tmpthis, arg)); - }, 15); - return this; + + function sub_regress(exog) { + var var_count = exog[0].length; + var modelList = jStat.arange(var_count).map(function(endog_index) { + var exog_index = + jStat.arange(var_count).filter(function(i){return i!==endog_index}); + return ols(jStat.col(exog, endog_index).map(function(x){ return x[0] }), + jStat.col(exog, exog_index)) + }); + return modelList; } - if (typeof jStat[passfunc](this, arg) === 'number') - return jStat[passfunc](this, arg); - else - return jStat(jStat[passfunc](this, arg)); - }; - }(funcs[i])); -}('add divide multiply subtract dot pow exp log abs norm angle'.split(' '))); - -}(jStat, Math)); -(function(jStat, Math) { - -var slice = [].slice; -var isNumber = jStat.utils.isNumber; -var isArray = jStat.utils.isArray; - -// flag==true denotes use of sample standard deviation -// Z Statistics -jStat.extend({ - // 2 different parameter lists: - // (value, mean, sd) - // (value, array, flag) - zscore: function zscore() { - var args = slice.call(arguments); - if (isNumber(args[1])) { - return (args[0] - args[1]) / args[2]; - } - return (args[0] - jStat.mean(args[1])) / jStat.stdev(args[1], args[2]); - }, - - // 3 different paramter lists: - // (value, mean, sd, sides) - // (zscore, sides) - // (value, array, sides, flag) - ztest: function ztest() { - var args = slice.call(arguments); - var z; - if (isArray(args[1])) { - // (value, array, sides, flag) - z = jStat.zscore(args[0],args[1],args[3]); - return (args[2] === 1) ? - (jStat.normal.cdf(-Math.abs(z), 0, 1)) : - (jStat.normal.cdf(-Math.abs(z), 0, 1)*2); - } else { - if (args.length > 2) { - // (value, mean, sd, sides) - z = jStat.zscore(args[0],args[1],args[2]); - return (args[3] === 1) ? - (jStat.normal.cdf(-Math.abs(z),0,1)) : - (jStat.normal.cdf(-Math.abs(z),0,1)* 2); - } else { - // (zscore, sides) - z = args[0]; - return (args[1] === 1) ? - (jStat.normal.cdf(-Math.abs(z),0,1)) : - (jStat.normal.cdf(-Math.abs(z),0,1)*2); + + // do OLS model regress + // exog have include const columns ,it will not generate it .In fact, exog is + // "design matrix" look at + //https://en.wikipedia.org/wiki/Design_matrix + function ols(endog, exog) { + var nobs = endog.length; + var df_model = exog[0].length - 1; + var df_resid = nobs-df_model - 1; + var coef = jStat.lstsq(exog, endog); + var predict = + jStat.multiply(exog, coef.map(function(x) { return [x] })) + .map(function(p) { return p[0] }); + var resid = jStat.subtract(endog, predict); + var ybar = jStat.mean(endog); + // constant cause problem + // var SST = jStat.sum(endog.map(function(y) { + // return Math.pow(y-ybar,2); + // })); + var SSE = jStat.sum(predict.map(function(f) { + return Math.pow(f - ybar, 2); + })); + var SSR = jStat.sum(endog.map(function(y, i) { + return Math.pow(y - predict[i], 2); + })); + var SST = SSE + SSR; + var R2 = (SSE / SST); + return { + exog:exog, + endog:endog, + nobs:nobs, + df_model:df_model, + df_resid:df_resid, + coef:coef, + predict:predict, + resid:resid, + ybar:ybar, + SST:SST, + SSE:SSE, + SSR:SSR, + R2:R2 + }; } - } - } -}); - -jStat.extend(jStat.fn, { - zscore: function zscore(value, flag) { - return (value - this.mean()) / this.stdev(flag); - }, - - ztest: function ztest(value, sides, flag) { - var zscore = Math.abs(this.zscore(value, flag)); - return (sides === 1) ? - (jStat.normal.cdf(-zscore, 0, 1)) : - (jStat.normal.cdf(-zscore, 0, 1) * 2); - } -}); - -// T Statistics -jStat.extend({ - // 2 parameter lists - // (value, mean, sd, n) - // (value, array) - tscore: function tscore() { - var args = slice.call(arguments); - return (args.length === 4) ? - ((args[0] - args[1]) / (args[2] / Math.sqrt(args[3]))) : - ((args[0] - jStat.mean(args[1])) / - (jStat.stdev(args[1], true) / Math.sqrt(args[1].length))); - }, - - // 3 different paramter lists: - // (value, mean, sd, n, sides) - // (tscore, n, sides) - // (value, array, sides) - ttest: function ttest() { - var args = slice.call(arguments); - var tscore; - if (args.length === 5) { - tscore = Math.abs(jStat.tscore(args[0], args[1], args[2], args[3])); - return (args[4] === 1) ? - (jStat.studentt.cdf(-tscore, args[3]-1)) : - (jStat.studentt.cdf(-tscore, args[3]-1)*2); - } - if (isNumber(args[1])) { - tscore = Math.abs(args[0]); - return (args[2] == 1) ? - (jStat.studentt.cdf(-tscore, args[1]-1)) : - (jStat.studentt.cdf(-tscore, args[1]-1) * 2); - } - tscore = Math.abs(jStat.tscore(args[0], args[1])); - return (args[2] == 1) ? - (jStat.studentt.cdf(-tscore, args[1].length-1)) : - (jStat.studentt.cdf(-tscore, args[1].length-1) * 2); - } -}); - -jStat.extend(jStat.fn, { - tscore: function tscore(value) { - return (value - this.mean()) / (this.stdev(true) / Math.sqrt(this.cols())); - }, - - ttest: function ttest(value, sides) { - return (sides === 1) ? - (1 - jStat.studentt.cdf(Math.abs(this.tscore(value)), this.cols()-1)) : - (jStat.studentt.cdf(-Math.abs(this.tscore(value)), this.cols()-1)*2); - } -}); - -// F Statistics -jStat.extend({ - // Paramter list is as follows: - // (array1, array2, array3, ...) - // or it is an array of arrays - // array of arrays conversion - anovafscore: function anovafscore() { - var args = slice.call(arguments), - expVar, sample, sampMean, sampSampMean, tmpargs, unexpVar, i, j; - if (args.length === 1) { - tmpargs = new Array(args[0].length); - for (var i = 0; i < args[0].length; i++) { - tmpargs[i] = args[0][i]; + + // H0: b_I=0 + // H1: b_I!=0 + function t_test(model) { + var subModelList = sub_regress(model.exog); + //var sigmaHat=jStat.stdev(model.resid); + var sigmaHat = Math.sqrt(model.SSR / (model.df_resid)); + var seBetaHat = subModelList.map(function(mod) { + var SST = mod.SST; + var R2 = mod.R2; + return sigmaHat / Math.sqrt(SST * (1 - R2)); + }); + var tStatistic = model.coef.map(function(coef, i) { + return (coef - 0) / seBetaHat[i]; + }); + var pValue = tStatistic.map(function(t) { + var leftppf = jStat.studentt.cdf(t, model.df_resid); + return (leftppf > 0.5 ? 1 - leftppf : leftppf) * 2; + }); + var c = jStat.studentt.inv(0.975, model.df_resid); + var interval95 = model.coef.map(function(coef, i) { + var d = c * seBetaHat[i]; + return [coef - d, coef + d]; + }); + return { + se: seBetaHat, + t: tStatistic, + p: pValue, + sigmaHat: sigmaHat, + interval95: interval95 + }; } - args = tmpargs; - } - // 2 sample case - if (args.length === 2) { - return jStat.variance(args[0]) / jStat.variance(args[1]); - } - // Builds sample array - sample = new Array(); - for (var i = 0; i < args.length; i++) { - sample = sample.concat(args[i]); - } - sampMean = jStat.mean(sample); - // Computes the explained variance - expVar = 0; - for (var i = 0; i < args.length; i++) { - expVar = expVar + args[i].length * Math.pow(jStat.mean(args[i]) - sampMean, 2); - } - expVar /= (args.length - 1); - // Computes unexplained variance - unexpVar = 0; - for (var i = 0; i < args.length; i++) { - sampSampMean = jStat.mean(args[i]); - for (j = 0; j < args[i].length; j++) { - unexpVar += Math.pow(args[i][j] - sampSampMean, 2); + + function F_test(model) { + var F_statistic = + (model.R2 / model.df_model) / ((1 - model.R2) / model.df_resid); + var fcdf = function(x, n1, n2) { + return jStat.beta.cdf(x / (n2 / n1 + x), n1 / 2, n2 / 2) + }; + var pvalue = 1 - fcdf(F_statistic, model.df_model, model.df_resid); + return { F_statistic: F_statistic, pvalue: pvalue }; } - } - unexpVar /= (sample.length - args.length); - return expVar / unexpVar; - }, - - // 2 different paramter setups - // (array1, array2, array3, ...) - // (anovafscore, df1, df2) - anovaftest: function anovaftest() { - var args = slice.call(arguments), - df1, df2, n, i; - if (isNumber(args[0])) { - return 1 - jStat.centralF.cdf(args[0], args[1], args[2]); - } - anovafscore = jStat.anovafscore(args); - df1 = args.length - 1; - n = 0; - for (var i = 0; i < args.length; i++) { - n = n + args[i].length; - } - df2 = n - df1 - 1; - return 1 - jStat.centralF.cdf(anovafscore, df1, df2); - }, - - ftest: function ftest(fscore, df1, df2) { - return 1 - jStat.centralF.cdf(fscore, df1, df2); - } -}); - -jStat.extend(jStat.fn, { - anovafscore: function anovafscore() { - return jStat.anovafscore(this.toArray()); - }, - - anovaftes: function anovaftes() { - var n = 0; - var i; - for (var i = 0; i < this.length; i++) { - n = n + this[i].length; - } - return jStat.ftest(this.anovafscore(), this.length - 1, n - this.length); - } -}); - -// Tukey's range test -jStat.extend({ - // 2 parameter lists - // (mean1, mean2, n1, n2, sd) - // (array1, array2, sd) - qscore: function qscore() { - var args = slice.call(arguments); - var mean1, mean2, n1, n2, sd; - if (isNumber(args[0])) { - mean1 = args[0]; - mean2 = args[1]; - n1 = args[2]; - n2 = args[3]; - sd = args[4]; - } else { - mean1 = jStat.mean(args[0]); - mean2 = jStat.mean(args[1]); - n1 = args[0].length; - n2 = args[1].length; - sd = args[2]; - } - return Math.abs(mean1 - mean2) / (sd * Math.sqrt((1 / n1 + 1 / n2) / 2)); - }, - - // 3 different parameter lists: - // (qscore, n, k) - // (mean1, mean2, n1, n2, sd, n, k) - // (array1, array2, sd, n, k) - qtest: function qtest() { - var args = slice.call(arguments); - - var qscore; - if (args.length === 3) { - qscore = args[0]; - args = args.slice(1); - } else if (args.length === 7) { - qscore = jStat.qscore(args[0], args[1], args[2], args[3], args[4]); - args = args.slice(5); - } else { - qscore = jStat.qscore(args[0], args[1], args[2]); - args = args.slice(3); - } - var n = args[0]; - var k = args[1]; + function ols_wrap(endog, exog) { + var model = ols(endog,exog); + var ttest = t_test(model); + var ftest = F_test(model); + // Provide the Wherry / Ezekiel / McNemar / Cohen Adjusted R^2 + // Which matches the 'adjusted R^2' provided by R's lm package + var adjust_R2 = + 1 - (1 - model.R2) * ((model.nobs - 1) / (model.df_resid)); + model.t = ttest; + model.f = ftest; + model.adjust_R2 = adjust_R2; + return model; + } - return 1 - jStat.tukey.cdf(qscore, k, n - k); - }, + return { ols: ols_wrap }; + })(); + // Make it compatible with previous version. + jStat.jStat = jStat; - tukeyhsd: function tukeyhsd(arrays) { - var sd = jStat.pooledstdev(arrays); - var means = arrays.map(function (arr) {return jStat.mean(arr);}); - var n = arrays.reduce(function (n, arr) {return n + arr.length;}, 0); + return jStat; + }); + }); - var results = []; - for (var i = 0; i < arrays.length; ++i) { - for (var j = i + 1; j < arrays.length; ++j) { - var p = jStat.qtest(means[i], means[j], arrays[i].length, arrays[j].length, sd, n, arrays.length); - results.push([[i, j], p]); + function get_alpha_sidaks_correction(alpha, variants) { + if (variants == 1) { + return alpha; } - } - return results; - } -}); - -// Error Bounds -jStat.extend({ - // 2 different parameter setups - // (value, alpha, sd, n) - // (value, alpha, array) - normalci: function normalci() { - var args = slice.call(arguments), - ans = new Array(2), - change; - if (args.length === 4) { - change = Math.abs(jStat.normal.inv(args[1] / 2, 0, 1) * - args[2] / Math.sqrt(args[3])); - } else { - change = Math.abs(jStat.normal.inv(args[1] / 2, 0, 1) * - jStat.stdev(args[2]) / Math.sqrt(args[2].length)); + return 1 - (Math.pow(1-alpha, 1/variants)); } - ans[0] = args[0] - change; - ans[1] = args[0] + change; - return ans; - }, - - // 2 different parameter setups - // (value, alpha, sd, n) - // (value, alpha, array) - tci: function tci() { - var args = slice.call(arguments), - ans = new Array(2), - change; - if (args.length === 4) { - change = Math.abs(jStat.studentt.inv(args[1] / 2, args[3] - 1) * - args[2] / Math.sqrt(args[3])); - } else { - change = Math.abs(jStat.studentt.inv(args[1] / 2, args[2].length - 1) * - jStat.stdev(args[2], true) / Math.sqrt(args[2].length)); + + // SOLVING FOR POWER + function solveforpower_Gtest(data) { + var { base_rate, effect_size } = data; + var mean_var = base_rate*(1+effect_size); + data.variance = base_rate*(1-base_rate) + mean_var*(1-mean_var); + + return solve_for_power(data); } - ans[0] = args[0] - change; - ans[1] = args[0] + change; - return ans; - }, - - significant: function significant(pvalue, alpha) { - return pvalue < alpha; - } -}); - -jStat.extend(jStat.fn, { - normalci: function normalci(value, alpha) { - return jStat.normalci(value, alpha, this.toArray()); - }, - - tci: function tci(value, alpha) { - return jStat.tci(value, alpha, this.toArray()); - } -}); - -// internal method for calculating the z-score for a difference of proportions test -function differenceOfProportions(p1, n1, p2, n2) { - if (p1 > 1 || p2 > 1 || p1 <= 0 || p2 <= 0) { - throw new Error("Proportions should be greater than 0 and less than 1") - } - var pooled = (p1 * n1 + p2 * n2) / (n1 + n2); - var se = Math.sqrt(pooled * (1 - pooled) * ((1/n1) + (1/n2))); - return (p1 - p2) / se; -} - -// Difference of Proportions -jStat.extend(jStat.fn, { - oneSidedDifferenceOfProportions: function oneSidedDifferenceOfProportions(p1, n1, p2, n2) { - var z = differenceOfProportions(p1, n1, p2, n2); - return jStat.ztest(z, 1); - }, - - twoSidedDifferenceOfProportions: function twoSidedDifferenceOfProportions(p1, n1, p2, n2) { - var z = differenceOfProportions(p1, n1, p2, n2); - return jStat.ztest(z, 2); - } -}); - -}(jStat, Math)); -jStat.models = (function(){ - - function sub_regress(endog, exog) { - return ols(endog, exog); - } - - function sub_regress(exog) { - var var_count = exog[0].length; - var modelList = jStat.arange(var_count).map(function(endog_index) { - var exog_index = - jStat.arange(var_count).filter(function(i){return i!==endog_index}); - return ols(jStat.col(exog, endog_index).map(function(x){ return x[0] }), - jStat.col(exog, exog_index)) - }); - return modelList; - } - - // do OLS model regress - // exog have include const columns ,it will not generate it .In fact, exog is - // "design matrix" look at - //https://en.wikipedia.org/wiki/Design_matrix - function ols(endog, exog) { - var nobs = endog.length; - var df_model = exog[0].length - 1; - var df_resid = nobs-df_model - 1; - var coef = jStat.lstsq(exog, endog); - var predict = - jStat.multiply(exog, coef.map(function(x) { return [x] })) - .map(function(p) { return p[0] }); - var resid = jStat.subtract(endog, predict); - var ybar = jStat.mean(endog); - // constant cause problem - // var SST = jStat.sum(endog.map(function(y) { - // return Math.pow(y-ybar,2); - // })); - var SSE = jStat.sum(predict.map(function(f) { - return Math.pow(f - ybar, 2); - })); - var SSR = jStat.sum(endog.map(function(y, i) { - return Math.pow(y - predict[i], 2); - })); - var SST = SSE + SSR; - var R2 = (SSE / SST); - return { - exog:exog, - endog:endog, - nobs:nobs, - df_model:df_model, - df_resid:df_resid, - coef:coef, - predict:predict, - resid:resid, - ybar:ybar, - SST:SST, - SSE:SSE, - SSR:SSR, - R2:R2 - }; - } - - // H0: b_I=0 - // H1: b_I!=0 - function t_test(model) { - var subModelList = sub_regress(model.exog); - //var sigmaHat=jStat.stdev(model.resid); - var sigmaHat = Math.sqrt(model.SSR / (model.df_resid)); - var seBetaHat = subModelList.map(function(mod) { - var SST = mod.SST; - var R2 = mod.R2; - return sigmaHat / Math.sqrt(SST * (1 - R2)); - }); - var tStatistic = model.coef.map(function(coef, i) { - return (coef - 0) / seBetaHat[i]; - }); - var pValue = tStatistic.map(function(t) { - var leftppf = jStat.studentt.cdf(t, model.df_resid); - return (leftppf > 0.5 ? 1 - leftppf : leftppf) * 2; - }); - var c = jStat.studentt.inv(0.975, model.df_resid); - var interval95 = model.coef.map(function(coef, i) { - var d = c * seBetaHat[i]; - return [coef - d, coef + d]; - }); - return { - se: seBetaHat, - t: tStatistic, - p: pValue, - sigmaHat: sigmaHat, - interval95: interval95 - }; - } - function F_test(model) { - var F_statistic = - (model.R2 / model.df_model) / ((1 - model.R2) / model.df_resid); - var fcdf = function(x, n1, n2) { - return jStat.beta.cdf(x / (n2 / n1 + x), n1 / 2, n2 / 2) - }; - var pvalue = 1 - fcdf(F_statistic, model.df_model, model.df_resid); - return { F_statistic: F_statistic, pvalue: pvalue }; - } - - function ols_wrap(endog, exog) { - var model = ols(endog,exog); - var ttest = t_test(model); - var ftest = F_test(model); - // Provide the Wherry / Ezekiel / McNemar / Cohen Adjusted R^2 - // Which matches the 'adjusted R^2' provided by R's lm package - var adjust_R2 = - 1 - (1 - model.R2) * ((model.nobs - 1) / (model.df_resid)); - model.t = ttest; - model.f = ftest; - model.adjust_R2 = adjust_R2; - return model; - } - - return { ols: ols_wrap }; -})(); - // Make it compatible with previous version. - jStat.jStat = jStat; - - return jStat; -}); -}); - -// SOLVING FOR POWER -function solveforpower_Gtest ({total_sample_size, base_rate, effect_size, alpha, alternative, mu}) { - var sample_size = total_sample_size/2; - - var mean_base = base_rate; - var mean_var = base_rate * (1+effect_size); - - var mean_diff = mean_var - mean_base; - var delta = mean_diff - mu; - - var variance = mean_base * (1-mean_base) + mean_var * (1-mean_var); - var z = jstat.normal.inv(1-alpha/2, 0, 1); - var mean = delta*Math.sqrt(sample_size/variance); - - var power; - if (alternative == 'lower') { - power = jstat.normal.cdf(jstat.normal.inv(alpha, 0, 1), mean, 1); - } else if (alternative == 'greater') { - power = 1-jstat.normal.cdf(jstat.normal.inv(1-alpha, 0, 1), mean, 1); - } else { - power = 1 - (jstat.normal.cdf(z, mean, 1) - - jstat.normal.cdf(-z, mean, 1)); + function solveforpower_Ttest(data) { + var { sd_rate } = data; + data.variance = 2*sd_rate**2; + + return solve_for_power(data); } - return power -} + function solve_for_power(data) { + var {total_sample_size, base_rate, variance, effect_size, alpha, variants, alternative, mu} = data; + var sample_size = total_sample_size / (1 + variants); -function solveforpower_Ttest({total_sample_size, base_rate, sd_rate, effect_size, alpha, alternative, mu}) { - var sample_size = total_sample_size/2; + var mean_base = base_rate; + var mean_var = base_rate * (1+effect_size); - var mean_base = base_rate; - var mean_var = base_rate * (1+effect_size); + var mean_diff = mean_var - mean_base; + var delta = mean_diff - mu; - var mean_diff = mean_var - mean_base; - var delta = mean_diff - mu; + var z = jstat.normal.inv(1-alpha/2, 0, 1); + var mean = delta*Math.sqrt(sample_size/variance); - var variance = 2*sd_rate**2; - var z = jstat.normal.inv(1-alpha/2, 0, 1); - var mean = delta*Math.sqrt(sample_size/variance); + var power; + if (alternative == 'lower') { + power = jstat.normal.cdf(jstat.normal.inv(alpha, 0, 1), mean, 1); + } else if (alternative == 'greater') { + power = 1-jstat.normal.cdf(jstat.normal.inv(1-alpha, 0, 1), mean, 1); + } else { + power = 1 - (jstat.normal.cdf(z, mean, 1) - jstat.normal.cdf(-z, mean, 1)); + } - var power; - if (alternative == 'lower') { - power = jstat.normal.cdf(jstat.normal.inv(alpha, 0, 1), mean, 1); - } else if (alternative == 'greater') { - power = 1-jstat.normal.cdf(jstat.normal.inv(1-alpha, 0, 1), mean, 1); - } else { - power = 1 - (jstat.normal.cdf(z, mean, 1) - - jstat.normal.cdf(-z, mean, 1)); - } + return power; + } - return power -} + function is_valid_input(data) { + var { base_rate, effect_size, alternative, opts, mu } = data; + var change = effect_size*base_rate; + if (typeof(mu) != 'undefined') { + if (alternative == 'greater' && mu >= change) { + return false; + } + if (alternative == 'lower' && mu <= change) { + return false; + } + } -function is_valid_input(data) { - var { base_rate, effect_size, alternative, opts, mu } = data; - var change = effect_size*base_rate; - if (typeof(mu) != 'undefined') { - if (alternative == 'greater' && mu >= change) { - return false; - } - if (alternative == 'lower' && mu <= change) { - return false; - } - } + if (opts && opts.type == 'relative') { + if (alternative == 'greater' && opts.threshold >= effect_size) { + return false; + } + if (alternative == 'lower' && opts.threshold <= effect_size) { + return false; + } + } - if (opts && opts.type == 'relative') { - if (alternative == 'greater' && opts.threshold >= effect_size) { - return false; - } - if (alternative == 'lower' && opts.threshold <= effect_size) { - return false; - } - } + if (opts && opts.type == 'absolutePerDay' && opts.calculating == 'days') { + if (alternative == 'greater' && opts.threshold/opts.visitors_per_day >= change) { + return false; + } + if (alternative == 'lower' && opts.threshold/opts.visitors_per_day <= change) { + return false; + } + } - if (opts && opts.type == 'absolutePerDay' && opts.calculating == 'days') { - if (alternative == 'greater' && opts.threshold/opts.visitors_per_day >= change) { - return false; + return true; + } + + // SOLVING FOR SAMPLE SIZE + function solve_quadratic_for_sample({mean_diff, Z, days, threshold, variance}) { + var a = mean_diff; + if (a == 0) { + return threshold*Math.sqrt(days)/(2*Math.sqrt(variance)*Z) } - if (alternative == 'lower' && opts.threshold/opts.visitors_per_day <= change) { - return false; + + var b = Math.sqrt(variance)*Z/Math.sqrt(days); + var c = -threshold/2; + + var det = b**2 - 4*a*c; + if (det < 0) { + return NaN; } - } - return true; -} + var sol_h = (-b + Math.sqrt(det)) / (2*a); + var sol_l = (-b - Math.sqrt(det)) / (2*a); + + return sol_h >= 0 ? sol_h : sol_l; + } -// SOLVING FOR SAMPLE SIZE -function solve_quadratic_for_sample({mean_diff, Z, days, threshold, variance}) { - var a = mean_diff; - if (a == 0) { - return threshold*Math.sqrt(days)/(2*Math.sqrt(variance)*Z) + function solveforsample_Ttest(data){ + var { sd_rate } = data; + data.variance = 2*sd_rate**2; + return sample_size_calculation(data); } - var b = Math.sqrt(variance)*Z/Math.sqrt(days); - var c = -threshold/2; + function solveforsample_Gtest(data){ + var { base_rate, effect_size } = data; + var mean_var = base_rate*(1+effect_size); + data.variance = base_rate*(1-base_rate) + mean_var*(1-mean_var); - var det = b**2 - 4*a*c; - if (det < 0) { - return NaN; + return sample_size_calculation(data); } - var sol_h = (-b + Math.sqrt(det)) / (2*a); - var sol_l = (-b - Math.sqrt(det)) / (2*a); + function sample_size_calculation(data) { + var { base_rate, variance, effect_size, alpha, beta, variants, alternative, mu, opts } = data; - return sol_h >= 0 ? sol_h : sol_l; -} + if (!is_valid_input(data)) { + return NaN; + } -function solveforsample_Ttest(data){ - var { base_rate, sd_rate, effect_size, alpha, beta, alternative, mu, opts } = data; - if (!is_valid_input(data)) { - return NaN; - } - var mean_base = base_rate; - var mean_var = base_rate * (1+effect_size); - - var variance = 2*sd_rate**2; - var mean_diff = mean_var - mean_base; - - var multiplier; - var sample_one_group; - if (opts && opts.type == 'absolutePerDay') { - if (opts.calculating == 'visitorsPerDay') { - var Z; - if (alternative == "greater") { - Z = jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1); - } else if (alternative == "lower") { - Z = jstat.normal.inv(1-beta, 0, 1) - jstat.normal.inv(alpha, 0, 1); + var mean_base = base_rate; + var mean_var = base_rate*(1+effect_size); + var mean_diff = mean_var - mean_base; + + var multiplier; + var sample_one_group; + if (opts && opts.type == 'absolutePerDay') { + if (opts.calculating == 'visitorsPerDay') { + var Z; + if (alternative == "greater") { + Z = jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1); + } else if (alternative == "lower") { + Z = jstat.normal.inv(1-beta, 0, 1) - jstat.normal.inv(alpha, 0, 1); + } else { + Z = jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1); + } + var sqrt_visitors_per_day = solve_quadratic_for_sample({mean_diff: mean_diff, Z: Z, + days: opts.days, threshold: opts.threshold, variance: variance}); + sample_one_group = opts.days*sqrt_visitors_per_day**2; } else { - Z = jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1); + multiplier = variance/(mean_diff*Math.sqrt(opts.visitors_per_day/2) - opts.threshold/(Math.sqrt(2*opts.visitors_per_day)))**2; + var days; + if (alternative == "greater" || alternative == "lower") { + days = multiplier * (jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1))**2; + } else { + days = multiplier * (jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1))**2; + } + sample_one_group = days*opts.visitors_per_day/2; } - var sqrt_visitors_per_day = solve_quadratic_for_sample({mean_diff: mean_diff, Z: Z, - days: opts.days, threshold: opts.threshold, variance: variance}); - sample_one_group = opts.days*sqrt_visitors_per_day**2; } else { - multiplier = variance/(mean_diff*Math.sqrt(opts.visitors_per_day/2) - opts.threshold/(Math.sqrt(2*opts.visitors_per_day)))**2; - var days; + multiplier = variance/(mu - mean_diff)**2; + if (alternative == "greater" || alternative == "lower") { - days = multiplier * (jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1))**2; + sample_one_group = multiplier * (jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1))**2; } else { - days = multiplier * (jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1))**2; + sample_one_group = multiplier * (jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1))**2; } - sample_one_group = days*opts.visitors_per_day/2; } - } else { - multiplier = variance/(mu - mean_diff)**2; - if (alternative == "greater" || alternative == "lower") { - sample_one_group = multiplier * (jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1))**2; + return (1+variants)*Math.ceil(sample_one_group); + } + + + + // SOLVING FOR EFFECT SIZE + function solveforeffectsize_Ttest({total_sample_size, base_rate, sd_rate, alpha, beta, variants, alternative, mu}){ + var sample_size = total_sample_size / (1 + variants); + var variance = 2*sd_rate**2; + + var z = jstat.normal.inv(1-beta, 0, 1); + var multiplier = Math.sqrt(variance/sample_size); + var effect_size; + if (alternative == "greater") { + effect_size = mu + (z - jstat.normal.inv(alpha, 0, 1)) * multiplier; + } else if (alternative == "lower") { + effect_size = mu - (z - jstat.normal.inv(alpha, 0, 1)) * multiplier; } else { - sample_one_group = multiplier * (jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1))**2; + var delta = (z + jstat.normal.inv(1-alpha/2, 0, 1) )* multiplier; + effect_size = mu + delta; } + + return effect_size/base_rate; } - return 2*Math.ceil(sample_one_group); -} + function solve_quadratic(Z, sample_size, control_rate, mu) { + var a = (Z**2 + sample_size) * control_rate**2; + var b = -(Z**2) * control_rate - 2 * (control_rate + mu) * sample_size * control_rate; + var c = sample_size * (control_rate + mu)**2 - Z**2 * control_rate * (1-control_rate); -function solveforsample_Gtest(data){ - var { base_rate, effect_size, alpha, beta, alternative, mu, opts } = data; - if (!is_valid_input(data)) { - return NaN; + var det = b**2 - 4*a*c; + if (det < 0) { + return [NaN, NaN]; + } + + var sol_h = (-b + Math.sqrt(det)) / (2*a); + var sol_l = (-b - Math.sqrt(det)) / (2*a); + + return [sol_h, sol_l]; } - var mean_base = base_rate; - var mean_var = base_rate*(1+effect_size); - - var variance = mean_base*(1-mean_base) + mean_var*(1-mean_var); - - var mean_diff = mean_var - mean_base; - - var multiplier; - var sample_one_group; - if (opts && opts.type == 'absolutePerDay') { - if (opts.calculating == 'visitorsPerDay') { - var Z; - if (alternative == "greater") { - Z = jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1); - } else if (alternative == "lower") { - Z = jstat.normal.inv(1-beta, 0, 1) - jstat.normal.inv(alpha, 0, 1); + + function solveforeffectsize_Gtest({total_sample_size, base_rate, alpha, beta, variants, alternative, mu}){ + var sample_size = total_sample_size / (1 + variants); + + var rel_effect_size; + var Z; + var solutions; + if (alternative == "greater" || alternative == "lower") { + Z = jstat.normal.inv(beta, 0, 1) + jstat.normal.inv(alpha, 0, 1); + solutions = solve_quadratic(Z, sample_size, base_rate, mu); + if (alternative == 'greater') { + rel_effect_size = solutions[0] - 1; } else { - Z = jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1); + rel_effect_size = solutions[1] - 1; } - var sqrt_visitors_per_day = solve_quadratic_for_sample({mean_diff: mean_diff, Z: Z, - days: opts.days, threshold: opts.threshold, variance: variance}); - sample_one_group = opts.days*sqrt_visitors_per_day**2; } else { - multiplier = variance/(mean_diff*Math.sqrt(opts.visitors_per_day/2) - opts.threshold/(Math.sqrt(2*opts.visitors_per_day)))**2; - var days; - if (alternative == "greater" || alternative == "lower") { - days = multiplier * (jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1))**2; - } else { - days = multiplier * (jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1))**2; - } - sample_one_group = days*opts.visitors_per_day/2; + Z = jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1); + solutions = solve_quadratic(Z, sample_size, base_rate, mu); + rel_effect_size = solutions[0] - 1; } - } else { - multiplier = variance/(mu - mean_diff)**2; - if (alternative == "greater" || alternative == "lower") { - sample_one_group = multiplier * (jstat.normal.inv(beta, 0, 1) - jstat.normal.inv(1-alpha, 0, 1))**2; - } else { - sample_one_group = multiplier * (jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1))**2; - } + return rel_effect_size; } - return 2*Math.ceil(sample_one_group); -} + function get_visitors_with_goals({total_sample_size, base_rate}){ + return total_sample_size * base_rate; + } + function get_base_rate({total_sample_size, visitors_with_goals}){ + return visitors_with_goals / total_sample_size; + } -// SOLVING FOR EFFECT SIZE -function solveforeffectsize_Ttest({total_sample_size, base_rate, sd_rate, alpha, beta, alternative, mu}){ - var sample_size = total_sample_size/2; - var variance = 2*sd_rate**2; + function get_absolute_impact_in_metric_hash({base_rate, effect_size}) { + let value = base_rate * effect_size; + return { + value, + min: base_rate - value, + max: base_rate + value + } + } - var z = jstat.normal.inv(1-beta, 0, 1); - var multiplier = Math.sqrt(variance/sample_size); - var effect_size; - if (alternative == "greater") { - effect_size = mu + (z - jstat.normal.inv(alpha, 0, 1)) * multiplier; - } else if (alternative == "lower") { - effect_size = mu - (z - jstat.normal.inv(alpha, 0, 1)) * multiplier; - } else { - var delta = (z + jstat.normal.inv(1-alpha/2, 0, 1) )* multiplier; - effect_size = mu + delta; + function get_relative_impact_from_absolute({base_rate, absolute_effect_size}) { + return ((base_rate + absolute_effect_size) / base_rate) - 1 } - return effect_size/base_rate; -} + function get_absolute_impact_in_visitors({total_sample_size, base_rate, effect_size}) { + let absoluteImpactInMetric = get_absolute_impact_in_metric_hash({base_rate, effect_size}).value; + return absoluteImpactInMetric * total_sample_size + } -function solve_quadratic(Z, sample_size, control_rate, mu) { - var a = (Z**2 + sample_size) * control_rate**2; - var b = -(Z**2) * control_rate - 2 * (control_rate + mu) * sample_size * control_rate; - var c = sample_size * (control_rate + mu)**2 - Z**2 * control_rate * (1-control_rate); + function get_relative_impact_from_visitors({total_sample_size, base_rate, visitors}) { + let absoluteImpactInMetric = visitors / total_sample_size; - var det = b**2 - 4*a*c; - if (det < 0) { - return [NaN, NaN]; + return get_relative_impact_from_absolute({ + base_rate, + absolute_effect_size: absoluteImpactInMetric + }) } - var sol_h = (-b + Math.sqrt(det)) / (2*a); - var sol_l = (-b - Math.sqrt(det)) / (2*a); - - return [sol_h, sol_l]; -} + function get_mu_from_relative_difference ({threshold, base_rate}) { + return threshold*base_rate; + } -function solveforeffectsize_Gtest({total_sample_size, base_rate, alpha, beta, alternative, mu}){ - var sample_size = total_sample_size / 2; + function get_mu_from_absolute_per_day ({threshold, visitors_per_day}) { + return threshold/visitors_per_day; + } - var rel_effect_size; - var Z; - var solutions; - if (alternative == "greater" || alternative == "lower") { - Z = jstat.normal.inv(beta, 0, 1) + jstat.normal.inv(alpha, 0, 1); - solutions = solve_quadratic(Z, sample_size, base_rate, mu); - if (alternative == 'greater') { - rel_effect_size = solutions[0] - 1; - } else { - rel_effect_size = solutions[1] - 1; + function get_alternative ({type}) { + let alternative = 'two-sided'; + if (type == 'noninferiority') { + alternative = 'greater'; } - } else { - Z = jstat.normal.inv(1-beta, 0, 1) + jstat.normal.inv(1-alpha/2, 0, 1); - solutions = solve_quadratic(Z, sample_size, base_rate, mu); - rel_effect_size = solutions[0] - 1; + return alternative; } - return rel_effect_size; -} + var statFormulas = { + gTest: { + power: solveforpower_Gtest, + sample: solveforsample_Gtest, + impact: solveforeffectsize_Gtest + }, + tTest: { + power: solveforpower_Ttest, + sample: solveforsample_Ttest, + impact: solveforeffectsize_Ttest + }, + getVisitorsWithGoals: get_visitors_with_goals, + getBaseRate: get_base_rate, + getAbsoluteImpactInMetricHash: get_absolute_impact_in_metric_hash, + getAbsoluteImpactInVisitors: get_absolute_impact_in_visitors, + getRelativeImpactFromAbsolute: get_relative_impact_from_absolute, + getRelativeImpactFromVisitors: get_relative_impact_from_visitors, + getMuFromRelativeDifference: get_mu_from_relative_difference, + getMuFromAbsolutePerDay: get_mu_from_absolute_per_day, + getAlternative: get_alternative, + getCorrectedAlpha: get_alpha_sidaks_correction, + }; + + var _incrementalTrials = { + getGraphYTicks () { + let impact = isNaN(this.impact) ? 0 : this.impact, + arr = [impact/1.50, impact/1.25, impact, impact*1.25, impact*1.50]; + return arr + }, + getGraphYTicksFormatted (y) { + let sample = this.sample, + base = this.base, + + result = statFormulas.getAbsoluteImpactInVisitors({ + total_sample_size: this.$store.getters.extractValue('sample', sample), + base_rate: this.$store.getters.extractValue('base', base), + effect_size: this.$store.getters.extractValue('impact', y) + }); -function get_visitors_with_goals({total_sample_size, base_rate}){ - return total_sample_size * base_rate; -} + return this.$store.getters.displayValue('impactByVisitors', result); + }, + updateClonedValues (clonedObj, value) { + clonedObj.effect_size = this.$store.getters.extractValue('impact', value); -function get_base_rate({total_sample_size, visitors_with_goals}){ - return visitors_with_goals / total_sample_size; -} + return clonedObj; + }, + getCurrentYValue () { + return this.impact + }, + getGraphXTicksFormatted (x) { + let { displayValue } = this, + result = x / this.runtime; -function get_absolute_impact_in_metric_hash({base_rate, effect_size}) { - let value = base_rate * effect_size; - return { - value, - min: base_rate - value, - max: base_rate + value - } -} - -function get_relative_impact_from_absolute({base_rate, absolute_effect_size}) { - return ((base_rate + absolute_effect_size) / base_rate) - 1 -} - -function get_absolute_impact_in_visitors({total_sample_size, base_rate, effect_size}) { - let absoluteImpactInMetric = get_absolute_impact_in_metric_hash({base_rate, effect_size}).value; - return absoluteImpactInMetric * total_sample_size -} - -function get_relative_impact_from_visitors({total_sample_size, base_rate, visitors}) { - let absoluteImpactInMetric = visitors / total_sample_size; - - return get_relative_impact_from_absolute({ - base_rate, - absolute_effect_size: absoluteImpactInMetric - }) -} - -function get_mu_from_relative_difference ({threshold, base_rate}) { - return threshold*base_rate; -} - -function get_mu_from_absolute_per_day ({threshold, visitors_per_day}) { - return threshold/visitors_per_day; -} - -function get_alternative ({type}) { - let alternative = 'two-sided'; - if (type == 'noninferiority') { - alternative = 'greater'; - } - return alternative; -} - -var statFormulas = { - gTest: { - power: solveforpower_Gtest, - sample: solveforsample_Gtest, - impact: solveforeffectsize_Gtest - }, - tTest: { - power: solveforpower_Ttest, - sample: solveforsample_Ttest, - impact: solveforeffectsize_Ttest - }, - getVisitorsWithGoals: get_visitors_with_goals, - getBaseRate: get_base_rate, - getAbsoluteImpactInMetricHash: get_absolute_impact_in_metric_hash, - getAbsoluteImpactInVisitors: get_absolute_impact_in_visitors, - getRelativeImpactFromAbsolute: get_relative_impact_from_absolute, - getRelativeImpactFromVisitors: get_relative_impact_from_visitors, - getMuFromRelativeDifference: get_mu_from_relative_difference, - getMuFromAbsolutePerDay: get_mu_from_absolute_per_day, - getAlternative: get_alternative, -}; - -var _incrementalTrials = { - getGraphYTicks () { - let impact = isNaN(this.impact) ? 0 : this.impact, - arr = [impact/1.50, impact/1.25, impact, impact*1.25, impact*1.50]; - - return arr - }, - getGraphYTicksFormatted (y) { - let sample = this.sample, - base = this.base, - - result = statFormulas.getAbsoluteImpactInVisitors({ - total_sample_size: this.$store.getters.extractValue('sample', sample), - base_rate: this.$store.getters.extractValue('base', base), - effect_size: this.$store.getters.extractValue('impact', y) - }); + result = displayValue('impactByVisitors', result); + if (result >= 1000) { + result = window.parseInt(result / 1000) + 'k'; + } - return this.$store.getters.displayValue('impactByVisitors', result); - }, - updateClonedValues (clonedObj, value) { - clonedObj.effect_size = this.$store.getters.extractValue('impact', value); - - return clonedObj; - }, - getCurrentYValue () { - return this.impact - }, - getGraphXTicksFormatted (x) { - let { displayValue } = this, - result = x / this.runtime; - - result = displayValue('impactByVisitors', result); - if (result >= 1000) { - result = window.parseInt(result / 1000) + 'k'; - } - - return result - }, - getGraphXValueForClonedValues (clonedValues) { - let {total_sample_size, base_rate, effect_size} = clonedValues, - - impactByVisitor = statFormulas.getAbsoluteImpactInVisitors({ - total_sample_size, - base_rate, - effect_size, - }); + return result + }, + getGraphXValueForClonedValues (clonedValues) { + let {total_sample_size, base_rate, effect_size} = clonedValues, - return this.$store.getters.displayValue('impactByVisitors', impactByVisitor); - } -}; - -var _incrementalTrialsPerDay = { - getGraphYTicks () { - let impact = isNaN(this.impact) ? 0 : this.impact, - arr = [impact/1.50, impact/1.25, impact, impact*1.25, impact*1.50]; - - return arr - }, - getGraphYTicksFormatted (y) { - let sample = this.sample, - base = this.base, - - result = statFormulas.getAbsoluteImpactInVisitors({ - total_sample_size: this.$store.getters.extractValue('sample', sample / this.runtime), - base_rate: this.$store.getters.extractValue('base', base), - effect_size: this.$store.getters.extractValue('impact', y) - }); + impactByVisitor = statFormulas.getAbsoluteImpactInVisitors({ + total_sample_size, + base_rate, + effect_size, + }); - if (isNaN(result)) { - result = 0; + return this.$store.getters.displayValue('impactByVisitors', impactByVisitor); } + }; + + var _incrementalTrialsPerDay = { + getGraphYTicks () { + let impact = isNaN(this.impact) ? 0 : this.impact, + arr = [impact/1.50, impact/1.25, impact, impact*1.25, impact*1.50]; + + return arr + }, + getGraphYTicksFormatted (y) { + let sample = this.sample, + base = this.base, + + result = statFormulas.getAbsoluteImpactInVisitors({ + total_sample_size: this.$store.getters.extractValue('sample', sample / this.runtime), + base_rate: this.$store.getters.extractValue('base', base), + effect_size: this.$store.getters.extractValue('impact', y) + }); + + if (isNaN(result)) { + result = 0; + } + + return this.$store.getters.displayValue('impactByVisitors', result); + }, + updateClonedValues (clonedObj, value) { + clonedObj.effect_size = this.$store.getters.extractValue('impact', value); + + return clonedObj; + }, + getCurrentYValue () { + return this.impact + }, + getGraphXTicksFormatted (x) { + let { displayValue } = this, + result = x / this.runtime; + + result = displayValue('impactByVisitors', result); + if (result >= 1000) { + result = window.parseInt(result / 1000) + 'k'; + } - return this.$store.getters.displayValue('impactByVisitors', result); - }, - updateClonedValues (clonedObj, value) { - clonedObj.effect_size = this.$store.getters.extractValue('impact', value); + return result + }, + getGraphXValueForClonedValues (clonedValues) { + let {total_sample_size, base_rate, effect_size} = clonedValues, - return clonedObj; - }, - getCurrentYValue () { - return this.impact - }, - getGraphXTicksFormatted (x) { - let { displayValue } = this, - result = x / this.runtime; + impactByVisitor = statFormulas.getAbsoluteImpactInVisitors({ + total_sample_size, + base_rate, + effect_size, + }); - result = displayValue('impactByVisitors', result); - if (result >= 1000) { - result = window.parseInt(result / 1000) + 'k'; + return this.$store.getters.displayValue('impactByVisitors', impactByVisitor); } + }; - return result - }, - getGraphXValueForClonedValues (clonedValues) { - let {total_sample_size, base_rate, effect_size} = clonedValues, + var _days = { + getGraphXTicksFormatted (x) { + let samplePerDay = this.sample / this.runtime, + result = x / samplePerDay; + result = this.$store.getters.displayValue('sample', result); + if (result >= 1000) { + result = window.parseInt(result / 1000) + 'k'; + } - impactByVisitor = statFormulas.getAbsoluteImpactInVisitors({ - total_sample_size, - base_rate, - effect_size, - }); + return result + }, + getGraphXValueForClonedValues (clonedValues) { + let graphX = 'sample'; + return this.$store.getters.displayValue(graphX, (this.math[graphX](clonedValues))); + } + }; - return this.$store.getters.displayValue('impactByVisitors', impactByVisitor); - } -}; - -var _days = { - getGraphXTicksFormatted (x) { - let samplePerDay = this.sample / this.runtime, - result = x / samplePerDay; - result = this.$store.getters.displayValue('sample', result); - if (result >= 1000) { - result = window.parseInt(result / 1000) + 'k'; - } - - return result - }, - getGraphXValueForClonedValues (clonedValues) { - let graphX = 'sample'; - return this.$store.getters.displayValue(graphX, (this.math[graphX](clonedValues))); - } -}; + var _sample = { + getGraphXTicksFormatted (x) { + let result = x; -var _sample = { - getGraphXTicksFormatted (x) { - let result = x; + result = this.$store.getters.displayValue('sample', result); + if (result >= 1000) { + result = window.parseInt(result / 1000) + 'k'; + } - result = this.$store.getters.displayValue('sample', result); - if (result >= 1000) { - result = window.parseInt(result / 1000) + 'k'; + return result } + }; - return result - } -}; + var _samplePerDay = { + getGraphXTicksFormatted (x) { + let result = x / this.runtime; + result = this.$store.getters.displayValue('sample', result); + if (result >= 1000) { + result = window.parseInt(result / 1000) + 'k'; + } -var _samplePerDay = { - getGraphXTicksFormatted (x) { - let result = x / this.runtime; - result = this.$store.getters.displayValue('sample', result); - if (result >= 1000) { - result = window.parseInt(result / 1000) + 'k'; + return result + }, + getGraphXValueForClonedValues (clonedValues) { + let graphX = 'sample'; + return this.$store.getters.displayValue(graphX, (this.math[graphX](clonedValues))); } + }; - return result - }, - getGraphXValueForClonedValues (clonedValues) { - let graphX = 'sample'; - return this.$store.getters.displayValue(graphX, (this.math[graphX](clonedValues))); - } -}; - -var _power = { - getGraphYTicks () { - let arr = [10, 25, 50, 75, 100]; - return arr - }, - getGraphYTicksFormatted (y) { - return `${y}%` - }, - updateClonedValues (clonedObj, value) { - clonedObj.beta = 1 - this.$store.getters.extractValue('power', value); - - return clonedObj; - }, - getCurrentYValue () { - return this.power - }, - getGraphXTicksFormatted () { - // not needed yet - }, -}; - -var _threshold = { - getGraphYTicks () { - let threshold = isNaN(this.$store.state.nonInferiority.threshold) ? 0 : this.$store.state.nonInferiority.threshold, - arr = [threshold/1.50, threshold/1.25, threshold, threshold*1.25, threshold*1.50]; - return arr - }, - getGraphYTicksFormatted (y) { - let num = window.parseFloat(y); - if ((num % 1) !== 0) { - num = num.toFixed(2); - } - - if (isNaN(num)) { - num = 0; - } - - const suffix = this.$store.state.nonInferiority.selected == 'relative' ? '%' : ''; - - return `${num}${suffix}` - }, - getCurrentYValue () { - return this.$store.state.nonInferiority.threshold - }, - updateClonedValues (clonedObj, value) { - let { getters, state } = this.$store, - { customMu, customOpts, customAlternative, customThresholdCorrectedValue } = getters; - - const thresholdCorrectedValue = customThresholdCorrectedValue({ - threshold: value, - selected: state.nonInferiority.selected - }); + var _power = { + getGraphYTicks () { + let arr = [10, 25, 50, 75, 100]; + return arr + }, + getGraphYTicksFormatted (y) { + return `${y}%` + }, + updateClonedValues (clonedObj, value) { + clonedObj.beta = 1 - this.$store.getters.extractValue('power', value); - const mu = customMu({ - runtime: getters.runtime, - thresholdCorrectedValue: thresholdCorrectedValue, - visitors_per_day: getters.visitorsPerDay, - base_rate: getters.extractValue('base'), - }); + return clonedObj; + }, + getCurrentYValue () { + return this.power + }, + getGraphXTicksFormatted () { + // not needed yet + }, + }; - const opts = customOpts({ - selected: state.nonInferiority.selected, - lockedField: getters.lockedField, - thresholdCorrectedValue: thresholdCorrectedValue, - runtime: getters.runtime, - visitorsPerDay: getters.visitorsPerDay, - }); + var _threshold = { + getGraphYTicks () { + let threshold = isNaN(this.$store.state.nonInferiority.threshold) ? 0 : this.$store.state.nonInferiority.threshold, + arr = [threshold/1.50, threshold/1.25, threshold, threshold*1.25, threshold*1.50]; + return arr + }, + getGraphYTicksFormatted (y) { + let num = window.parseFloat(y); + if ((num % 1) !== 0) { + num = num.toFixed(2); + } + + if (isNaN(num)) { + num = 0; + } + + const suffix = this.$store.state.nonInferiority.selected == 'relative' ? '%' : ''; + + return `${num}${suffix}` + }, + getCurrentYValue () { + return this.$store.state.nonInferiority.threshold + }, + updateClonedValues (clonedObj, value) { + let { getters, state } = this.$store, + { customMu, customOpts, customAlternative, customThresholdCorrectedValue } = getters; + + const thresholdCorrectedValue = customThresholdCorrectedValue({ + threshold: value, + selected: state.nonInferiority.selected + }); + + const mu = customMu({ + runtime: getters.runtime, + thresholdCorrectedValue: thresholdCorrectedValue, + visitors_per_day: getters.visitorsPerDay, + base_rate: getters.extractValue('base'), + }); + + const opts = customOpts({ + selected: state.nonInferiority.selected, + lockedField: getters.lockedField, + thresholdCorrectedValue: thresholdCorrectedValue, + runtime: getters.runtime, + visitorsPerDay: getters.visitorsPerDay, + }); + + const alternative = customAlternative({ type: 'noninferiority' }); - const alternative = customAlternative({ type: 'noninferiority' }); + Object.assign(clonedObj, { + mu, + opts, + alternative + }); + + return clonedObj; + }, + }; - Object.assign(clonedObj, { - mu, - opts, - alternative - }); + // name convention is the name used to set the graphY and graphX with a underscore before it - return clonedObj; - }, -}; - -// name convention is the name used to set the graphY and graphX with a underscore before it - -var defaultConfig = { - getGraphYTicks () { - throw Error (`getGraphYTicks not defined for ${this.graphY}`) - }, - getGraphYTicksFormatted () { - throw Error (`getGraphYTicksFormatted not defined for ${this.graphY}`) - }, - getGraphYDataSet ({amount}) { - let yTicks = this.getGraphYTicks(), - curYValue = this.getCurrentYValue(), - firstTick = yTicks[0], - lastTick = yTicks[yTicks.length - 1], - ratio = (lastTick - firstTick) / amount, - result = Array.from(new Array(amount)); - - result = result.map((cur, i, arr) => { - let value = firstTick + ratio * i; - return value - }); - // add the current value in case it isn't there - result.push(curYValue); - - // sort current value - result.sort((a,b) => { return a - b}); - - // remove duplicates - result = [...new Set(result)]; - - return result - }, - updateClonedValues () { - throw Error (`updateClonedValues not defined for ${this.graphY}`) - }, - getCurrentYValue () { - throw Error (`getCurrentYValue not defined for ${this.graphY}`) - }, - getGraphXTicksFormatted () { - throw Error (`getGraphXTicksFormatted not defined for ${this.graphY}`) - }, - getGraphXValueForClonedValues (clonedValues) { - if (!this.math[this.graphX]) { - throw Error (`getGraphXValueForClonedValues didn't find math formula for ${this.graphX}`) - } - return this.$store.getters.displayValue(this.graphX, (this.math[this.graphX](clonedValues))); - } -}; - - - -var graphDataMixin = { - beforeCreate () { - // register configurations for metric params - // this is done to agregate different pieces of configuration that need to work in harmony - // for the svg graph - Object.assign(this, { - _sample: Object.assign({}, defaultConfig, _sample), - _samplePerDay: Object.assign({}, defaultConfig, _samplePerDay), - _impact: Object.assign({}, defaultConfig, _impact), - _incrementalTrials: Object.assign({}, defaultConfig, _incrementalTrials), - _power: Object.assign({}, defaultConfig, _power), - _incrementalTrialsPerDay: Object.assign({}, defaultConfig, _incrementalTrialsPerDay), - _days: Object.assign({}, defaultConfig, _days), - _threshold: Object.assign({}, defaultConfig, _threshold), - }); - }, - methods: { + var defaultConfig = { getGraphYTicks () { - return this._getGraphY().getGraphYTicks.apply(this, []); + throw Error (`getGraphYTicks not defined for ${this.graphY}`) }, getGraphYTicksFormatted () { - return this._getGraphY().getGraphYTicksFormatted.apply(this, [...arguments]); + throw Error (`getGraphYTicksFormatted not defined for ${this.graphY}`) }, - getGraphYDataSet () { - return this._getGraphY().getGraphYDataSet.apply(this, [...arguments]); + getGraphYDataSet ({amount}) { + let yTicks = this.getGraphYTicks(), + curYValue = this.getCurrentYValue(), + firstTick = yTicks[0], + lastTick = yTicks[yTicks.length - 1], + ratio = (lastTick - firstTick) / amount, + result = Array.from(new Array(amount)); + + result = result.map((cur, i, arr) => { + let value = firstTick + ratio * i; + return value + }); + + // add the current value in case it isn't there + result.push(curYValue); + + // sort current value + result.sort((a,b) => { return a - b}); + + // remove duplicates + result = [...new Set(result)]; + + return result }, updateClonedValues () { - return this._getGraphY().updateClonedValues.apply(this, [...arguments]); + throw Error (`updateClonedValues not defined for ${this.graphY}`) }, getCurrentYValue () { - return this._getGraphY().getCurrentYValue.apply(this, [...arguments]); - }, - getGraphXValueForClonedValues () { - return this._getGraphX().getGraphXValueForClonedValues.apply(this, [...arguments]); + throw Error (`getCurrentYValue not defined for ${this.graphY}`) }, getGraphXTicksFormatted () { - return this._getGraphX().getGraphXTicksFormatted.apply(this, [...arguments]); + throw Error (`getGraphXTicksFormatted not defined for ${this.graphY}`) }, - _getGraphX () { - let graphX = this[`_${this.graphX}`]; - - if (!graphX) { - throw Error (`_${this.graphX} is not registered`); + getGraphXValueForClonedValues (clonedValues) { + if (!this.math[this.graphX]) { + throw Error (`getGraphXValueForClonedValues didn't find math formula for ${this.graphX}`) } + return this.$store.getters.displayValue(this.graphX, (this.math[this.graphX](clonedValues))); + } + }; + + - return graphX + var graphDataMixin = { + beforeCreate () { + // register configurations for metric params + // this is done to agregate different pieces of configuration that need to work in harmony + // for the svg graph + Object.assign(this, { + _sample: Object.assign({}, defaultConfig, _sample), + _samplePerDay: Object.assign({}, defaultConfig, _samplePerDay), + _impact: Object.assign({}, defaultConfig, _impact), + _incrementalTrials: Object.assign({}, defaultConfig, _incrementalTrials), + _power: Object.assign({}, defaultConfig, _power), + _incrementalTrialsPerDay: Object.assign({}, defaultConfig, _incrementalTrialsPerDay), + _days: Object.assign({}, defaultConfig, _days), + _threshold: Object.assign({}, defaultConfig, _threshold), + }); }, - _getGraphY () { - let graphY = this[`_${this.graphY}`]; + methods: { + getGraphYTicks () { + return this._getGraphY().getGraphYTicks.apply(this, []); + }, + getGraphYTicksFormatted () { + return this._getGraphY().getGraphYTicksFormatted.apply(this, [...arguments]); + }, + getGraphYDataSet () { + return this._getGraphY().getGraphYDataSet.apply(this, [...arguments]); + }, + updateClonedValues () { + return this._getGraphY().updateClonedValues.apply(this, [...arguments]); + }, + getCurrentYValue () { + return this._getGraphY().getCurrentYValue.apply(this, [...arguments]); + }, + getGraphXValueForClonedValues () { + return this._getGraphX().getGraphXValueForClonedValues.apply(this, [...arguments]); + }, + getGraphXTicksFormatted () { + return this._getGraphX().getGraphXTicksFormatted.apply(this, [...arguments]); + }, + _getGraphX () { + let graphX = this[`_${this.graphX}`]; - if (!graphY) { - throw Error (`_${this.graphY} is not registered`); - } + if (!graphX) { + throw Error (`_${this.graphX} is not registered`); + } - return graphY - } - } -}; + return graphX + }, + _getGraphY () { + let graphY = this[`_${this.graphY}`]; -var svgGraphTabItem = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('label',{staticClass:"pc-graph-radio-label"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.graphTypeSynced),expression:"graphTypeSynced"}],staticClass:"pc-graph-radio-input",attrs:{"type":"radio","name":"graph"},domProps:{"value":_vm.value,"checked":_vm._q(_vm.graphTypeSynced,_vm.value)},on:{"change":function($event){_vm.graphTypeSynced=_vm.value;}}}),_vm._v(" "),_c('span',{staticClass:"pc-graph-radio-text",class:_vm.spanClass},[_vm._v(_vm._s(_vm.graphName))])])},staticRenderFns: [], - props: ['graphType', 'graphX', 'graphY', 'getMetricDisplayName'], - data () { - return { + if (!graphY) { + throw Error (`_${this.graphY} is not registered`); + } + + return graphY + } } - }, - computed: { - graphName () { - const graphXDisplay = this.getMetricDisplayName(this.graphX); - const graphYDisplay = this.getMetricDisplayName(this.graphY); - return `${graphYDisplay} / ${graphXDisplay}` - }, - value () { - return `${this.graphX}-${this.graphY}` - }, - spanClass () { + }; + + var svgGraphTabItem = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('label',{staticClass:"pc-graph-radio-label"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.graphTypeSynced),expression:"graphTypeSynced"}],staticClass:"pc-graph-radio-input",attrs:{"type":"radio","name":"graph"},domProps:{"value":_vm.value,"checked":_vm._q(_vm.graphTypeSynced,_vm.value)},on:{"change":function($event){_vm.graphTypeSynced=_vm.value;}}}),_vm._v(" "),_c('span',{staticClass:"pc-graph-radio-text",class:_vm.spanClass},[_vm._v(_vm._s(_vm.graphName))])])},staticRenderFns: [], + props: ['graphType', 'graphX', 'graphY', 'getMetricDisplayName'], + data () { return { - 'pc-graph-radio-selected': this.graphType == this.value } }, - graphTypeSynced: { - get () { - return this.graphType + computed: { + graphName () { + const graphXDisplay = this.getMetricDisplayName(this.graphX); + const graphYDisplay = this.getMetricDisplayName(this.graphY); + return `${graphYDisplay} / ${graphXDisplay}` + }, + value () { + return `${this.graphX}-${this.graphY}` + }, + spanClass () { + return { + 'pc-graph-radio-selected': this.graphType == this.value + } }, - set (newValue) { - this.$emit('update:graphType', newValue); + graphTypeSynced: { + get () { + return this.graphType + }, + set (newValue) { + this.$emit('update:graphType', newValue); + } } } - } -}; - -/*global c3*/ -/*eslint no-undef: "error"*/ + }; -let dataDefault = [ - ['x', 0, 0, 0, 0, 0, 0], - ['Sample', 0, 0, 0, 0, 0], - ['Current', null, null, 50] - ]; + let dataDefault = [ + ['x', 0, 0, 0, 0, 0, 0], + ['Sample', 0, 0, 0, 0, 0], + ['Current', null, null, 50] + ]; -let style = document.createElement('style'); + let style = document.createElement('style'); -style.innerHTML = ` + style.innerHTML = ` .pc-graph .c3-axis-y-label { pointer-events: none; } @@ -5450,207 +5408,207 @@ style.innerHTML = ` } `; -document.querySelector('head').appendChild(style); - + document.querySelector('head').appendChild(style); -var svgGraph = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--graph"},[_c('div',{staticClass:"pc-graph-controls"},_vm._l((_vm.graphList),function(graph){return _c('svgGraphTabItem',{key:graph.graphX + '-' + graph.graphY,attrs:{"graphX":graph.graphX,"graphY":graph.graphY,"getMetricDisplayName":_vm.getMetricDisplayName,"graphType":_vm.graphType},on:{"update:graphType":function($event){_vm.graphType=$event;}}})})),_vm._v(" "),_c('div',{ref:"pc-graph-size",staticClass:"pc-graph"},[_c('div',{ref:"pc-graph-wrapper",style:(_vm.style)},[_c('div',{ref:"pc-graph"})])])])},staticRenderFns: [], - mixins: [graphDataMixin], - props: [], - data () { - return { - width: 100, - height: 100, - data: this.dataDefault, - graphType: this.getDefaultGraphOption() // x, y - // graphX: 'sample' // computed - // graphY: 'power' // computed - } - }, - computed: { - style () { - let { width, height } = this; + var svgGraph = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--graph"},[_c('div',{staticClass:"pc-graph-controls"},_vm._l((_vm.graphList),function(graph){return _c('svgGraphTabItem',{key:graph.graphX + '-' + graph.graphY,attrs:{"graphX":graph.graphX,"graphY":graph.graphY,"getMetricDisplayName":_vm.getMetricDisplayName,"graphType":_vm.graphType},on:{"update:graphType":function($event){_vm.graphType=$event;},"update:graph-type":function($event){_vm.graphType=$event;}}})}),1),_vm._v(" "),_c('div',{ref:"pc-graph-size",staticClass:"pc-graph"},[_c('div',{ref:"pc-graph-wrapper",style:(_vm.style)},[_c('div',{ref:"pc-graph"})])])])},staticRenderFns: [], + mixins: [graphDataMixin], + props: [], + data () { return { - width: `${width}px`, - height: `${height}px` + width: 100, + height: 100, + data: this.dataDefault, + graphType: this.getDefaultGraphOption() // x, y + // graphX: 'sample' // computed + // graphY: 'power' // computed } }, - graphX () { - // 'sample' - return this.graphType.split('-')[0] - }, - graphY () { - // 'power' - return this.graphType.split('-')[1] - }, - math () { - return this.$store.getters.formulaToSolveProp - }, - isNonInferiorityEnabled () { - return this.$store.state.nonInferiority.enabled - }, - sample () { - return this.$store.state.attributes.sample - }, - impact () { - return this.$store.state.attributes.impact - }, - power () { - return this.$store.state.attributes.power - }, - base () { - return this.$store.state.attributes.base - }, - falsePosRate () { - return this.$store.state.attributes.falsePosRate - }, - sdRate () { - return this.$store.state.attributes.sdRate - }, - runtime () { - return this.$store.state.attributes.runtime - }, - graphList() { - - let list = [ - { - name: 'days-incrementalTrialsPerDay', - cond: !this.isNonInferiorityEnabled - }, - { - name: 'samplePerDay-incrementalTrials', - cond: !this.isNonInferiorityEnabled - }, - { - name: 'sample-impact', - cond: !this.isNonInferiorityEnabled - }, - { - name: 'sample-threshold', - cond: this.isNonInferiorityEnabled - }, - { - name: 'sample-power', - cond: true - }, - { - name: 'samplePerDay-power', - cond: true - }, - { - name: 'impact-power', - cond: !this.isNonInferiorityEnabled - }, - ]; + computed: { + style () { + let { width, height } = this; - return list.filter((obj) => {return obj.cond == true}).map((obj) => { - let [graphX, graphY] = obj.name.split('-'); - return { - graphX, - graphY - } - }) - } - }, - methods: { - getDefaultGraphOption () { - let result = 'days-incrementalTrialsPerDay'; - if (this.$store.state.nonInferiority.enabled) { - result = 'sample-threshold'; + return { + width: `${width}px`, + height: `${height}px` + } + }, + graphX () { + // 'sample' + return this.graphType.split('-')[0] + }, + graphY () { + // 'power' + return this.graphType.split('-')[1] + }, + math () { + return this.$store.getters.formulaToSolveProp + }, + isNonInferiorityEnabled () { + return this.$store.state.nonInferiority.enabled + }, + sample () { + return this.$store.state.attributes.sample + }, + impact () { + return this.$store.state.attributes.impact + }, + power () { + return this.$store.state.attributes.power + }, + base () { + return this.$store.state.attributes.base + }, + falsePosRate () { + return this.$store.state.attributes.falsePosRate + }, + sdRate () { + return this.$store.state.attributes.sdRate + }, + runtime () { + return this.$store.state.attributes.runtime + }, + graphList() { + + let list = [ + { + name: 'days-incrementalTrialsPerDay', + cond: !this.isNonInferiorityEnabled + }, + { + name: 'samplePerDay-incrementalTrials', + cond: !this.isNonInferiorityEnabled + }, + { + name: 'sample-impact', + cond: !this.isNonInferiorityEnabled + }, + { + name: 'sample-threshold', + cond: this.isNonInferiorityEnabled + }, + { + name: 'sample-power', + cond: true + }, + { + name: 'samplePerDay-power', + cond: true + }, + { + name: 'impact-power', + cond: !this.isNonInferiorityEnabled + }, + ]; + + return list.filter((obj) => {return obj.cond == true}).map((obj) => { + let [graphX, graphY] = obj.name.split('-'); + return { + graphX, + graphY + } + }) } - return result }, - resize () { - let {width, paddingLeft, paddingRight} = window.getComputedStyle(this.$refs['pc-graph-size']); + methods: { + getDefaultGraphOption () { + let result = 'days-incrementalTrialsPerDay'; + if (this.$store.state.nonInferiority.enabled) { + result = 'sample-threshold'; + } + return result + }, + resize () { + let {width, paddingLeft, paddingRight} = window.getComputedStyle(this.$refs['pc-graph-size']); - // update svg size - this.width = window.parseInt(width) - window.parseInt(paddingLeft) - window.parseInt(paddingRight); - this.height = 220; - }, - createYList ({ amount, rate = 10, cur }) { //rate of 10 and amount of 10 will reach from 0 to 100 - let result = []; + // update svg size + this.width = window.parseInt(width) - window.parseInt(paddingLeft) - window.parseInt(paddingRight); + this.height = 220; + }, + createYList ({ amount, rate = 10, cur }) { //rate of 10 and amount of 10 will reach from 0 to 100 + let result = []; - for (let i = 0; i <= amount; i += 1) { - let y = rate * i, - nextY = rate * (i + 1); + for (let i = 0; i <= amount; i += 1) { + let y = rate * i, + nextY = rate * (i + 1); - result.push(y); + result.push(y); - if (cur > y && cur < nextY) { - result.push(cur); + if (cur > y && cur < nextY) { + result.push(cur); + } } - } - return result; - }, - trimInvalidSamples (newData) { - let result = newData[0].reduce((prevArr, xValue, i) => { + return result; + }, + trimInvalidSamples (newData) { + let result = newData[0].reduce((prevArr, xValue, i) => { - if (i == 0) { - prevArr[0] = []; - prevArr[1] = []; - prevArr[2] = []; - } + if (i == 0) { + prevArr[0] = []; + prevArr[1] = []; + prevArr[2] = []; + } - // i == 0 is the name of the dataset - if (i == 0 || (!isNaN(xValue) && isFinite(xValue))) { - prevArr[0].push(newData[0][i]); - prevArr[1].push(newData[1][i]); - prevArr[2].push(newData[2][i]); - } + // i == 0 is the name of the dataset + if (i == 0 || (!isNaN(xValue) && isFinite(xValue))) { + prevArr[0].push(newData[0][i]); + prevArr[1].push(newData[1][i]); + prevArr[2].push(newData[2][i]); + } - return prevArr; - }, []); - return result; - }, - updateGraphData () { + return prevArr; + }, []); + return result; + }, + updateGraphData () { - let clonedValues = this.deepCloneObject(this.$store.getters.convertDisplayedValues), - newData = this.deepCloneObject(dataDefault), - yList = this.getGraphYDataSet({amount: 10}), - curY = this.getCurrentYValue(); + let clonedValues = this.deepCloneObject(this.$store.getters.convertDisplayedValues), + newData = this.deepCloneObject(dataDefault), + yList = this.getGraphYDataSet({amount: 10}), + curY = this.getCurrentYValue(); - // erase previous values but keep names of datasets - newData[0].length = 1; - newData[1].length = 1; - newData[2].length = 1; + // erase previous values but keep names of datasets + newData[0].length = 1; + newData[1].length = 1; + newData[2].length = 1; - yList.forEach((yValue, i) => { + yList.forEach((yValue, i) => { - clonedValues = this.updateClonedValues(clonedValues, yValue); + clonedValues = this.updateClonedValues(clonedValues, yValue); - let xValues = this.getGraphXValueForClonedValues(clonedValues, yValue); + let xValues = this.getGraphXValueForClonedValues(clonedValues, yValue); - newData[0][i + 1] = xValues; // x - newData[1][i + 1] = yValue; // line - newData[2][i + 1] = yValue == curY ? yValue : null; // current power dot - }); + newData[0][i + 1] = xValues; // x + newData[1][i + 1] = yValue; // line + newData[2][i + 1] = yValue == curY ? yValue : null; // current power dot + }); - newData = this.trimInvalidSamples(newData); + newData = this.trimInvalidSamples(newData); - this.chart.axis.labels({x: this.updateXLabel(), y: this.updateYLabel()}); + this.chart.axis.labels({x: this.updateXLabel(), y: this.updateYLabel()}); - this.chart.load({ - columns: newData - }); - }, - deepCloneObject (obj) { - return JSON.parse(JSON.stringify(obj)) - }, - createTooltip (V) { + this.chart.load({ + columns: newData + }); + }, + deepCloneObject (obj) { + return JSON.parse(JSON.stringify(obj)) + }, + createTooltip (V) { - return { - grouped: false, - contents ([{x = 0, value = 0, id = ''}]) { + return { + grouped: false, + contents ([{x = 0, value = 0, id = ''}]) { - let {graphX, graphY, getMetricDisplayName, getGraphXTicksFormatted, getGraphYTicksFormatted} = V, - th = getMetricDisplayName(graphX), - name = getMetricDisplayName(graphY), - xFormatted = getGraphXTicksFormatted(x), - yFormatted = getGraphYTicksFormatted(value); + let {graphX, graphY, getMetricDisplayName, getGraphXTicksFormatted, getGraphYTicksFormatted} = V, + th = getMetricDisplayName(graphX), + name = getMetricDisplayName(graphY), + xFormatted = getGraphXTicksFormatted(x), + yFormatted = getGraphYTicksFormatted(value); - return ` + return ` @@ -5667,454 +5625,460 @@ var svgGraph = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c
` + } } + }, + updateYLabel () { + return this.getMetricDisplayName(this.graphY) + }, + updateXLabel () { + return this.getMetricDisplayName(this.graphX) + }, + getMetricDisplayName (metric) { + return { + sample: 'Sample', + impact: 'Impact', + power: 'Power', + base: 'Base', + falseposrate: 'False Positive Rate', + sdrate: 'Base Standard deviation', + + samplePerDay: 'Daily Visitors', + incrementalTrials: 'Incremental Trials', + + days: '# of days', + incrementalTrialsPerDay: 'Inc. Trials per day', + threshold: 'Non Inf. Threshold', + }[metric] || '' } }, - updateYLabel () { - return this.getMetricDisplayName(this.graphY) - }, - updateXLabel () { - return this.getMetricDisplayName(this.graphX) - }, - getMetricDisplayName (metric) { - return { - sample: 'Sample', - impact: 'Impact', - power: 'Power', - base: 'Base', - falseposrate: 'False Positive Rate', - sdrate: 'Base Standard deviation', - - samplePerDay: 'Daily Visitors', - incrementalTrials: 'Incremental Trials', - - days: '# of days', - incrementalTrialsPerDay: 'Inc. Trials per day', - threshold: 'Non Inf. Threshold', - }[metric] || '' - } - }, - watch: { - sample () { - this.updateGraphData(); - }, - impact () { - this.updateGraphData(); - }, - power () { - this.updateGraphData(); - }, - graphY () { - this.updateGraphData(); - }, - graphX () { - this.updateGraphData(); - }, - runtime () { - this.updateGraphData(); - }, - isNonInferiorityEnabled (bool) { - if (this.graphList.indexOf(this.graphType) == -1) { - let { graphX, graphY } = this.graphList[0]; - this.graphType = `${graphX}-${graphY}`; - } - } - }, - mounted () { - let {resize, createTooltip} = this, - vueInstance = this; - resize(); - - this.dataDefault = dataDefault; - - this.chart = c3.generate({ - bindto: this.$refs['pc-graph'], - size: { - width: this.width, - height: this.height + watch: { + sample () { + this.updateGraphData(); }, - legend: { - show: false + impact () { + this.updateGraphData(); }, - data: { - x: 'x', - columns: this.dataDefault, - type: 'area' + power () { + this.updateGraphData(); }, - grid: { - x: { - show: true - }, - y: { - show: true - } + graphY () { + this.updateGraphData(); }, - axis: { - x: { - label: this.updateXLabel(), - tick: { - values (minMax) { - let [min, max] = minMax.map((num) => {return window.parseInt(num)}), - amount = 7, - ratio = (max - min) / amount, - result = new Array(amount + 1); - - // create the values - result = Array.from(result).map((undef, i) => { - return (min + (ratio * i)).toFixed(2) - }); - - return result - }, - format (x) { - return vueInstance.getGraphXTicksFormatted(x) - } + graphX () { + this.updateGraphData(); + }, + runtime () { + this.updateGraphData(); + }, + isNonInferiorityEnabled (bool) { + if (this.graphList.indexOf(this.graphType) == -1) { + let { graphX, graphY } = this.graphList[0]; + this.graphType = `${graphX}-${graphY}`; + } + } + }, + mounted () { + let {resize, createTooltip} = this, + vueInstance = this; + resize(); + + this.dataDefault = dataDefault; + + this.chart = c3.generate({ + bindto: this.$refs['pc-graph'], + size: { + width: this.width, + height: this.height + }, + legend: { + show: false + }, + data: { + x: 'x', + columns: this.dataDefault, + type: 'area' + }, + grid: { + x: { + show: true + }, + y: { + show: true } }, - y: { - label: this.updateYLabel(), - tick: { - values () { - return vueInstance.getGraphYTicks() - }, - format (y) { - return vueInstance.getGraphYTicksFormatted(y) + axis: { + x: { + label: this.updateXLabel(), + tick: { + values (minMax) { + let [min, max] = minMax.map((num) => {return window.parseInt(num)}), + amount = 7, + ratio = (max - min) / amount, + result = new Array(amount + 1); + + // create the values + result = Array.from(result).map((undef, i) => { + return (min + (ratio * i)).toFixed(2) + }); + + return result + }, + format (x) { + return vueInstance.getGraphXTicksFormatted(x) + } + } + }, + y: { + label: this.updateYLabel(), + tick: { + values () { + return vueInstance.getGraphYTicks() + }, + format (y) { + return vueInstance.getGraphYTicksFormatted(y) + } } } - } - }, - padding: { - top: 20, - left: 70, - right: 20 - }, - tooltip: createTooltip(this) - }); + }, + padding: { + top: 20, + left: 70, + right: 20 + }, + tooltip: createTooltip(this) + }); - this.updateGraphData(); - }, - components: { - svgGraphTabItem - } -}; - -let validateFunctions = { - '*': { - // Base rate > 0 - base: { - fn (value) { - return value > 0 + this.updateGraphData(); + }, + components: { + svgGraphTabItem + } + }; + + let validateFunctions = { + '*': { + // Base rate > 0 + base: { + fn (value) { + return value > 0 + }, + defaultVal: 10 }, - defaultVal: 10 - }, - // Power be between 0 and 100%(incl) - power: { - fn (value) { - return value >= 0 && value <= 100 + // Power be between 0 and 100%(incl) + power: { + fn (value) { + return value >= 0 && value <= 100 + }, + defaultVal: 80 }, - defaultVal: 80 - }, - days: { - fn (value) { - return value > 0 + days: { + fn (value) { + return value > 0 + }, + defaultVal: 14 }, - defaultVal: 14 - }, - nonInfThreshold: { - fn (value) { - return value > 0 + nonInfThreshold: { + fn (value) { + return value > 0 + }, + defaultVal: 0 }, - defaultVal: 0 - } - }, - gTest: { - // Limit Gtest rate input to be between 0.0001% and 99.999% - base: { - fn (value) { - return value >= 0.01 && value <= 99.9 + variants: { + fn (value) { + return Number.isInteger(value) && value > 1 + }, + defaultVal: 1 + } + }, + gTest: { + // Limit Gtest rate input to be between 0.0001% and 99.999% + base: { + fn (value) { + return value >= 0.01 && value <= 99.9 + } + } + }, + tTest: { + // Standard deviation > 0 + sdRate: { + fn (value) { + return value > 0 + }, + defaultVal: 10 } } }, - tTest: { - // Standard deviation > 0 - sdRate: { - fn (value) { - return value > 0 - }, - defaultVal: 10 + validationCache = {}; + + var pcBlockField = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return (_vm.enableEdit)?_c('span',{staticClass:"pc-value-field-wrapper",class:_vm.fieldWrapperClasses,on:{"click":function($event){return _vm.setFocus()}}},[_c('span',{staticClass:"pc-value-formatting pc-value--formatted",class:'pc-value-formatting-' + _vm.fieldProp,style:(_vm.fieldFormattedStyle),attrs:{"aria-hidden":"true","data-prefix":_vm.prefix,"data-suffix":_vm.suffix}},[_c('span',{ref:"pc-formatted-value",staticClass:"pc-value-display"},[_vm._v(_vm._s(_vm.formattedVal))])]),_vm._v(" "),_c('span',{staticClass:"pc-value-formatting",class:'pc-value-formatting-' + _vm.fieldProp,style:(_vm.fieldEditableStyle),attrs:{"data-prefix":_vm.prefix,"data-suffix":_vm.suffix}},[_c('span',{directives:[{name:"initialvalue",rawName:"v-initialvalue"}],ref:"pc-value",staticClass:"pc-value-display",attrs:{"data-test":_vm.isFocused,"contenteditable":!_vm.isReadOnly},on:{"focus":function($event){return _vm.setFocusStyle(true)},"blur":_vm.blur,"input":_vm.updateVal}})])]):_c('span',{staticClass:"pc-value-display pc-field-not-editable"},[_vm._v(" "+_vm._s(_vm.prefix)+" "),_c('strong',[_vm._v(_vm._s(_vm.formattedVal))]),_vm._v(" "+_vm._s(_vm.suffix)+" ")])},staticRenderFns: [], + template: '#pc-block-field', + props: [ + 'lockedField', + 'enableEdit', + 'isReadOnly', + 'fieldProp', + 'fieldValue', + 'prefix', + 'suffix', + 'fieldFromBlock' + ], + data () { + return { + islockedFieldSet: (this.lockedField || '').length > 0, + val: parseFloat(this.fieldValue), + isFocused: false, } - } - }; -let validationCache = {}; - -var pcBlockField = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return (_vm.enableEdit)?_c('span',{staticClass:"pc-value-field-wrapper",class:_vm.fieldWrapperClasses,on:{"click":function($event){_vm.setFocus();}}},[_c('span',{staticClass:"pc-value-formatting pc-value--formatted",class:'pc-value-formatting-' + _vm.fieldProp,style:(_vm.fieldFormattedStyle),attrs:{"aria-hidden":"true","data-prefix":_vm.prefix,"data-suffix":_vm.suffix}},[_c('span',{ref:"pc-formatted-value",staticClass:"pc-value-display"},[_vm._v(_vm._s(_vm.formattedVal))])]),_vm._v(" "),_c('span',{staticClass:"pc-value-formatting",class:'pc-value-formatting-' + _vm.fieldProp,style:(_vm.fieldEditableStyle),attrs:{"data-prefix":_vm.prefix,"data-suffix":_vm.suffix}},[_c('span',{directives:[{name:"initialvalue",rawName:"v-initialvalue"}],ref:"pc-value",staticClass:"pc-value-display",attrs:{"data-test":_vm.isFocused,"contenteditable":!_vm.isReadOnly},on:{"focus":function($event){_vm.setFocusStyle(true);},"blur":_vm.blur,"input":_vm.updateVal}})])]):_c('span',{staticClass:"pc-value-display pc-field-not-editable"},[_vm._v(" "+_vm._s(_vm.prefix)+" "),_c('strong',[_vm._v(_vm._s(_vm.formattedVal))]),_vm._v(" "+_vm._s(_vm.suffix)+" ")])},staticRenderFns: [], - template: '#pc-block-field', - props: [ - 'lockedField', - 'enableEdit', - 'isReadOnly', - 'fieldProp', - 'fieldValue', - 'prefix', - 'suffix', - 'fieldFromBlock' - ], - data () { - return { - islockedFieldSet: (this.lockedField || '').length > 0, - val: parseFloat(this.fieldValue), - isFocused: false, - } - }, - computed: { - isLocked () { - return this.lockedField && this.lockedField == this.fieldProp }, - formattedVal () { - let result = this.val; - let sep = ','; + computed: { + isLocked () { + return this.lockedField && this.lockedField == this.fieldProp + }, + formattedVal () { + let result = this.val; + let sep = ','; - if (result / 1000 >= 1) { - let [integer, decimal] = (result + '').split('.'); + if (result / 1000 >= 1) { + let [integer, decimal] = (result + '').split('.'); - result = integer.split('').reduceRight((prev, cur, i, arr) => { - let resultStr = cur + prev, - iFromLeft = arr.length - i; + result = integer.split('').reduceRight((prev, cur, i, arr) => { + let resultStr = cur + prev, + iFromLeft = arr.length - i; - if (iFromLeft % 3 == 0 && iFromLeft != 0 && i != 0) { - resultStr = sep + resultStr; - } + if (iFromLeft % 3 == 0 && iFromLeft != 0 && i != 0) { + resultStr = sep + resultStr; + } + + return resultStr; + }, ''); - return resultStr; - }, ''); + if (decimal) { + result += '.' + decimal; - if (decimal) { - result += '.' + decimal; + } } - } + return result; + }, + fieldWrapperClasses () { + let obj = {}; - return result; - }, - fieldWrapperClasses () { - let obj = {}; + obj['pc-field--read-only'] = this.isReadOnly; + obj['pc-field--focused'] = this.isFocused; + obj['pc-field-' + this.fieldProp] = true; - obj['pc-field--read-only'] = this.isReadOnly; - obj['pc-field--focused'] = this.isFocused; - obj['pc-field-' + this.fieldProp] = true; + return obj + }, + fieldFormattedStyle () { + let result = {}; + if (this.isFocused) { + result.display = 'none'; + } - return obj - }, - fieldFormattedStyle () { - let result = {}; - if (this.isFocused) { - result.display = 'none'; - } + return result + }, + fieldEditableStyle () { + let result = {}; + if (!this.isFocused) { + result.opacity = 0; + } - return result - }, - fieldEditableStyle () { - let result = {}; - if (!this.isFocused) { - result.opacity = 0; + return result + }, + testType () { + return this.$store.state.attributes.testType } - - return result }, - testType () { - return this.$store.state.attributes.testType - } - }, - methods: { - getSanitizedPcValue () { - // People will use copy paste. We need some data sanitization + methods: { + getSanitizedPcValue () { + // People will use copy paste. We need some data sanitization - // remove markup - let oldValue = this.$refs['pc-value'].textContent + '', - newValue; + // remove markup + let oldValue = this.$refs['pc-value'].textContent + '', + newValue; - // remove commas - newValue = oldValue.replace(/,/g, ''); + // remove commas + newValue = oldValue.replace(/,/g, ''); - // try to extract numbers from it - newValue = parseFloat(newValue); + // try to extract numbers from it + newValue = parseFloat(newValue); - newValue = this.validateField(newValue); + newValue = this.validateField(newValue); - return newValue + return newValue - }, - updateVal () { - if (this.enableEdit) { - let value = this.getSanitizedPcValue(); + }, + updateVal () { + if (this.enableEdit) { + let value = this.getSanitizedPcValue(); - if (value != this.val) { - this.val = value; + if (value != this.val) { + this.val = value; + } } - } - }, - formatDisplay () { - if (this.enableEdit) { - this.$refs['pc-value'].textContent = this.formatNumberFields(this.getSanitizedPcValue()); - } else { - this.val = this.formatNumberFields(this.val); - } - }, - formatNumberFields (value) { - let result = parseFloat(value); + }, + formatDisplay () { + if (this.enableEdit) { + this.$refs['pc-value'].textContent = this.formatNumberFields(this.getSanitizedPcValue()); + } else { + this.val = this.formatNumberFields(this.val); + } + }, + formatNumberFields (value) { + let result = parseFloat(value); - result = this.validateField(result); + result = this.validateField(result); - return result || 0 - }, + return result || 0 + }, - blur () { - this.formatDisplay(); - this.setFocusStyle(false); - }, - setFocusStyle (bool) { - this.isFocused = bool; - }, - setFocus () { - let el = this.$refs['pc-value']; - el.focus(); - }, - validateField (value) { - let validateConfigList = this.getValidationConfig(), - isValid = true, - result = value; + blur () { + this.formatDisplay(); + this.setFocusStyle(false); + }, + setFocusStyle (bool) { + this.isFocused = bool; + }, + setFocus () { + let el = this.$refs['pc-value']; + el.focus(); + }, + validateField (value) { + let validateConfigList = this.getValidationConfig(), + isValid = true, + result = value; - if (isNaN(result) || !isFinite(result)) { - result = validateConfigList.defaultVal; - } + if (isNaN(result) || !isFinite(result)) { + result = validateConfigList.defaultVal; + } - if (validateConfigList.fns.length > 0) { - validateConfigList.fns.forEach((fn) => { - if (!fn(result)) { - isValid = false; - } - }); - } + if (validateConfigList.fns.length > 0) { + validateConfigList.fns.forEach((fn) => { + if (!fn(result)) { + isValid = false; + } + }); + } - if (!isValid) { - result = validateConfigList.defaultVal; - } + if (!isValid) { + result = validateConfigList.defaultVal; + } - return result - }, - getValidationConfig () { - let {fieldProp, testtype} = this, - validationTypeCategories, - result; + return result + }, + getValidationConfig () { + let {fieldProp, testtype} = this, + validationTypeCategories, + result; - if (validationCache[testtype] && validationCache[testtype][fieldProp]) { - return validationCache[testtype][fieldProp] - } + if (validationCache[testtype] && validationCache[testtype][fieldProp]) { + return validationCache[testtype][fieldProp] + } - validationTypeCategories = [validateFunctions['*'], validateFunctions[testtype]].filter(Boolean); + validationTypeCategories = [validateFunctions['*'], validateFunctions[testtype]].filter(Boolean); - result = validationTypeCategories.reduce((prev, cur) => { - let {fn, defaultVal} = cur[fieldProp] || {}; + result = validationTypeCategories.reduce((prev, cur) => { + let {fn, defaultVal} = cur[fieldProp] || {}; - if (typeof fn != 'undefined') { - prev.fns.push(fn); - } - if (typeof defaultVal != 'undefined') { - prev.defaultVal = defaultVal; - } - return prev - }, {fns: [], defaultVal: 0}); + if (typeof fn != 'undefined') { + prev.fns.push(fn); + } + if (typeof defaultVal != 'undefined') { + prev.defaultVal = defaultVal; + } + return prev + }, {fns: [], defaultVal: 0}); - //cacheing - validationCache[testtype] = validationCache[testtype] || {}; - validationCache[testtype][fieldProp] = result; + //cacheing + validationCache[testtype] = validationCache[testtype] || {}; + validationCache[testtype][fieldProp] = result; - return result - }, - placeCaretAtEnd (el) { - if (typeof window.getSelection != "undefined" - && typeof document.createRange != "undefined") { - var range = document.createRange(); - range.selectNodeContents(el); - range.collapse(false); - var sel = window.getSelection(); - sel.removeAllRanges(); - sel.addRange(range); - } else if (typeof document.body.createTextRange != "undefined") { - var textRange = document.body.createTextRange(); - textRange.moveToElementText(el); - textRange.collapse(false); - textRange.select(); + return result + }, + placeCaretAtEnd (el) { + if (typeof window.getSelection != "undefined" + && typeof document.createRange != "undefined") { + var range = document.createRange(); + range.selectNodeContents(el); + range.collapse(false); + var sel = window.getSelection(); + sel.removeAllRanges(); + sel.addRange(range); + } else if (typeof document.body.createTextRange != "undefined") { + var textRange = document.body.createTextRange(); + textRange.moveToElementText(el); + textRange.collapse(false); + textRange.select(); + } } - } - }, - watch: { - val (newValue, oldValue) { - let newVal = parseFloat(this.formatNumberFields(newValue)), - oldVal = parseFloat(this.formatNumberFields(oldValue)); + }, + watch: { + val (newValue, oldValue) { + let newVal = parseFloat(this.formatNumberFields(newValue)), + oldVal = parseFloat(this.formatNumberFields(oldValue)); - if (this.isReadOnly || !this.isFocused || (newVal == oldVal)) { - return; - } + if (this.isReadOnly || !this.isFocused || (newVal == oldVal)) { + return; + } - // updating calculations - this.$store.dispatch('field:change', { - prop: this.fieldProp, - value: newVal || 0 - }); - }, - fieldValue () { - // in case some input field on the same block changes the main math values - // we need to update the input fiel - let anotherFieldInBlockIsUpdating = !this.isFocused; - if (!this.isReadOnly && !anotherFieldInBlockIsUpdating) { - return - } + // updating calculations + this.$store.dispatch('field:change', { + prop: this.fieldProp, + value: newVal || 0 + }); + }, + fieldValue () { + // in case some input field on the same block changes the main math values + // we need to update the input fiel + let anotherFieldInBlockIsUpdating = !this.isFocused; + if (!this.isReadOnly && !anotherFieldInBlockIsUpdating) { + return + } - this.val = this.fieldValue; - if (this.enableEdit) { - this.$refs['pc-value'].textContent = this.val; + this.val = this.fieldValue; + if (this.enableEdit) { + this.$refs['pc-value'].textContent = this.val; + } + }, + isFocused (newValue) { + this.$emit('update:focus', { + fieldProp: this.fieldFromBlock || this.fieldProp, + value: newValue + }); } }, - isFocused (newValue) { - this.$emit('update:focus', { - fieldProp: this.fieldFromBlock || this.fieldProp, - value: newValue - }); - } - }, - directives: { - initialvalue: { - inserted (el, directive, vnode) { - el.textContent = vnode.context.val; + directives: { + initialvalue: { + inserted (el, directive, vnode) { + el.textContent = vnode.context.val; + } } } - } -}; + }; -var pcSvgChain = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('svg',{staticClass:"pc-svg-chain-icon",style:(_vm.svgBgLine),attrs:{"width":"100%","height":"59px","viewBox":`0 0 ${_vm.svgBoxWidth} 59`,"version":"1.1","xmlns":"http://www.w3.org/2000/svg","xmlns:xlink":"http://www.w3.org/1999/xlink","aria-hidden":"true"}},[_c('g',{attrs:{"id":"Power-Calculator","transform":"translate(-811.000000, -485.000000)"}},[_c('g',{attrs:{"transform":"translate(676.000000, 327.000000)"}},[_c('g',{attrs:{"id":"Chain","transform":"translate(132.000000, 158.000000)"}},[_c('path',{attrs:{"d":"M12.1680332,24.5 L14.5,24.5 C16.709139,24.5 18.5,26.290861 18.5,28.5 L18.5,29.5 C18.5,31.709139 16.709139,33.5 14.5,33.5 L9.5,33.5 C7.290861,33.5 5.5,31.709139 5.5,29.5 L5.5,28.5 C6.20701437,28.5 6.87368104,28.5 7.5,28.5 L7.5,29.5 C7.5,30.6045695 8.3954305,31.5 9.5,31.5 L14.5,31.5 C15.6045695,31.5 16.5,30.6045695 16.5,29.5 L16.5,28.5 C16.5,27.3954305 15.6045695,26.5 14.5,26.5 L13.7237764,26.5 C13.463452,25.7924504 12.944871,25.1257837 12.1680332,24.5 Z","id":"Rectangle-5","fill":_vm.svgFillColor,"fill-rule":"nonzero","transform":"translate(12.000000, 29.000000) scale(-1, -1) translate(-12.000000, -29.000000) "}}),_vm._v(" "),_c('path',{attrs:{"d":"M18.1680332,24.5 L20.5,24.5 C22.709139,24.5 24.5,26.290861 24.5,28.5 L24.5,29.5 C24.5,31.709139 22.709139,33.5 20.5,33.5 L15.5,33.5 C13.290861,33.5 11.5,31.709139 11.5,29.5 L11.5,28.5 C12.2070144,28.5 12.873681,28.5 13.5,28.5 L13.5,29.5 C13.5,30.6045695 14.3954305,31.5 15.5,31.5 L20.5,31.5 C21.6045695,31.5 22.5,30.6045695 22.5,29.5 L22.5,28.5 C22.5,27.3954305 21.6045695,26.5 20.5,26.5 L19.7237764,26.5 C19.463452,25.7924504 18.944871,25.1257837 18.1680332,24.5 Z","id":"Rectangle-5","fill":_vm.svgFillColor,"fill-rule":"nonzero"}})])])])])},staticRenderFns: [], - props: ['fieldFromBlock'], - data () { - return { - svgBoxWidth: 26 - } - }, - computed: { - calculateProp () { - return this.$store.state.attributes.calculateProp - }, - svgFillColor () { - return this.calculateProp == this.fieldFromBlock ? '#E2B634' : '#C1CFD8' - }, - svgBgColor () { - return this.calculateProp == this.fieldFromBlock ? '#FEF1CB' : '#F0F0F0' + var pcSvgChain = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('svg',{staticClass:"pc-svg-chain-icon",style:(_vm.svgBgLine),attrs:{"width":"100%","height":"59px","viewBox":`0 0 ${_vm.svgBoxWidth} 59`,"version":"1.1","xmlns":"http://www.w3.org/2000/svg","xmlns:xlink":"http://www.w3.org/1999/xlink","aria-hidden":"true"}},[_c('g',{attrs:{"id":"Power-Calculator","transform":"translate(-811.000000, -485.000000)"}},[_c('g',{attrs:{"transform":"translate(676.000000, 327.000000)"}},[_c('g',{attrs:{"id":"Chain","transform":"translate(132.000000, 158.000000)"}},[_c('path',{attrs:{"d":"M12.1680332,24.5 L14.5,24.5 C16.709139,24.5 18.5,26.290861 18.5,28.5 L18.5,29.5 C18.5,31.709139 16.709139,33.5 14.5,33.5 L9.5,33.5 C7.290861,33.5 5.5,31.709139 5.5,29.5 L5.5,28.5 C6.20701437,28.5 6.87368104,28.5 7.5,28.5 L7.5,29.5 C7.5,30.6045695 8.3954305,31.5 9.5,31.5 L14.5,31.5 C15.6045695,31.5 16.5,30.6045695 16.5,29.5 L16.5,28.5 C16.5,27.3954305 15.6045695,26.5 14.5,26.5 L13.7237764,26.5 C13.463452,25.7924504 12.944871,25.1257837 12.1680332,24.5 Z","id":"Rectangle-5","fill":_vm.svgFillColor,"fill-rule":"nonzero","transform":"translate(12.000000, 29.000000) scale(-1, -1) translate(-12.000000, -29.000000) "}}),_vm._v(" "),_c('path',{attrs:{"d":"M18.1680332,24.5 L20.5,24.5 C22.709139,24.5 24.5,26.290861 24.5,28.5 L24.5,29.5 C24.5,31.709139 22.709139,33.5 20.5,33.5 L15.5,33.5 C13.290861,33.5 11.5,31.709139 11.5,29.5 L11.5,28.5 C12.2070144,28.5 12.873681,28.5 13.5,28.5 L13.5,29.5 C13.5,30.6045695 14.3954305,31.5 15.5,31.5 L20.5,31.5 C21.6045695,31.5 22.5,30.6045695 22.5,29.5 L22.5,28.5 C22.5,27.3954305 21.6045695,26.5 20.5,26.5 L19.7237764,26.5 C19.463452,25.7924504 18.944871,25.1257837 18.1680332,24.5 Z","id":"Rectangle-5","fill":_vm.svgFillColor,"fill-rule":"nonzero"}})])])])])},staticRenderFns: [], + props: ['fieldFromBlock'], + data () { + return { + svgBoxWidth: 26 + } }, - svgBgLine () { - let { svgFillColor, svgBgColor, svgBoxWidth } = this, - strokeWidth = 2, - stokeStyle = `linear-gradient( + computed: { + calculateProp () { + return this.$store.state.attributes.calculateProp + }, + svgFillColor () { + return this.calculateProp == this.fieldFromBlock ? '#E2B634' : '#C1CFD8' + }, + svgBgColor () { + return this.calculateProp == this.fieldFromBlock ? '#FEF1CB' : '#F0F0F0' + }, + svgBgLine () { + let { svgFillColor, svgBgColor, svgBoxWidth } = this, + strokeWidth = 2, + stokeStyle = `linear-gradient( 0deg, transparent, transparent calc(50% - ${strokeWidth/2}px - 1px), @@ -6123,7 +6087,7 @@ var pcSvgChain = {render: function(){var _vm=this;var _h=_vm.$createElement;var transparent calc(50% + ${strokeWidth/2}px + 1px), transparent 100% )`, - strokeMask = `linear-gradient( + strokeMask = `linear-gradient( 90deg, transparent, transparent calc(50% - ${svgBoxWidth/2}px - 1px), @@ -6134,1097 +6098,1247 @@ var pcSvgChain = {render: function(){var _vm=this;var _h=_vm.$createElement;var )`; - return `background: ${strokeMask}, ${stokeStyle};`.replace(/\n/g, ''); + return `background: ${strokeMask}, ${stokeStyle};`.replace(/\n/g, ''); + } } - } -}; + }; -var pcBlock = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div')},staticRenderFns: [], - props: ['fieldFromBlock'], - data () { - return { - // isCalculated: this.calculateProp == this.fieldFromBlock, - } - }, - computed: { - isCalculated: { - get () { - return this.$store.state.attributes.calculateProp == this.fieldFromBlock - }, - set () { - this.$store.dispatch('update:calculateprop', {value: this.fieldFromBlock}); + var pcBlock = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div')},staticRenderFns: [], + props: ['fieldFromBlock'], + data () { + return { + // isCalculated: this.calculateProp == this.fieldFromBlock, } }, + computed: { + isCalculated: { + get () { + return this.$store.state.attributes.calculateProp == this.fieldFromBlock + }, + set () { + this.$store.dispatch('update:calculateprop', {value: this.fieldFromBlock}); + } + }, - calculateProp () { - return this.$store.state.attributes.calculateProp - }, - testType () { - return this.$store.state.attributes.testType - } - }, - components: { - 'pc-block-field': pcBlockField, - 'pc-svg-chain': pcSvgChain, - } -}; - -var sampleComp = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--sample",class:{'pc-block-focused': _vm.isBlockFocused == 'sample', 'pc-block-to-calculate': _vm.calculateProp == 'sample'}},[_c('pc-svg-chain',{attrs:{"calculateProp":_vm.calculateProp,"fieldFromBlock":_vm.fieldFromBlock}}),_vm._v(" "),_c('label',{staticClass:"pc-calc-radio pc-calc-radio--sample",class:{'pc-calc-radio--active': _vm.isCalculated},attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.isCalculated),expression:"isCalculated"}],attrs:{"type":"radio"},domProps:{"value":true,"checked":_vm._q(_vm.isCalculated,true)},on:{"change":function($event){_vm.isCalculated=true;}}}),_vm._v(" "+_vm._s(_vm.isCalculated ? 'Calculating' : 'Calculate')+" ")]),_vm._v(" "),_c('div',{staticClass:"pc-header"},[_vm._v(" Sample Size ")]),_vm._v(" "),_c('ul',{staticClass:"pc-inputs"},[_c('li',{staticClass:"pc-input-item pc-input-left"},[_c('label',[_vm._m(0),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"sample","fieldValue":_vm.sample,"isReadOnly":_vm.calculateProp == 'sample',"enableEdit":_vm.enableEdit},on:{"update:focus":_vm.updateFocus}})],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-right pc-value-field--lockable",class:_vm.getLockedStateClass('visitorsPerDay')},[_c('label',[_vm._m(1),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"visitorsPerDay","fieldValue":_vm.visitorsPerDay,"isReadOnly":_vm.lockedField == 'visitorsPerDay',"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit,"lockedField":_vm.lockedField},on:{"update:focus":_vm.updateFocus}})],1),_vm._v(" "),_c('button',{staticClass:"pc-swap-button",attrs:{"type":"button"},on:{"click":_vm.switchLockedField}},[_c('svg',{attrs:{"width":"20px","height":"20px","viewBox":"0 0 20 20","version":"1.1","xmlns":"http://www.w3.org/2000/svg","xmlns:xlink":"http://www.w3.org/1999/xlink"}},[_c('desc',[_vm._v("Lock "+_vm._s(_vm.lockedField == 'days' ? 'number of days' : 'visitors per day'))]),_vm._v(" "),_c('defs',[_c('circle',{attrs:{"id":"path-1","cx":"13","cy":"13","r":"10"}}),_vm._v(" "),_c('filter',{attrs:{"x":"-5.0%","y":"-5.0%","width":"110.0%","height":"110.0%","filterUnits":"objectBoundingBox","id":"filter-2"}},[_c('feGaussianBlur',{attrs:{"stdDeviation":"0.5","in":"SourceAlpha","result":"shadowBlurInner1"}}),_vm._v(" "),_c('feOffset',{attrs:{"dx":"0","dy":"1","in":"shadowBlurInner1","result":"shadowOffsetInner1"}}),_vm._v(" "),_c('feComposite',{attrs:{"in":"shadowOffsetInner1","in2":"SourceAlpha","operator":"arithmetic","k2":"-1","k3":"1","result":"shadowInnerInner1"}}),_vm._v(" "),_c('feColorMatrix',{attrs:{"values":"0 0 0 0 0.489716199 0 0 0 0 0.489716199 0 0 0 0 0.489716199 0 0 0 0.5 0","type":"matrix","in":"shadowInnerInner1"}})],1)]),_vm._v(" "),_c('g',{attrs:{"id":"Page-1","stroke":"none","stroke-width":"1","fill":"none","fill-rule":"evenodd"}},[_c('g',{attrs:{"id":"Power-Calculator","transform":"translate(-550.000000, -522.000000)"}},[_c('g',{attrs:{"id":"Switcher","transform":"translate(547.000000, 519.000000)"}},[_c('g',{attrs:{"id":"Oval-3"}},[_c('use',{attrs:{"fill":"#EFEFEF","fill-rule":"evenodd","xlink:href":"#path-1"}}),_vm._v(" "),_c('use',{attrs:{"fill":"black","fill-opacity":"1","filter":"url(#filter-2)","xlink:href":"#path-1"}})]),_vm._v(" "),_c('g',{attrs:{"id":"Group","stroke-width":"1","fill-rule":"evenodd","transform":"translate(7.000000, 7.000000)","fill":"#155EAB"}},[_c('path',{attrs:{"d":"M4.5,4.20404051 L4.5,9.9127641 L2.5,9.9127641 L2.5,4.20404051 L0.5,4.20404051 L3.5,0.70872359 L6.5,4.20404051 L4.5,4.20404051 Z","id":"Combined-Shape"}}),_vm._v(" "),_c('path',{attrs:{"d":"M9.5,5.49531692 L9.5,11.2040405 L7.5,11.2040405 L7.5,5.49531692 L5.5,5.49531692 L8.5,2 L11.5,5.49531692 L9.5,5.49531692 Z","id":"Combined-Shape","transform":"translate(8.500000, 6.602020) scale(1, -1) translate(-8.500000, -6.602020) "}})])])])])])])]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-right-swap pc-value-field--lockable",class:_vm.getLockedStateClass('days')},[_c('label',[_c('pc-block-field',{attrs:{"fieldProp":"runtime","prefix":"","suffix":" days","fieldValue":_vm.runtime,"isReadOnly":_vm.lockedField == 'days',"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit,"lockedField":_vm.lockedField,"aria-label":"Days"},on:{"update:focus":_vm.updateFocus}})],1)])])],1)},staticRenderFns: [function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('span',{staticClass:"pc-input-title"},[_vm._v("Total # "),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v("of visitors")])])},function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('span',{staticClass:"pc-input-title"},[_vm._v("Daily # "),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v("of visitors")])])}],_scopeId: 'data-v-297fe272', - props: ['enableEdit', 'isBlockFocused', 'fieldFromBlock'], - template: '#sample-comp', - extends: pcBlock, - data () { - return { - variants: 2, - focusedBlock: '' - } - }, - computed: { - sample () { - return this.$store.state.attributes.sample - }, - visitorsPerDay () { - return this.$store.state.attributes.visitorsPerDay - }, - runtime () { - return this.$store.state.attributes.runtime + calculateProp () { + return this.$store.state.attributes.calculateProp + }, + testType () { + return this.$store.state.attributes.testType + } }, - lockedField () { - return this.$store.state.attributes.lockedField + components: { + 'pc-block-field': pcBlockField, + 'pc-svg-chain': pcSvgChain, } - }, - methods: { - updateFocus ({fieldProp, value}) { + }; - if (this.focusedBlock == fieldProp && value === false) { - this.focusedBlock = ''; - } else if (value === true) { - this.focusedBlock = fieldProp; + var sampleComp = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--sample",class:{'pc-block-focused': _vm.isBlockFocused == 'sample', 'pc-block-to-calculate': _vm.calculateProp == 'sample'}},[_c('pc-svg-chain',{attrs:{"calculateProp":_vm.calculateProp,"fieldFromBlock":_vm.fieldFromBlock}}),_vm._v(" "),_c('label',{staticClass:"pc-calc-radio pc-calc-radio--sample",class:{'pc-calc-radio--active': _vm.isCalculated},attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.isCalculated),expression:"isCalculated"}],attrs:{"type":"radio"},domProps:{"value":true,"checked":_vm._q(_vm.isCalculated,true)},on:{"change":function($event){_vm.isCalculated=true;}}}),_vm._v(" "+_vm._s(_vm.isCalculated ? 'Calculating' : 'Calculate')+" ")]),_vm._v(" "),_c('div',{staticClass:"pc-header"},[_vm._v(" Sample Size ")]),_vm._v(" "),_c('ul',{staticClass:"pc-inputs",class:{'pc-inputs-no-grid': _vm.onlyTotalVisitors}},[_c('li',{staticClass:"pc-input-item pc-input-left"},[_c('label',[_vm._m(0),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"sample","fieldValue":_vm.sample,"isReadOnly":_vm.calculateProp == 'sample',"enableEdit":_vm.enableEdit},on:{"update:focus":_vm.updateFocus}})],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-right pc-value-field--lockable",class:[_vm.getLockedStateClass('visitorsPerDay'), {'pc-hidden': _vm.onlyTotalVisitors}]},[_c('label',[_vm._m(1),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"visitorsPerDay","fieldValue":_vm.visitorsPerDay,"isReadOnly":_vm.lockedField == 'visitorsPerDay',"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit,"lockedField":_vm.lockedField},on:{"update:focus":_vm.updateFocus}})],1),_vm._v(" "),_c('button',{staticClass:"pc-swap-button",attrs:{"type":"button"},on:{"click":_vm.switchLockedField}},[_c('svg',{attrs:{"width":"20px","height":"20px","viewBox":"0 0 20 20","version":"1.1","xmlns":"http://www.w3.org/2000/svg","xmlns:xlink":"http://www.w3.org/1999/xlink"}},[_c('desc',[_vm._v("Lock "+_vm._s(_vm.lockedField == 'days' ? 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'pc-value-field--locked' : 'pc-value-field--unlocked' - } - } -}; + methods: { + updateFocus ({fieldProp, value}) { -var impactComp = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--impact",class:{'pc-block-focused': _vm.isBlockFocused, 'pc-block-to-calculate': _vm.calculateProp == 'impact'}},[_c('pc-svg-chain',{attrs:{"calculateProp":_vm.calculateProp,"fieldFromBlock":_vm.fieldFromBlock}}),_vm._v(" "),_c('label',{staticClass:"pc-calc-radio pc-calc-radio--impact",class:{'pc-calc-radio--active': _vm.isCalculated},attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.isCalculated),expression:"isCalculated"}],attrs:{"type":"radio"},domProps:{"value":true,"checked":_vm._q(_vm.isCalculated,true)},on:{"change":function($event){_vm.isCalculated=true;}}}),_vm._v(" "+_vm._s(_vm.isCalculated ? 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' Incremental trials per day': ' Incremental change in the metric per day')+" ")])],1)])])],1)},staticRenderFns: [], - extends: pcBlock, - template: '#impact-comp', - props: ['enableEdit', 'fieldFromBlock', 'isBlockFocused', 'isnoninferiority'], - data () { - return { - focusedBlock: '' - } - }, - computed: { - days () { - return this.$store.state.attributes.runtime - }, - base () { - return this.$store.state.attributes.base - }, - sample () { - return this.$store.state.attributes.sample - }, - impact () { - return this.$store.state.attributes.impact - }, - testType () { - return this.$store.state.attributes.testType - }, - isReadOnly () { - return this.calculateProp == 'impact' - }, - impactByMetricDisplay () { - return this.$store.getters.impactByMetricDisplay - }, - impactByMetricMinDisplay () { - return this.$store.getters.impactByMetricMinDisplay - }, - impactByMetricMaxDisplay () { - return this.$store.getters.impactByMetricMaxDisplay - }, - impactByVisitorsDisplay () { - return this.$store.getters.impactByVisitorsDisplay - }, - impactByVisitorsPerDayDisplay () { - return this.$store.getters.impactByVisitorsPerDayDisplay - } - }, - watch: { - isReadOnly () { - return this.calculateProp == 'impact' - } - }, - methods: { - updateFocus ({fieldProp, value}) { - if (this.focusedBlock == fieldProp && value === false) { - this.focusedBlock = ''; - } else if (value === true) { - this.focusedBlock = fieldProp; - } + if (this.focusedBlock == fieldProp && value === false) { + this.focusedBlock = ''; + } else if (value === true) { + this.focusedBlock = fieldProp; + } - this.$emit('update:focus', { - fieldProp: this.fieldFromBlock, - value: value - }); - }, - addPercentToString (str) { - let result = str; - if (this.testType == 'gTest') { - result += '%'; + this.$emit('update:focus', { + fieldProp: this.fieldFromBlock, + value: value + }); + }, + switchLockedField () { + this.$store.dispatch('switch:lockedfield'); + }, + getLockedStateClass (param) { + return this.lockedField == param ? 'pc-value-field--locked' : 'pc-value-field--unlocked' } - - return result } - } -}; + }; -var baseComp = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--base",class:{'pc-block-focused': _vm.focusedblock == 'base'}},[_c('pc-svg-chain',{attrs:{"fieldFromBlock":_vm.fieldFromBlock}}),_vm._v(" "),(_vm.testType == 'gTest')?_c('div',{staticClass:"pc-header"},[_vm._v(" Base Rate ")]):_c('div',{staticClass:"pc-header"},[_vm._v(" Base Average ")]),_vm._v(" "),_c('ul',{staticClass:"pc-inputs"},[_c('li',{staticClass:"pc-input-item pc-input-left"},[_c('label',[_c('span',{staticClass:"pc-input-title"},[_vm._v(_vm._s(_vm.testType == 'gTest' ? 'Base Rate' : 'Base Average')+" "),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v("conversion")])]),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"base","suffix":_vm.testType == 'gTest' ? 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'%' : '',"fieldValue":_vm.impactByMetricDisplay,"testType":_vm.testType,"isReadOnly":_vm.isReadOnly,"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit,"aria-label":"visitors with goals"},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),_c('span',{staticClass:"pc-input-details"},[_vm._v(" going from "+_vm._s(_vm.addPercentToString(_vm.base))+" to either "+_vm._s(_vm.addPercentToString(_vm.impactByMetricMinDisplay))+" or "+_vm._s(_vm.addPercentToString(_vm.impactByMetricMaxDisplay))+" ")])],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-bottom-left"},[_c('label',[_c('pc-block-field',{staticClass:"pc-input-field",attrs:{"fieldProp":"impactByVisitors","fieldValue":_vm.impactByVisitorsDisplay,"testType":_vm.testType,"isReadOnly":_vm.isReadOnly,"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit && _vm.calculateProp != 'sample'},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),_c('span',{staticClass:"pc-input-details"},[_vm._v(" "+_vm._s(_vm.testType == 'gTest' ? ' Incremental trials': ' Incremental change in the metric')+" ")])],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-bottom-right"},[_c('label',[_c('pc-block-field',{attrs:{"fieldProp":"impactByVisitorsPerDay","fieldValue":_vm.impactByVisitorsPerDayDisplay,"testType":_vm.testType,"isReadOnly":_vm.isReadOnly,"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit && _vm.calculateProp != 'sample'},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),_c('span',{staticClass:"pc-input-details"},[_vm._v(" "+_vm._s(_vm.testType == 'gTest' ? ' Incremental trials per day': ' Incremental change in the metric per day')+" ")])],1)])])],1)},staticRenderFns: [], + extends: pcBlock, + template: '#impact-comp', + props: ['enableEdit', 'fieldFromBlock', 'isBlockFocused', 'isnoninferiority'], + data () { + return { + focusedBlock: '' + } }, - visitorsWithGoals () { - return this.$store.getters.visitorsWithGoals - } - }, - methods: { - enableInput () { - this.$emit('edit:update', {prop: 'base'}); + computed: { + days () { + return this.$store.state.attributes.runtime + }, + base () { + return this.$store.state.attributes.base + }, + sample () { + return this.$store.state.attributes.sample + }, + impact () { + return this.$store.state.attributes.impact + }, + testType () { + return this.$store.state.attributes.testType + }, + isReadOnly () { + return this.calculateProp == 'impact' + }, + impactByMetricDisplay () { + return this.$store.getters.impactByMetricDisplay + }, + impactByMetricMinDisplay () { + return this.$store.getters.impactByMetricMinDisplay + }, + impactByMetricMaxDisplay () { + return this.$store.getters.impactByMetricMaxDisplay + }, + impactByVisitorsDisplay () { + return this.$store.getters.impactByVisitorsDisplay + }, + impactByVisitorsPerDayDisplay () { + return this.$store.getters.impactByVisitorsPerDayDisplay + } }, - updateFocus ({fieldProp, value}) { - if (this.focusedBlock == fieldProp && value === false) { - this.focusedBlock = ''; - } else if (value === true) { - this.focusedBlock = fieldProp; + watch: { + isReadOnly () { + return this.calculateProp == 'impact' } + }, + methods: { + updateFocus ({fieldProp, value}) { + if (this.focusedBlock == fieldProp && value === false) { + this.focusedBlock = ''; + } else if (value === true) { + this.focusedBlock = fieldProp; + } - this.$emit('update:focus', { - fieldProp: this.fieldFromBlock, - value: value - }); - } - - } -}; - -var pcTooltip = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-tooltip"},[_c('div',{on:{"mouseover":function($event){_vm.showTooltip(true);},"mouseout":function($event){_vm.showTooltip(false);}}},[_vm._t("text")],2),_vm._v(" "),_c('div',{directives:[{name:"show",rawName:"v-show",value:(_vm.isTooltipShown),expression:"isTooltipShown"}],staticClass:"tooltip-wrapper"},[_vm._t("tooltip")],2)])},staticRenderFns: [], - template: '#pc-tooltip', - data () { - return { - isTooltipShown: false - } - }, - methods: { - showTooltip (bool) { - this.isTooltipShown = bool; - } - } - -}; - -var nonInferiority = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-non-inferiority"},[_c('label',{staticClass:"pc-non-inf-label"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.enabled),expression:"enabled"}],attrs:{"type":"checkbox"},domProps:{"checked":Array.isArray(_vm.enabled)?_vm._i(_vm.enabled,null)>-1:(_vm.enabled)},on:{"change":function($event){var $$a=_vm.enabled,$$el=$event.target,$$c=$$el.checked?(true):(false);if(Array.isArray($$a)){var $$v=null,$$i=_vm._i($$a,$$v);if($$el.checked){$$i<0&&(_vm.enabled=$$a.concat([$$v]));}else{$$i>-1&&(_vm.enabled=$$a.slice(0,$$i).concat($$a.slice($$i+1)));}}else{_vm.enabled=$$c;}}}}),_vm._v(" Use non inferiority test ")])])},staticRenderFns: [], - props: [ 'lockedField' ], - data () { - return { - } - }, - computed: { - enabled: { - get () { - return this.$store.state.nonInferiority.enabled - }, - set (newValue) { - this.$store.dispatch('change:noninferiority', { - prop: 'enabled', - value: newValue + this.$emit('update:focus', { + fieldProp: this.fieldFromBlock, + value: value }); + }, + addPercentToString (str) { + let result = str; + if (this.testType == 'gTest') { + result += '%'; + } + + return result } - }, - isRelative () { - return this.$store.state.nonInferiority.selected == 'relative' } - } -}; + }; -var nonInferiorityComp = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--noninferiority",class:{'pc-block-focused': _vm.focusedblock == 'noninferiority'}},[_c('pc-svg-chain',{attrs:{"fieldFromBlock":_vm.fieldFromBlock}}),_vm._v(" "),_c('div',{staticClass:"pc-header"},[_vm._v(" Non Inferiority ")]),_vm._v(" "),_c('ul',{staticClass:"pc-inputs"},[_c('li',{staticClass:"pc-input-item pc-input-left"},[_c('label',[_c('span',{staticClass:"pc-input-title"},[_vm._v("Acceptable Cost "),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v(" "+_vm._s(_vm.isRelative ? 'relative difference of' : 'absolute impact per day of')+" ")])]),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"threshold","suffix":_vm.isRelative ? '%' : '',"fieldValue":_vm.threshold,"fieldFromBlock":_vm.fieldFromBlock,"isBlockFocused":_vm.isBlockFocused,"isReadOnly":_vm.isReadOnly,"enableEdit":true},on:{"update:focus":_vm.updateFocus}})],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-right"},[_c('label',[_c('span',{staticClass:"pc-input-title"},[_vm._v(" Type "+_vm._s(_vm.isRelative ? '' : '(per day)')+" "),_c('small',{staticClass:"pc-input-sub-title"})]),_vm._v(" "),_c('div',{staticClass:"pc-non-inf-select-wrapper"},[_c('select',{directives:[{name:"model",rawName:"v-model",value:(_vm.selected),expression:"selected"}],staticClass:"pc-non-inf-select",on:{"change":function($event){var $$selectedVal = Array.prototype.filter.call($event.target.options,function(o){return o.selected}).map(function(o){var val = "_value" in o ? o._value : o.value;return val}); _vm.selected=$event.target.multiple ? $$selectedVal : $$selectedVal[0];}}},_vm._l((_vm.options),function(option,index){return _c('option',{key:index,domProps:{"value":option.value}},[_vm._v(" "+_vm._s(option.text)+" ")])}))])])]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-left-bottom"},[_c('label',[_vm._m(0),_vm._v(" "),_c('div',{staticClass:"pc-non-inf-select-wrapper"},[_c('select',{directives:[{name:"model",rawName:"v-model",value:(_vm.expectedChange),expression:"expectedChange"}],staticClass:"pc-non-inf-select",on:{"change":function($event){var $$selectedVal = Array.prototype.filter.call($event.target.options,function(o){return o.selected}).map(function(o){var val = "_value" in o ? o._value : o.value;return val}); _vm.expectedChange=$event.target.multiple ? $$selectedVal : $$selectedVal[0];}}},[_c('option',{attrs:{"value":"nochange"}},[_vm._v(" No Change ")]),_vm._v(" "),_c('option',{attrs:{"value":"degradation"}},[_vm._v(" Degradation ")]),_vm._v(" "),_c('option',{attrs:{"value":"improvement"}},[_vm._v(" Improvement ")])])])])])])],1)},staticRenderFns: [function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('span',{staticClass:"pc-input-title no-sub-title"},[_vm._v(" Expected Change "),_c('small',{staticClass:"pc-input-sub-title"})])}], - props: ['enableEdit', 'fieldFromBlock', 'isBlockFocused'], - extends: pcBlock, - template: '#base-comp', - data () { - return { - focusedBlock: '', - options: [ - { - text: 'relative', - value: 'relative' - }, - { - text: 'absolute', - value: 'absolutePerDay' - } - ] - } - }, - computed: { - isReadOnly () { - return this.calculateProp == 'base' - }, - enabled () { - return this.$store.state.nonInferiority.enabled - }, - threshold () { - return this.$store.state.nonInferiority.threshold - }, - isRelative () { - return this.$store.state.nonInferiority.selected == 'relative' + var baseComp = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--base",class:{'pc-block-focused': _vm.focusedblock == 'base'}},[_c('pc-svg-chain',{attrs:{"fieldFromBlock":_vm.fieldFromBlock}}),_vm._v(" "),(_vm.testType == 'gTest')?_c('div',{staticClass:"pc-header"},[_vm._v(" Base Rate ")]):_c('div',{staticClass:"pc-header"},[_vm._v(" Base Average ")]),_vm._v(" "),_c('ul',{staticClass:"pc-inputs"},[_c('li',{staticClass:"pc-input-item pc-input-left"},[_c('label',[_c('span',{staticClass:"pc-input-title"},[_vm._v(_vm._s(_vm.testType == 'gTest' ? 'Base Rate' : 'Base Average')+" "),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v("conversion")])]),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"base","suffix":_vm.testType == 'gTest' ? '%' : '',"fieldValue":_vm.base,"isReadOnly":_vm.isReadOnly,"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit},on:{"update:focus":_vm.updateFocus}})],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-right"},[_c('label',[_vm._m(0),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"visitorsWithGoals","fieldValue":_vm.visitorsWithGoals,"fieldFromBlock":_vm.fieldFromBlock,"isBlockFocused":_vm.isBlockFocused,"isReadOnly":_vm.isReadOnly,"enableEdit":_vm.enableEdit && this.calculateProp != 'sample'},on:{"update:focus":_vm.updateFocus}})],1)]),_vm._v(" "),(_vm.testType == 'tTest')?_c('li',{staticClass:"pc-input-item pc-input-sd-rate"},[_c('label',[_c('pc-block-field',{attrs:{"prefix":"±","fieldProp":"sdRate","fieldFromBlock":"base","fieldValue":_vm.sdRate,"isReadOnly":_vm.isReadOnly,"isBlockFocused":_vm.isBlockFocused,"enableEdit":_vm.enableEdit},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),_c('span',{staticClass:"pc-input-details"},[_vm._v("Base Standard deviation")])],1)]):_vm._e()])],1)},staticRenderFns: [function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('span',{staticClass:"pc-input-title"},[_vm._v("Metric Totals"),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v("visitors reached goal")])])}],_scopeId: 'data-v-0d0bef2b', + props: ['enableEdit', 'fieldFromBlock', 'isBlockFocused'], + extends: pcBlock, + template: '#base-comp', + data () { + return { + focusedBlock: '', + } }, - selected: { - get () { - return this.$store.state.nonInferiority.selected + computed: { + isReadOnly () { + return this.calculateProp == 'base' }, - set (newValue) { - this.$store.dispatch('change:noninferiority', { - prop: 'selected', - value: newValue - }); + base () { + return this.$store.state.attributes.base + }, + sdRate () { + return this.$store.state.attributes.sdRate + }, + sample () { + return this.$store.state.attributes.sample + }, + testType () { + return this.$store.state.attributes.testType + }, + visitorsWithGoals () { + return this.$store.getters.visitorsWithGoals } }, - expectedChange: { - get () { - return this.$store.state.nonInferiority.expectedChange + methods: { + enableInput () { + this.$emit('edit:update', {prop: 'base'}); }, - set (newValue) { - this.$store.dispatch('field:change', { - prop: 'expectedChange', - value: newValue + updateFocus ({fieldProp, value}) { + if (this.focusedBlock == fieldProp && value === false) { + this.focusedBlock = ''; + } else if (value === true) { + this.focusedBlock = fieldProp; + } + + this.$emit('update:focus', { + fieldProp: this.fieldFromBlock, + value: value }); } - }, - }, - methods: { - enableInput () { - this.$emit('edit:update', {prop: 'base'}); - }, - updateFocus ({fieldProp, value}) { - if (this.focusedBlock == fieldProp && value === false) { - this.focusedBlock = ''; - } else if (value === true) { - this.focusedBlock = fieldProp; - } - this.$emit('update:focus', { - fieldProp: this.fieldFromBlock, - value: value - }); } + }; - } -}; - -var powerCalculator = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"power-calculator"},[_c('form',{staticClass:"pc-form",attrs:{"action":"."}},[_c('div',{staticClass:"pc-main-header"},[_c('div',{staticClass:"pc-test-type"},[_c('pc-tooltip',{staticClass:"pc-test-type-tooltip-wrapper"},[_c('label',{staticClass:"pc-test-type-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.testType),expression:"testType"}],attrs:{"type":"radio","name":"test-mode","value":"gTest","checked":"checked"},domProps:{"checked":_vm._q(_vm.testType,"gTest")},on:{"change":function($event){_vm.testType="gTest";}}}),_vm._v(" Binary Metric ")]),_vm._v(" "),_c('span',{attrs:{"slot":"tooltip"},slot:"tooltip"},[_vm._v(" A binary metric is one that can be only two values like 0 or 1, yes or no, converted or not converted ")])]),_vm._v(" "),_c('pc-tooltip',{staticClass:"pc-test-type-tooltip-wrapper"},[_c('label',{staticClass:"pc-test-type-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.testType),expression:"testType"}],attrs:{"type":"radio","name":"test-mode","value":"tTest"},domProps:{"checked":_vm._q(_vm.testType,"tTest")},on:{"change":function($event){_vm.testType="tTest";}}}),_vm._v(" Continuous Metric ")]),_vm._v(" "),_c('span',{attrs:{"slot":"tooltip"},slot:"tooltip"},[_vm._v(" A continuous metric is one that can be any number like time on site or the number of rooms sold ")])])],1),_vm._v(" "),_c('non-inferiority'),_vm._v(" "),_vm._m(0),_vm._v(" "),_c('label',{staticClass:"pc-false-positive"},[_c('pc-block-field',{staticClass:"pc-false-positive-input",class:{ 'pc-top-fields-error': _vm.falsePosRate > 10 },attrs:{"suffix":"%","fieldProp":"falsePosRate","fieldValue":_vm.falsePosRate,"enableEdit":true}}),_vm._v(" false positive rate ")],1),_vm._v(" "),_c('label',{staticClass:"pc-power"},[_c('pc-block-field',{staticClass:"pc-power-input",class:{ 'pc-top-fields-error': _vm.power < 80 },attrs:{"suffix":"%","fieldProp":"power","fieldValue":_vm.power,"enableEdit":true}}),_vm._v(" power ")],1)],1),_vm._v(" "),_c('div',{staticClass:"pc-blocks-wrapper",class:{'pc-blocks-wrapper-ttest': _vm.testType == 'tTest'}},[_c('base-comp',{attrs:{"fieldFromBlock":"base","isBlockFocused":_vm.focusedBlock == 'base',"enableEdit":_vm.enabledMainInputs.base},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),_c('sample-comp',{attrs:{"fieldFromBlock":"sample","enableEdit":_vm.enabledMainInputs.sample,"isBlockFocused":_vm.focusedBlock == 'sample'},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),(!_vm.nonInferiorityEnabled)?_c('impact-comp',{attrs:{"fieldFromBlock":"impact","enableEdit":_vm.enabledMainInputs.impact,"isBlockFocused":_vm.focusedBlock == 'impact'},on:{"update:focus":_vm.updateFocus}}):_vm._e(),_vm._v(" "),(_vm.nonInferiorityEnabled)?_c('non-inferiority-comp',{attrs:{"fieldFromBlock":"non-inferiority","enableEdit":_vm.enabledMainInputs['non-inferiority'],"isBlockFocused":_vm.focusedBlock == 'non-inferiority'},on:{"update:focus":_vm.updateFocus}}):_vm._e(),_vm._v(" "),_c('svg-graph')],1)])])},staticRenderFns: [function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-title"},[_vm._v("Power Calculator "),_c('sup',{staticStyle:{"color":"#F00","font-size":"11px"}},[_vm._v("BETA")])])}], - mounted () { - // start of application - this.$store.dispatch('init:calculator'); - }, - props: ['parentmetricdata'], - data () { - // values if parent component sends them - let importedData = this.parentmetricdata || {}; - - let data = { - focusedBlock: '', - - // false means the editable ones are the secondary mode (metric totals, days&daily trials and absolute impact) - enabledMainInputs: { - base: true, - sample: true, - impact: true, - power: true, - 'non-inferiority': true + var pcTooltip = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-tooltip"},[_c('div',{on:{"mouseover":function($event){return _vm.showTooltip(true)},"mouseout":function($event){return _vm.showTooltip(false)}}},[_vm._t("text")],2),_vm._v(" "),_c('div',{directives:[{name:"show",rawName:"v-show",value:(_vm.isTooltipShown),expression:"isTooltipShown"}],staticClass:"tooltip-wrapper"},[_vm._t("tooltip")],2)])},staticRenderFns: [], + template: '#pc-tooltip', + data () { + return { + isTooltipShown: false } - }; - - // mergeComponentData has no array support for now - return this.mergeComponentData(data, JSON.parse(JSON.stringify(importedData))); - }, - computed: { - disableBaseSecondaryInput () { - // only metric total is available and as it depends on sample this - // creates a circular dependency - // this also forces main input back - return this.calculateProp == 'sample' - }, - - // in case parent component needs this information - metricData () { - let result = { - testType: this.testType, - calculateProp: this.calculateProp, - view: this.view, - lockedField: this.lockedField, - nonInferiority: this.nonInferiority - }; - return JSON.parse(JSON.stringify(result)) - }, - - nonInferiorityEnabled () { - return this.$store.state.nonInferiority.enabled - }, - - nonInferioritySelected () { - return this.$store.state.nonInferiority.selected }, + methods: { + showTooltip (bool) { + this.isTooltipShown = bool; + } + } - falsePosRate () { - return this.$store.state.attributes.falsePosRate - }, + }; - power () { - return this.$store.state.attributes.power + var nonInferiority = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-non-inferiority"},[_c('label',{staticClass:"pc-non-inf-label"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.enabled),expression:"enabled"}],attrs:{"type":"checkbox"},domProps:{"checked":Array.isArray(_vm.enabled)?_vm._i(_vm.enabled,null)>-1:(_vm.enabled)},on:{"change":function($event){var $$a=_vm.enabled,$$el=$event.target,$$c=$$el.checked?(true):(false);if(Array.isArray($$a)){var $$v=null,$$i=_vm._i($$a,$$v);if($$el.checked){$$i<0&&(_vm.enabled=$$a.concat([$$v]));}else{$$i>-1&&(_vm.enabled=$$a.slice(0,$$i).concat($$a.slice($$i+1)));}}else{_vm.enabled=$$c;}}}}),_vm._v(" Use non inferiority test ")])])},staticRenderFns: [], + props: [ 'lockedField' ], + data () { + return { + } }, - testType: { - get () { - return this.$store.state.attributes.testType + computed: { + enabled: { + get () { + return this.$store.state.nonInferiority.enabled + }, + set (newValue) { + this.$store.dispatch('change:noninferiority', { + prop: 'enabled', + value: newValue + }); + } }, - set (newValue) { - this.$store.dispatch('field:change', { - prop: 'testType', - value: newValue || 0 - }); + isRelative () { + return this.$store.state.nonInferiority.selected == 'relative' } } + }; - }, - methods: { - updateFocus ({fieldProp, value}) { - if (this.focusedBlock == fieldProp && value === false) { - this.focusedBlock = ''; - } else if (value === true) { - this.focusedBlock = fieldProp; + var nonInferiorityComp = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-block pc-block--noninferiority",class:{'pc-block-focused': _vm.focusedblock == 'noninferiority'}},[_c('pc-svg-chain',{attrs:{"fieldFromBlock":_vm.fieldFromBlock}}),_vm._v(" "),_c('div',{staticClass:"pc-header"},[_vm._v(" Non Inferiority ")]),_vm._v(" "),_c('ul',{staticClass:"pc-inputs"},[_c('li',{staticClass:"pc-input-item pc-input-left"},[_c('label',[_vm._m(0),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"thresholdRelative","suffix":"%","fieldValue":_vm.thresholdRelative,"fieldFromBlock":_vm.fieldFromBlock,"isBlockFocused":_vm.isBlockFocused,"isReadOnly":_vm.isReadOnly,"enableEdit":true},on:{"update:focus":_vm.updateFocus}})],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-right"},[_c('label',[_vm._m(1),_vm._v(" "),_c('pc-block-field',{attrs:{"fieldProp":"thresholdAbsolute","suffix":"","fieldValue":_vm.thresholdAbsolute,"fieldFromBlock":_vm.fieldFromBlock,"isBlockFocused":_vm.isBlockFocused,"isReadOnly":_vm.isReadOnly,"enableEdit":true},on:{"update:focus":_vm.updateFocus}})],1)]),_vm._v(" "),_c('li',{staticClass:"pc-input-item pc-input-left-bottom"},[_c('label',[_vm._m(2),_vm._v(" "),_c('div',{staticClass:"pc-non-inf-select-wrapper"},[_c('select',{directives:[{name:"model",rawName:"v-model",value:(_vm.expectedChange),expression:"expectedChange"}],staticClass:"pc-non-inf-select",on:{"change":function($event){var $$selectedVal = Array.prototype.filter.call($event.target.options,function(o){return o.selected}).map(function(o){var val = "_value" in o ? o._value : o.value;return val}); _vm.expectedChange=$event.target.multiple ? $$selectedVal : $$selectedVal[0];}}},[_c('option',{attrs:{"value":"nochange"}},[_vm._v(" No Change ")]),_vm._v(" "),_c('option',{attrs:{"value":"degradation"}},[_vm._v(" Degradation ")]),_vm._v(" "),_c('option',{attrs:{"value":"improvement"}},[_vm._v(" Improvement ")])])])])])])],1)},staticRenderFns: [function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('span',{staticClass:"pc-input-title"},[_vm._v("Relative "),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v("change")])])},function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('span',{staticClass:"pc-input-title"},[_vm._v("Absolute "),_c('small',{staticClass:"pc-input-sub-title"},[_vm._v("impact per day")])])},function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('span',{staticClass:"pc-input-title no-sub-title"},[_vm._v(" Expected Change "),_c('small',{staticClass:"pc-input-sub-title"})])}], + props: ['enableEdit', 'fieldFromBlock', 'isBlockFocused'], + extends: pcBlock, + template: '#base-comp', + data () { + return { + focusedBlock: '', + options: [ + { + text: 'relative', + value: 'relative' + }, + { + text: 'absolute', + value: 'absolutePerDay' + } + ] } }, - mergeComponentData (base, toClone) { - // merges default data with imported one from parent component - let result = recursive(base, toClone); - - // no array support for now - function recursive (baseRef, cloneRef) { - Object.keys(cloneRef).forEach((prop) => { - if (typeof cloneRef[prop] == 'object') { - baseRef[prop] = recursive(baseRef[prop], cloneRef[prop]); - } else { - baseRef[prop] = cloneRef[prop]; - } - }); + computed: { + isReadOnly () { + return this.calculateProp == 'base' + }, + enabled () { + return this.$store.state.nonInferiority.enabled + }, + threshold () { + return this.$store.state.nonInferiority.threshold + }, + thresholdRelative () { + return this.$store.state.nonInferiority.thresholdRelative + }, + thresholdAbsolute () { + return this.$store.state.nonInferiority.thresholdAbsolute + }, + isRelative () { + return this.$store.state.nonInferiority.selected == 'relative' + }, + selected: { + get () { + return this.$store.state.nonInferiority.selected + }, + set (newValue) { + this.$store.dispatch('change:noninferiority', { + prop: 'selected', + value: newValue + }); + } + }, + expectedChange: { + get () { + return this.$store.state.nonInferiority.expectedChange + }, + set (newValue) { + this.$store.dispatch('field:change', { + prop: 'expectedChange', + value: newValue + }); + } + }, + }, + methods: { + enableInput () { + this.$emit('edit:update', {prop: 'base'}); + }, + updateFocus ({fieldProp, value}) { + if (this.focusedBlock == fieldProp && value === false) { + this.focusedBlock = ''; + } else if (value === true) { + this.focusedBlock = fieldProp; + } - return baseRef; + this.$emit('update:focus', { + fieldProp: this.fieldFromBlock, + value: value + }); } - return result; - } - }, - watch: { - // in case parent component needs this information - metricData () { - this.$emit('update:metricdata', this.metricData); - } - }, - components: { - 'svg-graph': svgGraph, - 'pc-block-field': pcBlockField, - 'pc-tooltip': pcTooltip, - 'sample-comp': sampleComp, - 'impact-comp' : impactComp, - 'base-comp': baseComp, - 'non-inferiority': nonInferiority, - 'non-inferiority-comp': nonInferiorityComp - - } -}; - -var actions = { - 'field:change' (context, { prop, value }) { - // add validations necessary here - - switch (prop) { + } + }; - // these 3 cases will call the same extra action - case 'base': - context.commit('field:change', { prop, value }); - if (context.state.nonInferiority.enabled === true && context.state.nonInferiority.selected == 'absolutePerDay') { - context.dispatch('change:noninferiorityimpact'); + var powerCalculator = {render: function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"power-calculator"},[_c('form',{staticClass:"pc-form",attrs:{"action":"."}},[_c('div',{staticClass:"pc-main-header"},[_c('div',{staticClass:"pc-controls-left"},[_c('div',{staticClass:"pc-test-type"},[_c('pc-tooltip',{staticClass:"pc-test-type-tooltip-wrapper"},[_c('label',{staticClass:"pc-test-type-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.testType),expression:"testType"}],attrs:{"type":"radio","name":"test-mode","value":"gTest","checked":"checked"},domProps:{"checked":_vm._q(_vm.testType,"gTest")},on:{"change":function($event){_vm.testType="gTest";}}}),_vm._v(" Binomial Metric ")]),_vm._v(" "),_c('span',{attrs:{"slot":"tooltip"},slot:"tooltip"},[_vm._v(" A binomial metric is one that can be only two values like 0 or 1, yes or no, converted or not converted ")])]),_vm._v(" "),_c('pc-tooltip',{staticClass:"pc-test-type-tooltip-wrapper"},[_c('label',{staticClass:"pc-test-type-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.testType),expression:"testType"}],attrs:{"type":"radio","name":"test-mode","value":"tTest"},domProps:{"checked":_vm._q(_vm.testType,"tTest")},on:{"change":function($event){_vm.testType="tTest";}}}),_vm._v(" Continuous Metric ")]),_vm._v(" "),_c('span',{attrs:{"slot":"tooltip"},slot:"tooltip"},[_vm._v(" A continuous metric is one that can be any number like time on site or the number of rooms sold ")])])],1),_vm._v(" "),_c('div',{staticClass:"pc-traffic-mode"},[_c('label',{staticClass:"pc-traffic-mode-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.trafficMode),expression:"trafficMode"}],attrs:{"type":"radio","name":"traffic-mode","value":"daily","checked":"checked","disabled":_vm.nonInferiorityEnabled},domProps:{"checked":_vm._q(_vm.trafficMode,"daily")},on:{"change":function($event){_vm.trafficMode="daily";}}}),_vm._v(" Daily traffic ")]),_vm._v(" "),_c('label',{staticClass:"pc-traffic-mode-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.trafficMode),expression:"trafficMode"}],attrs:{"type":"radio","name":"traffic-mode","value":"total","disabled":_vm.nonInferiorityEnabled},domProps:{"checked":_vm._q(_vm.trafficMode,"total")},on:{"change":function($event){_vm.trafficMode="total";}}}),_vm._v(" Total traffic ")])]),_vm._v(" "),_c('non-inferiority'),_vm._v(" "),_c('div',{staticClass:"pc-comparison-mode"},[_c('label',{staticClass:"pc-comparison-mode-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.comparisonMode),expression:"comparisonMode"}],attrs:{"type":"radio","name":"comparison-mode","value":"all","checked":"checked"},domProps:{"checked":_vm._q(_vm.comparisonMode,"all")},on:{"change":function($event){_vm.comparisonMode="all";}}}),_vm._v(" Base vs All variants ")]),_vm._v(" "),_c('label',{staticClass:"pc-traffic-mode-labels",attrs:{"slot":"text"},slot:"text"},[_c('input',{directives:[{name:"model",rawName:"v-model",value:(_vm.comparisonMode),expression:"comparisonMode"}],attrs:{"type":"radio","name":"comparison-mode","value":"one"},domProps:{"checked":_vm._q(_vm.comparisonMode,"one")},on:{"change":function($event){_vm.comparisonMode="one";}}}),_vm._v(" Base vs One variant ")])])],1),_vm._v(" "),_vm._m(0),_vm._v(" "),_c('div',{staticClass:"pc-controls-right"},[_c('label',{staticClass:"pc-variants"},[_c('pc-block-field',{staticClass:"pc-variants-input",attrs:{"fieldProp":"variants","prefix":"base + ","fieldValue":_vm.variants,"enableEdit":true}}),_vm._v(" variant"+_vm._s(_vm.variants > 1 ? 's' : '')+" ")],1),_vm._v(" "),_c('label',{staticClass:"pc-false-positive"},[_c('pc-block-field',{staticClass:"pc-false-positive-input",class:{ 'pc-top-fields-error': _vm.falsePosRate > 10 },attrs:{"suffix":"%","fieldProp":"falsePosRate","fieldValue":_vm.falsePosRate,"enableEdit":true}}),_vm._v(" false positive rate ")],1),_vm._v(" "),_c('label',{staticClass:"pc-power"},[_c('pc-block-field',{staticClass:"pc-power-input",class:{ 'pc-top-fields-error': _vm.power < 80 },attrs:{"suffix":"%","fieldProp":"power","fieldValue":_vm.power,"enableEdit":true}}),_vm._v(" power ")],1)])]),_vm._v(" "),_c('div',{staticClass:"pc-blocks-wrapper",class:{'pc-blocks-wrapper-ttest': _vm.testType == 'tTest'}},[_c('base-comp',{attrs:{"fieldFromBlock":"base","isBlockFocused":_vm.focusedBlock == 'base',"enableEdit":_vm.enabledMainInputs.base},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),_c('sample-comp',{attrs:{"fieldFromBlock":"sample","enableEdit":_vm.enabledMainInputs.sample,"isBlockFocused":_vm.focusedBlock == 'sample'},on:{"update:focus":_vm.updateFocus}}),_vm._v(" "),(!_vm.nonInferiorityEnabled)?_c('impact-comp',{attrs:{"fieldFromBlock":"impact","enableEdit":_vm.enabledMainInputs.impact,"isBlockFocused":_vm.focusedBlock == 'impact'},on:{"update:focus":_vm.updateFocus}}):_vm._e(),_vm._v(" "),(_vm.nonInferiorityEnabled)?_c('non-inferiority-comp',{attrs:{"fieldFromBlock":"non-inferiority","enableEdit":_vm.enabledMainInputs['non-inferiority'],"isBlockFocused":_vm.focusedBlock == 'non-inferiority'},on:{"update:focus":_vm.updateFocus}}):_vm._e(),_vm._v(" "),_c('svg-graph')],1)])])},staticRenderFns: [function(){var _vm=this;var _h=_vm.$createElement;var _c=_vm._self._c||_h;return _c('div',{staticClass:"pc-title"},[_vm._v("Power Calculator "),_c('sup',{staticStyle:{"color":"#F00","font-size":"11px"}},[_vm._v("BETA")])])}], + mounted () { + // start of application + this.$store.dispatch('init:calculator'); + }, + props: ['parentmetricdata'], + data () { + // values if parent component sends them + let importedData = this.parentmetricdata || {}; + + let data = { + focusedBlock: '', + + // false means the editable ones are the secondary mode (metric totals, days&daily trials and absolute impact) + enabledMainInputs: { + base: true, + sample: true, + impact: true, + power: true, + 'non-inferiority': true } + }; - context.dispatch('update:proptocalculate', context.getters.calculatedValues); - break; - - case 'sample': - case 'runtime': - case 'visitorsPerDay': - context.dispatch('sample:sideeffect', {prop, value}); - context.dispatch('update:proptocalculate', context.getters.calculatedValues); - break; - - case 'threshold': - context.dispatch('threshold:sideeffect', {prop, value}); - context.dispatch('change:noninferiorityimpact'); - break; - - case 'impactByMetricValue': - context.dispatch('convert:absoluteimpact', {prop, value}); - break; + // mergeComponentData has no array support for now + return this.mergeComponentData(data, JSON.parse(JSON.stringify(importedData))); + }, + computed: { + disableBaseSecondaryInput () { + // only metric total is available and as it depends on sample this + // creates a circular dependency + // this also forces main input back + return this.calculateProp == 'sample' + }, - case 'expectedChange': - context.commit('field:change', { prop, value }); - context.dispatch('change:noninferiorityimpact'); - break; + // in case parent component needs this information + metricData () { + let result = { + testType: this.testType, + calculateProp: this.calculateProp, + view: this.view, + lockedField: this.lockedField, + nonInferiority: this.nonInferiority, + comparisonMode: this.comparisonMode, + }; + return JSON.parse(JSON.stringify(result)) + }, - case 'visitorsWithGoals': - context.dispatch('convert:visitorswithgoals', {prop, value}); - break; + nonInferiorityEnabled () { + return this.$store.state.nonInferiority.enabled + }, - default: - context.commit('field:change', { prop, value }); + nonInferioritySelected () { + return this.$store.state.nonInferiority.selected + }, - // calculate new value for calculated prop - context.dispatch('update:proptocalculate', context.getters.calculatedValues); - break; - } - }, - 'change:noninferiority' (context, { prop, value }) { - // add validations necessary here - context.commit('change:noninferiority', { prop, value }); - context.dispatch('change:noninferiorityimpact'); + falsePosRate () { + return this.$store.state.attributes.falsePosRate + }, + power () { + return this.$store.state.attributes.power + }, + variants () { + return this.$store.state.attributes.variants + }, + comparisonMode: { + get () { + return this.$store.state.attributes.comparisonMode + }, + set (newValue) { + this.$store.dispatch('field:change', { + prop: 'comparisonMode', + value: newValue + }); + } + }, + testType: { + get () { + return this.$store.state.attributes.testType + }, + set (newValue) { + this.$store.dispatch('field:change', { + prop: 'testType', + value: newValue || 0 + }); + } + }, + trafficMode: { + get () { + if (this.$store.state.attributes.onlyTotalVisitors) { + return 'total'; + } - if (prop == 'enabled') { + return 'daily'; + }, + set (newValue) { + this.$store.dispatch('field:change', { + prop: 'onlyTotalVisitors', + value: newValue == 'total' + }); + } + } + }, + methods: { + updateFocus ({fieldProp, value}) { + if (this.focusedBlock == fieldProp && value === false) { + this.focusedBlock = ''; + } else if (value === true) { + this.focusedBlock = fieldProp; + } + }, + mergeComponentData (base, toClone) { + // merges default data with imported one from parent component + let result = recursive(base, toClone); + + // no array support for now + function recursive (baseRef, cloneRef) { + Object.keys(cloneRef).forEach((prop) => { + if (typeof cloneRef[prop] == 'object') { + baseRef[prop] = recursive(baseRef[prop], cloneRef[prop]); + } else { + baseRef[prop] = cloneRef[prop]; + } + }); - if (value === true) { - context.dispatch('field:change', { - prop: 'lockedField', - value: 'days' - }); + return baseRef; + } + return result; } - } else { - // update values based on nonInferiority.selected - context.dispatch('update:proptocalculate', context.getters.calculatedValues); - } - }, - 'change:noninferiorityimpact' (context) { - let impactValue = context.getters.nonInferiorityImpact; + }, + watch: { + // in case parent component needs this information + metricData () { + this.$emit('update:metricdata', this.metricData); + } + }, + components: { + 'svg-graph': svgGraph, + 'pc-block-field': pcBlockField, + 'pc-tooltip': pcTooltip, + 'sample-comp': sampleComp, + 'impact-comp' : impactComp, + 'base-comp': baseComp, + 'non-inferiority': nonInferiority, + 'non-inferiority-comp': nonInferiorityComp - if (context.state.nonInferiority.enabled === true) { - this.__impactBackup = context.state.attributes.impact; - } else { - impactValue = this.__impactBackup || 0; } + }; - context.dispatch('field:change', { - prop: 'impact', - value: impactValue - }); - }, - 'switch:lockedfield' (context) { - let newLockedField = context.state.attributes.lockedField == 'days' ? 'visitorsPerDay' : 'days'; + var actions = { + 'field:change' (context, { prop, value }) { + // add validations necessary here - if (context.state.nonInferiority.enabled === true) { - newLockedField = 'days'; - } + switch (prop) { - context.commit('switch:lockedfield', { - value: newLockedField - }); - }, - 'sample:sideeffect' (context, { prop, value }) { - const isSampleCalculated = context.state.attributes.calculateProp == 'sample'; - let lockedField = context.state.attributes.lockedField; - - let stateMachine = { - calculated: { - sample: false, - runtime: lockedField == 'days', - visitorsPerDay: lockedField != 'days', - }, - notCalculated: { - sample: true, - runtime: lockedField == 'days' && prop == 'sample', - visitorsPerDay: lockedField != 'days' && prop == 'sample', - } - }; + // these 3 cases will call the same extra action + case 'base': + context.commit('field:change', { prop, value }); + if (context.state.nonInferiority.enabled === true) { + if (context.state.nonInferiority.selected == 'absolutePerDay') { + context.dispatch('change:noninferiorityimpact'); + } + context.dispatch('convert:noninferioritythreshold', { prop, value }); + } - let input = stateMachine[isSampleCalculated ? 'calculated' : 'notCalculated']; + context.dispatch('update:proptocalculate', context.getters.calculatedValues); + break; - let data = { - sample: context.state.attributes.sample, - visitorsPerDay: context.state.attributes.visitorsPerDay, - runtime: context.state.attributes.runtime - }; + case 'sample': + case 'runtime': + case 'visitorsPerDay': + context.dispatch('sample:sideeffect', {prop, value}); + context.dispatch('update:proptocalculate', context.getters.calculatedValues); - // override of the prop changed by the action change:fields with the new value - data[prop] = value; + if (context.state.nonInferiority.enabled === true && prop == 'visitorsPerDay') { + context.dispatch('convert:noninferioritythreshold', { prop, value }); + } + break; - context.commit('sample:sideeffect', { - prop: prop, - value: data[prop] - }); + case 'thresholdRelative': + case 'thresholdAbsolute': + context.dispatch('threshold:sideeffect', {prop, value}); + context.dispatch('change:noninferiorityimpact'); + context.dispatch('convert:noninferioritythreshold', { prop, value }); + break; - // looks throught the stat machine list and updates all values based on data - Object.keys(input).forEach((key) => { - if (input[key] == true) { - if (key != prop) { - let result = 0; - if (key == 'runtime') { - result = Math.ceil(window.parseInt(data.sample) / data.visitorsPerDay); - result = isNaN(result) ? '-' : result; + case 'impactByMetricValue': + context.dispatch('convert:absoluteimpact', {prop, value}); + break; - } else if (key == 'visitorsPerDay') { - let isInvalid = false; - result = Math.floor(window.parseInt(data.sample) / data.runtime); - isInvalid = isNaN(result); + case 'expectedChange': + context.commit('field:change', { prop, value }); + context.dispatch('change:noninferiorityimpact'); + break; - result = isNaN(result) || isInvalid ? '-' : result; + case 'visitorsWithGoals': + context.dispatch('convert:visitorswithgoals', {prop, value}); + break; - } else if (key == 'sample') { - let isInvalid = false; - result = Math.ceil(data.runtime * data.visitorsPerDay); - isInvalid = isNaN(result); + default: + context.commit('field:change', { prop, value }); - result = isInvalid ? '-' : result; + if (context.state.nonInferiority.enabled === true) { + context.dispatch('convert:noninferioritythreshold', { prop, value }); } - context.commit('sample:sideeffect', { - prop: key, - value: result + // calculate new value for calculated prop + context.dispatch('update:proptocalculate', context.getters.calculatedValues); + break; + } + }, + 'change:noninferiority' (context, { prop, value }) { + // add validations necessary here + context.commit('change:noninferiority', { prop, value }); + context.dispatch('change:noninferiorityimpact'); + + if (prop == 'enabled') { + + if (value === true) { + context.dispatch('field:change', { + prop: 'lockedField', + value: 'days' + }); + context.dispatch('field:change', { + prop: 'onlyTotalVisitors', + value: false }); + context.dispatch('update:calculateprop', {value: 'sample'}); } + } else { + // update values based on nonInferiority.selected + context.dispatch('update:proptocalculate', context.getters.calculatedValues); } - }); - }, - 'threshold:sideeffect' (context, { prop, value }) { - context.commit('field:change', { prop, value }); - if (context.state.attributes.calculateProp == 'sample') { - context.dispatch('sample:sideeffect', context.getters.calculatedValues); - } - context.dispatch('update:proptocalculate', context.getters.calculatedValues); - }, - 'update:calculateprop' (context, { value }) { - context.commit('update:calculateprop', { value }); - }, - 'convert:absoluteimpact' (context, { prop, value }) { - let impactObj = { + }, + 'change:noninferiorityimpact' (context) { + let impactValue = context.getters.nonInferiorityImpact; + + if (context.state.nonInferiority.enabled === true) { + this.__impactBackup = context.state.attributes.impact; + context.dispatch('convert:noninferioritythreshold', { + prop: 'impact', + value: impactValue + }); + } else { + impactValue = this.__impactBackup || 0; + } + + context.dispatch('field:change', { prop: 'impact', - value: context.getters.displayValue('impact', context.getters.calculateImpactFromAbsoluteImpact(value)) + value: impactValue + }); + }, + 'switch:lockedfield' (context) { + let newLockedField = context.state.attributes.lockedField == 'days' ? 'visitorsPerDay' : 'days'; + + if (context.state.nonInferiority.enabled === true) { + newLockedField = 'days'; + } + + context.commit('switch:lockedfield', { + value: newLockedField + }); + }, + 'sample:sideeffect' (context, { prop, value }) { + const isSampleCalculated = context.state.attributes.calculateProp == 'sample'; + let lockedField = context.state.attributes.lockedField; + + let stateMachine = { + calculated: { + sample: false, + runtime: lockedField == 'days', + visitorsPerDay: lockedField != 'days', + }, + notCalculated: { + sample: true, + runtime: lockedField == 'days' && prop == 'sample', + visitorsPerDay: lockedField != 'days' && prop == 'sample', + } }; - context.dispatch('field:change', impactObj); - }, - 'convert:visitorswithgoals' (context, { prop, value }) { - let newValue = context.getters.baseFromVisitorsWithGoals(value); + let input = stateMachine[isSampleCalculated ? 'calculated' : 'notCalculated']; - context.dispatch('field:change', { - prop: 'base', - value: newValue - }); - }, - 'update:proptocalculate' (context) { - let calculatedObj = context.getters.calculatedValues; - context.commit('update:proptocalculate', calculatedObj); - - if (calculatedObj.prop == 'sample') { - // apply side effects - context.dispatch('sample:sideeffect', calculatedObj); - } - }, - 'init:calculator' (context) { - if (context.state.nonInferiority.enabled) { - context.dispatch('change:noninferiorityimpact'); - } + let data = { + sample: context.state.attributes.sample, + visitorsPerDay: context.state.attributes.visitorsPerDay, + runtime: context.state.attributes.runtime + }; - if (context.state.attributes.calculateProp != 'sample') { - context.dispatch('field:change', { - prop: 'sample', - value: context.state.attributes.sample - }); - } + // override of the prop changed by the action change:fields with the new value + data[prop] = value; - context.dispatch('update:proptocalculate'); - }, - 'test:reset' (context, stateObj) { - context.commit('test:reset', stateObj); - context.dispatch('init:calculator'); - } -}; + context.commit('sample:sideeffect', { + prop: prop, + value: data[prop] + }); -var mutations = { - 'update:proptocalculate' (state, {prop, value}) { - state.attributes[prop] = value; - } -}; + // looks throught the stat machine list and updates all values based on data + Object.keys(input).forEach((key) => { + if (input[key] == true) { + if (key != prop) { + let result = 0; + if (key == 'runtime') { + result = Math.ceil(window.parseInt(data.sample) / data.visitorsPerDay); + result = isNaN(result) ? '-' : result; -var attributes = { - state:{ - testType: 'gTest', - calculateProp: 'sample', // power, impact, base, sample + } else if (key == 'visitorsPerDay') { + let isInvalid = false; + result = Math.floor(window.parseInt(data.sample) / data.runtime); + isInvalid = isNaN(result); - sample: 561364, - base: 10, - impact: 2, - power: 80, - falsePosRate: 10, - sdRate: 10, + result = isNaN(result) || isInvalid ? '-' : result; - runtime: 14, //days + } else if (key == 'sample') { + let isInvalid = false; + result = Math.ceil(data.runtime * data.visitorsPerDay); + isInvalid = isNaN(result); - visitorsPerDay: Math.ceil(561364 / 14), - lockedField: 'days' - }, + result = isInvalid ? '-' : result; + } - mutations: { - 'field:change' (state, { prop, value }) { - if (typeof state[prop] != 'undefined') { - state[prop] = value; - } - }, - 'sample:sideeffect' (state, { prop, value }) { - state[prop] = value; - }, - 'switch:lockedfield' (state, { value }) { - state.lockedField = value; - }, - 'update:calculateprop' (state, { value }) { - state.calculateProp = value; - }, - 'test:reset' (state, stateObj) { - let props = Object.keys(state); - props.forEach((prop) => { - if (prop in stateObj) { - state[prop] = stateObj[prop]; + context.commit('sample:sideeffect', { + prop: key, + value: result + }); + } } }); - } - }, - - getters: { - visitorsPerDay (state, getters) { - return state.visitorsPerDay }, - lockedField (state, getters) { - return state.lockedField + 'threshold:sideeffect' (context, { prop, value }) { + context.commit('field:change', { prop, value }); + if (context.state.attributes.calculateProp == 'sample') { + context.dispatch('sample:sideeffect', context.getters.calculatedValues); + } + context.dispatch('update:proptocalculate', context.getters.calculatedValues); }, - runtime (state, getters) { - return state.runtime + 'update:calculateprop' (context, { value }) { + context.commit('update:calculateprop', { value }); }, - visitorsWithGoals (state, getters) { - let result = statFormulas.getVisitorsWithGoals({ - total_sample_size: getters.extractValue('sample'), - base_rate: getters.extractValue('base') - }); + 'convert:absoluteimpact' (context, { prop, value }) { + let impactObj = { + prop: 'impact', + value: context.getters.displayValue('impact', context.getters.calculateImpactFromAbsoluteImpact(value)) + }; - return getters.displayValue('metricTotals', result) + context.dispatch('field:change', impactObj); }, - impactByMetric (state, getters) { - return function impactByMetricInner (prop = 'value') { - let impactByMetricObj = statFormulas.getAbsoluteImpactInMetricHash({ - base_rate: getters.extractValue('base', state.base), - effect_size: getters.extractValue('impact', state.impact) - }); + 'convert:visitorswithgoals' (context, { prop, value }) { + let newValue = context.getters.baseFromVisitorsWithGoals(value); - return impactByMetricObj[prop]; - } - }, - impactByVisitors (state, getters) { - return statFormulas.getAbsoluteImpactInVisitors({ - total_sample_size: getters.extractValue('sample', state.sample), - base_rate: getters.extractValue('base', state.base), - effect_size: getters.extractValue('impact', state.impact) - }) - }, - impactByVisitorsPerDay (state, getters) { - return Math.floor(getters.impactByVisitors / state.runtime); - }, - impactByMetricDisplay (state, getters) { - return getters.displayValue('impactByMetricValue', getters.impactByMetric()); - }, - impactByMetricMinDisplay (state, getters) { - return getters.displayValue('impactByMetricValue', getters.impactByMetric('min')); - }, - impactByMetricMaxDisplay (state, getters) { - return getters.displayValue('impactByMetricValue', getters.impactByMetric('max')); + context.dispatch('field:change', { + prop: 'base', + value: newValue + }); }, - impactByVisitorsDisplay (state, getters) { - return getters.displayValue('impactByVisitors', getters.impactByVisitors) + 'convert:noninferioritythreshold' (context, { prop, value }) { + let valueToCalculate, + currentValue, + propToUpdate, + calculatedValue = 0; + + switch (prop) { + case 'thresholdRelative': + valueToCalculate = 'absolute'; + currentValue = value; + break; + + case 'thresholdAbsolute': + valueToCalculate = 'relative'; + currentValue = value; + break; + default: + if (context.state.nonInferiority.selected == 'absolutePerDay') { + valueToCalculate = 'relative'; + } else { + valueToCalculate = 'absolute'; + } + currentValue = context.state.nonInferiority.threshold; + break; + } + + if (valueToCalculate == 'absolute') { + calculatedValue = context.getters.displayValue('nonInfThresholdAbsolute', context.getters.calculateAbsoluteFromRelative(currentValue)); + calculatedValue = Math.round(calculatedValue * 100) / 100; + propToUpdate = 'thresholdAbsolute'; + } else if (valueToCalculate == 'relative') { + calculatedValue = context.getters.displayValue('nonInfThresholdRelative', context.getters.calculateRelativeFromAbsolute(currentValue)); + propToUpdate = 'thresholdRelative'; + } + + context.commit('change:noninferiority', { + prop: propToUpdate, + value: calculatedValue + }); }, - impactByVisitorsPerDayDisplay (state, getters) { - return getters.displayValue('impactByVisitorsPerDay', getters.impactByVisitorsPerDay) + 'update:proptocalculate' (context) { + let calculatedObj = context.getters.calculatedValues; + context.commit('update:proptocalculate', calculatedObj); + + if (calculatedObj.prop == 'sample') { + // apply side effects + context.dispatch('sample:sideeffect', calculatedObj); + } }, - calculateImpactFromAbsoluteImpact (state, getters) { - return function calculateImpactFromAbsoluteImpactInner (absolute_effect_size) { + 'init:calculator' (context) { + if (context.state.nonInferiority.enabled) { + context.dispatch('change:noninferiorityimpact'); + } - let absoluteImpact = getters.extractValue('impactByMetricValue', absolute_effect_size); - return statFormulas.getRelativeImpactFromAbsolute({ - absolute_effect_size: absoluteImpact, - base_rate: getters.extractValue('base', state.base) + if (context.state.attributes.calculateProp != 'sample') { + context.dispatch('field:change', { + prop: 'sample', + value: context.state.attributes.sample }); } + + context.dispatch('update:proptocalculate'); }, - baseFromVisitorsWithGoals (state, getters) { - return function baseFromVisitorsWithGoalsInner (value) { - return getters.displayValue( - 'base', - statFormulas.getBaseRate({ - total_sample_size: getters.extractValue('sample', state.sample), - visitors_with_goals: value - }) - ) - } + 'test:reset' (context, stateObj) { + context.commit('test:reset', stateObj); + context.dispatch('init:calculator'); } + }; - } -}; - -var nonInferiority$1 = { - state:{ - threshold: 0, - selected: 'relative', // relative, absolutePerDay - enabled: false, - expectedChange: 'nochange' // nochange, degradation, improvement - }, - mutations: { - 'field:change' (state, { prop, value }) { - if (typeof state[prop] != 'undefined') { - state[prop] = value; + var mutations = { + 'update:proptocalculate' (state, {prop, value}) { + state.attributes[prop] = value; } + }; + + var attributes = { + state:{ + testType: 'gTest', + calculateProp: 'sample', // power, impact, base, sample + + sample: 561364, + base: 10, + impact: 2, + power: 80, + falsePosRate: 10, + sdRate: 10, + variants: 1, + + runtime: 14, //days + + visitorsPerDay: Math.ceil(561364 / 14), + lockedField: 'days', + onlyTotalVisitors: false, + comparisonMode: 'all' }, - 'change:noninferiority' (state, {prop, value}) { - state[prop] = value; - }, - 'test:reset' (state, stateObj) { - let props = Object.keys(state); - props.forEach((prop) => { - if (prop in stateObj) { - state[prop] = stateObj[prop]; - } - }); - } - }, - getters: { - nonInferiorityImpact (state, getters, rootState) { - let { expectedChange, threshold, selected } = state, - newImpact = 0, - visitorsPerDay = rootState.attributes.visitorsPerDay, - base = getters.extractValue('base', rootState.attributes.base); - if (selected == 'absolutePerDay') { - threshold = threshold/(base*visitorsPerDay)*100; + mutations: { + 'field:change' (state, { prop, value }) { + if (typeof state[prop] != 'undefined') { + state[prop] = value; + } + }, + 'sample:sideeffect' (state, { prop, value }) { + state[prop] = value; + }, + 'switch:lockedfield' (state, { value }) { + state.lockedField = value; + }, + 'update:calculateprop' (state, { value }) { + state.calculateProp = value; + }, + 'test:reset' (state, stateObj) { + let props = Object.keys(state); + props.forEach((prop) => { + if (prop in stateObj) { + state[prop] = stateObj[prop]; + } + }); } - switch (expectedChange) { - case 'nochange': - default: - // zero - break; + }, - case 'degradation': - newImpact = -threshold/2; - break; + getters: { + visitorsPerDay (state, getters) { + return state.visitorsPerDay + }, + lockedField (state, getters) { + return state.lockedField + }, + runtime (state, getters) { + return state.runtime + }, + visitorsWithGoals (state, getters) { + let result = statFormulas.getVisitorsWithGoals({ + total_sample_size: getters.extractValue('sample'), + base_rate: getters.extractValue('base') + }); - case 'improvement': - newImpact = threshold; - break; + return getters.displayValue('metricTotals', result) + }, + impactByMetric (state, getters) { + return function impactByMetricInner (prop = 'value') { + let impactByMetricObj = statFormulas.getAbsoluteImpactInMetricHash({ + base_rate: getters.extractValue('base', state.base), + effect_size: getters.extractValue('impact', state.impact) + }); + + return impactByMetricObj[prop]; + } + }, + impactByVisitors (state, getters) { + return statFormulas.getAbsoluteImpactInVisitors({ + total_sample_size: getters.extractValue('sample', state.sample), + base_rate: getters.extractValue('base', state.base), + effect_size: getters.extractValue('impact', state.impact) + }) + }, + impactByVisitorsPerDay (state, getters) { + return Math.floor(getters.impactByVisitors / state.runtime); + }, + impactByMetricDisplay (state, getters) { + return getters.displayValue('impactByMetricValue', getters.impactByMetric()); + }, + impactByMetricMinDisplay (state, getters) { + return getters.displayValue('impactByMetricValue', getters.impactByMetric('min')); + }, + impactByMetricMaxDisplay (state, getters) { + return getters.displayValue('impactByMetricValue', getters.impactByMetric('max')); + }, + impactByVisitorsDisplay (state, getters) { + return getters.displayValue('impactByVisitors', getters.impactByVisitors) + }, + impactByVisitorsPerDayDisplay (state, getters) { + return getters.displayValue('impactByVisitorsPerDay', getters.impactByVisitorsPerDay) + }, + calculateImpactFromAbsoluteImpact (state, getters) { + return function calculateImpactFromAbsoluteImpactInner (absolute_effect_size) { + + let absoluteImpact = getters.extractValue('impactByMetricValue', absolute_effect_size); + return statFormulas.getRelativeImpactFromAbsolute({ + absolute_effect_size: absoluteImpact, + base_rate: getters.extractValue('base', state.base) + }); + } + }, + baseFromVisitorsWithGoals (state, getters) { + return function baseFromVisitorsWithGoalsInner (value) { + return getters.displayValue( + 'base', + statFormulas.getBaseRate({ + total_sample_size: getters.extractValue('sample', state.sample), + visitors_with_goals: value + }) + ) + } } - return newImpact - }, - mu (state, getters) { - return getters.customMu({ - runtime: getters.runtime, - thresholdCorrectedValue: getters.thresholdCorrectedValue, - visitors_per_day: getters.visitorsPerDay, - base_rate: getters.extractValue('base'), - }); + } + }; + + var nonInferiority$1 = { + state:{ + threshold: 0, + thresholdRelative: 0, + thresholdAbsolute: 0, + selected: 'relative', // relative, absolutePerDay + enabled: false, + expectedChange: 'nochange' // nochange, degradation, improvement }, - customMu (state, getters) { - return function customMuInner ({runtime, thresholdCorrectedValue, visitors_per_day, base_rate}) { - let mu = 0; - - if (state.enabled) { - let thresholdType = state.selected, - data = { - runtime, - threshold: -( getters.extractValue('nonInfThreshold', thresholdCorrectedValue) ), - visitors_per_day, - base_rate - }; - mu = { - absolutePerDay: statFormulas.getMuFromAbsolutePerDay, - relative: statFormulas.getMuFromRelativeDifference - }[thresholdType](data); + mutations: { + 'field:change' (state, { prop, value }) { + switch (prop) { + case 'thresholdRelative': + state.threshold = value; + state.selected = 'relative'; + break; + case 'thresholdAbsolute': + state.threshold = value; + state.selected = 'absolutePerDay'; + break; + case 'threshold': + if (state.selected == 'absolutePerDay') { + state.thresholdAbsolute = value; + } else { + state.thresholdRelative = value; + } + break; + case 'selected': + if (value == 'absolutePerDay') { + state.thresholdAbsolute = state.threshold; + } else { + state.thresholdRelative = state.threshold; + } + break; + default: + + break; } - return mu + if (typeof state[prop] != 'undefined') { + state[prop] = value; + } + }, + 'change:noninferiority' (state, {prop, value}) { + state[prop] = value; + }, + 'test:reset' (state, stateObj) { + let props = Object.keys(state); + props.forEach((prop) => { + if (prop in stateObj) { + state[prop] = stateObj[prop]; + } + }); } }, - opts (state, getters) { - if (!state.enabled) { - return false - } + getters: { + nonInferiorityImpact (state, getters, rootState) { + let { expectedChange, threshold, selected } = state, + newImpact = 0, + visitorsPerDay = rootState.attributes.visitorsPerDay, + base = getters.extractValue('base', rootState.attributes.base); + + if (selected == 'absolutePerDay') { + threshold = threshold/(base*visitorsPerDay)*100; + } + switch (expectedChange) { + case 'nochange': + default: + // zero + break; + + case 'degradation': + newImpact = -threshold/2; + break; + + case 'improvement': + newImpact = threshold; + break; + } - let opts = { - selected: state.selected, - lockedField: getters.lockedField, - thresholdCorrectedValue: getters.thresholdCorrectedValue, - runtime: getters.runtime, - visitorsPerDay: getters.visitorsPerDay, - }; + return newImpact + }, + mu (state, getters) { + return getters.customMu({ + runtime: getters.runtime, + thresholdCorrectedValue: getters.thresholdCorrectedValue, + visitors_per_day: getters.visitorsPerDay, + base_rate: getters.extractValue('base'), + }); + }, + customMu (state, getters) { + return function customMuInner ({runtime, thresholdCorrectedValue, visitors_per_day, base_rate}) { + let mu = 0; + + if (state.enabled) { + let thresholdType = state.selected, + data = { + runtime, + threshold: -( getters.extractValue('nonInfThreshold', thresholdCorrectedValue) ), + visitors_per_day, + base_rate + }; + mu = { + absolutePerDay: statFormulas.getMuFromAbsolutePerDay, + relative: statFormulas.getMuFromRelativeDifference + }[thresholdType](data); + } - return getters.customOpts(opts); - }, - customOpts (state, getters) { - return function customOptsInner ({ selected, lockedField, thresholdCorrectedValue, runtime, visitorsPerDay }) { - let type = selected, - opts; - - opts = { - type, - calculating: lockedField, - threshold: -( getters.extractValue('nonInfThreshold', thresholdCorrectedValue) ), + return mu + } + }, + opts (state, getters) { + if (!state.enabled) { + return false + } + + let opts = { + selected: state.selected, + lockedField: getters.lockedField, + thresholdCorrectedValue: getters.thresholdCorrectedValue, + runtime: getters.runtime, + visitorsPerDay: getters.visitorsPerDay, }; - if (type == 'absolutePerDay') { - if (lockedField == 'visitorsPerDay') { - opts = Object.assign( - opts, - { - days: runtime - } - ); - } else { - opts = Object.assign( - opts, - { - visitors_per_day: getters.extractValue('sample', visitorsPerDay) - } - ); + return getters.customOpts(opts); + }, + customOpts (state, getters) { + return function customOptsInner ({ selected, lockedField, thresholdCorrectedValue, runtime, visitorsPerDay }) { + let type = selected, + opts; + + opts = { + type, + calculating: lockedField, + threshold: -( getters.extractValue('nonInfThreshold', thresholdCorrectedValue) ), + }; + + if (type == 'absolutePerDay') { + if (lockedField == 'visitorsPerDay') { + opts = Object.assign( + opts, + { + days: runtime + } + ); + } else { + opts = Object.assign( + opts, + { + visitors_per_day: getters.extractValue('sample', visitorsPerDay) + } + ); + } } + + return opts } - return opts - } + }, + alternative (state, getters) { + if (!state.enabled) { + // in this case the test type would be 'comparative' + return false + } - }, - alternative (state, getters) { - if (!state.enabled) { - // in this case the test type would be 'comparative' - return false + return getters.customAlternative({type: 'noninferiority'}); + }, + customAlternative (state, getters) { + return function customAlternativeInner ({type}) { + return statFormulas.getAlternative({type}); + } + }, + thresholdCorrectedValue (state, getters) { + let { threshold, selected } = state; + + return getters.customThresholdCorrectedValue({ threshold, selected }); + }, + customThresholdCorrectedValue (state) { + return function customThresholdCorrectedValueInner ({ threshold, selected }) { + // when relative is selected the value we will convert it to + // percentage + + let nonInfThreshold = threshold; + const isRelative = selected == 'relative'; + + let result = nonInfThreshold; + if (isRelative) { + result = result / 100; + } + + return result; + } + }, + calculateRelativeFromAbsolute (state, getters, rootState) { + return function caclulateRelativeFromAbsoluteInner(absoluteThreshold) { + let visitorsPerDay = rootState.attributes.visitorsPerDay, + base = getters.extractValue('base', rootState.attributes.base); + + return absoluteThreshold/(base*visitorsPerDay); + } + }, + calculateAbsoluteFromRelative (state, getters, rootState) { + return function calculateAbsoluteFromRelativeInner(relativeThreshold) { + let visitorsPerDay = rootState.attributes.visitorsPerDay, + base = getters.extractValue('base', rootState.attributes.base); + + return (relativeThreshold*base*visitorsPerDay)/100; + } } + } + }; - return getters.customAlternative({type: 'noninferiority'}); + // getters to present data and format for calculations + let validations = { + sample: {type: 'int'}, + base: { + gTest: {type: 'percentage'}, + tTest: {type: 'float'} }, - customAlternative (state, getters) { - return function customAlternativeInner ({type}) { - return statFormulas.getAlternative({type}); - } + impact: {type: 'percentage'}, + runtime: {type: 'int'}, + variants: {type: 'int'}, + power: {type: 'percentage'}, + falsePosRate: {type: 'percentage'}, + impactByMetricValue: { + gTest: {type: 'percentage'}, + tTest: {type: 'float'} }, - thresholdCorrectedValue (state, getters) { - let { threshold, selected } = state; + impactByVisitors: {type: 'int'}, + impactByVisitorsPerDay: {type: 'int'}, + metricTotals: {type: 'int'}, + sdRate: {type: 'float'}, + nonInfThreshold: {type: 'float'}, + nonInfThresholdRelative: {type: 'percentage'}, + nonInfThresholdAbsolute: {type: 'float'}, + }; + + // add validation for component version of main data + validations.totalSample = validations.sample; + validations.relativeImpact = validations.impact; + validations.baseRate = validations.base; + - return getters.customThresholdCorrectedValue({ threshold, selected }); - }, - customThresholdCorrectedValue (state) { - return function customThresholdCorrectedValueInner ({ threshold, selected }) { - // when relative is selected the value we will convert it to - // percentage - let nonInfThreshold = threshold; - const isRelative = selected == 'relative'; + var dataFormat = { + displayValue (state) { + return function displayValueInner (prop, value) { + let result = value, + type = getType(prop, 'displayValue', state.attributes.testType); - let result = nonInfThreshold; - if (isRelative) { - result = result / 100; + if (type == 'int') { + result = window.parseInt(result); } - return result; - } - } - } -}; - -// getters to present data and format for calculations -let validations = { - sample: {type: 'int'}, - base: { - gTest: {type: 'percentage'}, - tTest: {type: 'float'} - }, - impact: {type: 'percentage'}, - runtime: {type: 'int'}, - power: {type: 'percentage'}, - falsePosRate: {type: 'percentage'}, - impactByMetricValue: { - gTest: {type: 'percentage'}, - tTest: {type: 'float'} - }, - impactByVisitors: {type: 'int'}, - impactByVisitorsPerDay: {type: 'int'}, - metricTotals: {type: 'int'}, - sdRate: {type: 'float'}, - nonInfThreshold: {type: 'float'} -}; - -// add validation for component version of main data -validations.totalSample = validations.sample; -validations.relativeImpact = validations.impact; -validations.baseRate = validations.base; - - - -var dataFormat = { - displayValue (state) { - return function displayValueInner (prop, value) { - let result = value, - type = getType(prop, 'displayValue', state.attributes.testType); - - if (type == 'int') { - result = window.parseInt(result); - } + if (type == 'float') { + result = window.parseFloat(result); + } - if (type == 'float') { - result = window.parseFloat(result); - } + if (type == 'percentage') { + result = (window.parseFloat(result) * 100).toFixed(2); + result = +result.toString(); + } - if (type == 'percentage') { - result = (window.parseFloat(result) * 100).toFixed(2); - result = +result.toString(); + return isNaN(result) || !isFinite(result) ? '-' : result; } + }, + extractValue (state) { + return function extractValueInner (prop, value) { // value is option and is used when we don't want to update the state + let result = typeof value == 'undefined' ? state.attributes[prop] : value, + type = getType(prop, 'extractValue', state.attributes.testType); - return isNaN(result) || !isFinite(result) ? '-' : result; - } - }, - extractValue (state) { - return function extractValueInner (prop, value) { // value is option and is used when we don't want to update the state - let result = typeof value == 'undefined' ? state.attributes[prop] : value, - type = getType(prop, 'extractValue', state.attributes.testType); + if (type == 'int') { + return window.parseInt(result); + } - if (type == 'int') { - return window.parseInt(result); - } + if (type == 'float') { + return window.parseFloat(result); + } - if (type == 'float') { - return window.parseFloat(result); + if (type == 'percentage') { + return window.parseFloat(result) / 100; + } } + } + }; - if (type == 'percentage') { - return window.parseFloat(result) / 100; + function getType (prop, methodName, testType) { + let validationConfig = validations[prop], + result, + throwError = false; + + if (validationConfig) { + if (validationConfig.type) { + result = validationConfig.type; + } else if (validationConfig[testType] && validationConfig[testType].type) { + result = validationConfig[testType].type; + } else { + throwError = true; } - } - } -}; - -function getType (prop, methodName, testType) { - let validationConfig = validations[prop], - result, - throwError = false; - - if (validationConfig) { - if (validationConfig.type) { - result = validationConfig.type; - } else if (validationConfig[testType] && validationConfig[testType].type) { - result = validationConfig[testType].type; } else { throwError = true; } - } else { - throwError = true; - } - if (throwError) { - throw new Error(`Type not found for "${prop}" when trying to call "${methodName}".`) - } + if (throwError) { + throw new Error(`Type not found for "${prop}" when trying to call "${methodName}".`) + } - return result || '' -} + return result || '' + } -var math = { - calculatedValues (state, getters) { - let prop = state.attributes.calculateProp, - value = getters.formulaToSolve(getters.convertDisplayedValues); - return { - prop, - value: getters.displayValue(prop, value) - }; - }, - formulaToSolve (state, getters) { - let calculateProp = state.attributes.calculateProp; + var math = { + calculatedValues (state, getters) { + let prop = state.attributes.calculateProp, + value = getters.formulaToSolve(getters.convertDisplayedValues); + return { + prop, + value: getters.displayValue(prop, value) + }; + }, + formulaToSolve (state, getters) { + let calculateProp = state.attributes.calculateProp; - return getters.formulaToSolveProp[calculateProp]; - }, - formulaToSolveProp (state, getters) { - // used for the graph as we need to pass many different values to dynamic attributes - let testType = state.attributes.testType; + return getters.formulaToSolveProp[calculateProp]; + }, + formulaToSolveProp (state, getters) { + // used for the graph as we need to pass many different values to dynamic attributes + let testType = state.attributes.testType; - return statFormulas[testType]; - }, - convertDisplayedValues (state, getters) { - let { mu, opts, alternative } = getters; + return statFormulas[testType]; + }, + convertDisplayedValues (state, getters) { + let { mu, opts, alternative } = getters; - return { - mu, - opts, - alternative, - total_sample_size: getters.extractValue('sample'), - base_rate: getters.extractValue('base'), - effect_size: getters.extractValue('impact'), - alpha: getters.extractValue('falsePosRate'), - beta: 1 - getters.extractValue('power'), // power of 80%, beta is actually 20% - sd_rate: getters.extractValue('sdRate') + return { + mu, + opts, + alternative, + variants: getters.extractValue('variants'), + total_sample_size: getters.extractValue('sample'), + base_rate: getters.extractValue('base'), + effect_size: getters.extractValue('impact'), + alpha: state.attributes.comparisonMode === 'all' ? statFormulas.getCorrectedAlpha(getters.extractValue('falsePosRate'), getters.extractValue('variants')) : getters.extractValue('falsePosRate'), + beta: 1 - getters.extractValue('power'), // power of 80%, beta is actually 20% + sd_rate: getters.extractValue('sdRate') + } } - } -}; - -var store = { - modules: { - attributes, - nonInferiority: nonInferiority$1, - }, - actions, - mutations, - getters: Object.assign({}, dataFormat, math) -}; - -var index = { - powerCalculator, - store -}; - -return index; - -}))); + }; + + var store = { + modules: { + attributes, + nonInferiority: nonInferiority$1, + }, + actions, + mutations, + getters: Object.assign({}, dataFormat, math) + }; + + var index = { + powerCalculator, + store + }; + + return index; + +})); diff --git a/src/components/impact-comp.vue b/src/components/impact-comp.vue index a02b092..d34f0aa 100644 --- a/src/components/impact-comp.vue +++ b/src/components/impact-comp.vue @@ -93,6 +93,7 @@ {{ testType == 'gTest' ? ' Incremental trials per day': ' Incremental change in the metric per day' }} + diff --git a/src/components/non-inferiority-comp.vue b/src/components/non-inferiority-comp.vue index acf2e17..862919d 100644 --- a/src/components/non-inferiority-comp.vue +++ b/src/components/non-inferiority-comp.vue @@ -10,20 +10,13 @@
    • @@ -114,6 +103,12 @@ export default { threshold () { return this.$store.state.nonInferiority.threshold }, + thresholdRelative () { + return this.$store.state.nonInferiority.thresholdRelative + }, + thresholdAbsolute () { + return this.$store.state.nonInferiority.thresholdAbsolute + }, isRelative () { return this.$store.state.nonInferiority.selected == 'relative' }, diff --git a/src/components/pc-block-field.vue b/src/components/pc-block-field.vue index 144140b..7e4c64f 100644 --- a/src/components/pc-block-field.vue +++ b/src/components/pc-block-field.vue @@ -61,6 +61,12 @@ let validateFunctions = { return value > 0 }, defaultVal: 0 + }, + variants: { + fn (value) { + return Number.isInteger(value) && value > 1 + }, + defaultVal: 1 } }, gTest: { @@ -376,7 +382,8 @@ export default { .pc-non-inf-treshold-input, .pc-power-input, -.pc-false-positive-input { +.pc-false-positive-input, +.pc-variants-input { display: inline-block; vertical-align: middle; padding: 4px 8px; @@ -387,6 +394,10 @@ export default { font-size: inherit; } +.pc-variants-input { + width: 6.5em; +} + .pc-top-fields-error { color: var(--red); } diff --git a/src/components/sample-comp.vue b/src/components/sample-comp.vue index 38e65fa..d05b5a3 100644 --- a/src/components/sample-comp.vue +++ b/src/components/sample-comp.vue @@ -13,10 +13,10 @@ -
      +
        • -
      • +
        • -
      • +