@tracerbench/stats - npm Package Compare versions

Comparing version 4.5.3 to 4.5.5

dist/src/confidence-interval.d.ts

@@ -8,3 +8,3 @@ /**
 /**
- * Apply the passed "_func" to the permutations of the items in listOne and listTwo
+ * Apply the passed "func" to the permutations of the items in listOne and listTwo
  *
@@ -23,4 +23,11 @@ * @param listOne - Array of numbers
  */
-export declare function confidenceInterval(distributionOne: number[], distributionTwo: number[], interval: number): [number, number];
+export declare function confidenceInterval(a: number[], b: number[], confidence: number): {
+    lower: number;
+    median: number;
+    upper: number;
+    U: number;
+    zScore: number;
+    pValue: number;
+};
 export {};
 //# sourceMappingURL=confidence-interval.d.ts.map

dist/src/confidence-interval.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
 exports.confidenceInterval = exports.cartesianProduct = void 0;
+const d3_array_1 = require("d3-array");
 const jStat = require("jstat");
@@ -14,3 +15,3 @@ /**
 /**
- * Apply the passed "_func" to the permutations of the items in listOne and listTwo
+ * Apply the passed "func" to the permutations of the items in listOne and listTwo
  *
@@ -39,17 +40,35 @@ * @param listOne - Array of numbers
  */
-function confidenceInterval(distributionOne, distributionTwo, interval) {
-    const distributionOneLength = distributionOne.length;
-    const distributionTwoLength = distributionTwo.length;
-    const lengthsMultiplied = distributionOneLength * distributionTwoLength;
-    const sqrtOfSomething = Math.sqrt((lengthsMultiplied * (distributionOneLength + distributionTwoLength + 1)) /
-        12);
-    const other = jStat.normal.inv(1 - (1 - interval) / 2, 0, 1) * sqrtOfSomething;
-    const ca = Math.floor((distributionOneLength * distributionTwoLength) / 2 - other);
-    const diffs = cartesianProduct(distributionOne, distributionTwo);
-    return [
-        diffs[ca - 1],
-        diffs[distributionOneLength * distributionTwoLength - ca]
-    ];
+function confidenceInterval(a, b, confidence) {
+    var _a;
+    const aLength = a.length;
+    const bLength = b.length;
+    const maxU = aLength * bLength;
+    const meanU = maxU / 2;
+    const deltas = a
+        .map((a) => b.map((b) => a - b))
+        .flat()
+        .sort((a, b) => a - b);
+    const U = deltas.reduce((accum, value) => accum + (value < 0 ? 1 : value == 0 ? 0.5 : 0), 0);
+    const lowerTail = U <= meanU;
+    const standadDeviationU = Math.sqrt((maxU * (aLength + bLength + 1)) / 12);
+    // we are estimating a discrete distribution so bias the mean depending on which tail
+    // we are computing the pValue for
+    const continuityCorrection = lowerTail ? 0.5 : -0.5;
+    const zScore = (U - meanU + continuityCorrection) / standadDeviationU;
+    // z is symmetrical, so use lower tail and double the cumulative for each tail
+    // since this is a two tailed test
+    const pValue = jStat.normal.cdf(-Math.abs(zScore), 0, 1) * 2;
+    const alpha = 1 - confidence;
+    const lowerU = Math.round(jStat.normal.inv(alpha / 2, meanU + 0.5, standadDeviationU));
+    const upperU = Math.round(jStat.normal.inv(1 - alpha / 2, meanU + 0.5, standadDeviationU));
+    return {
+        lower: deltas[lowerU],
+        median: (_a = d3_array_1.median(deltas)) !== null && _a !== void 0 ? _a : 0,
+        upper: deltas[upperU],
+        zScore,
+        pValue,
+        U
+    };
 }
 exports.confidenceInterval = confidenceInterval;
 //# sourceMappingURL=confidence-interval.js.map

dist/src/stats.js

@@ -86,10 +86,10 @@ "use strict";
         const ci = confidence_interval_1.confidenceInterval(control, experiment, confidenceLevel);
-        const isSig = (ci[0] < 0 && 0 < ci[1]) ||
-            (ci[0] > 0 && 0 > ci[1]) ||
-            (ci[0] === 0 && ci[1] === 0)
+        const isSig = (ci.lower < 0 && 0 < ci.upper) ||
+            (ci.lower > 0 && 0 > ci.upper) ||
+            (ci.lower === 0 && ci.upper === 0)
             ? false
             : true;
         return {
-            min: Math.round(Math.ceil(ci[0] * 100) / 100),
-            max: Math.round(Math.ceil(ci[1] * 100) / 100),
+            min: Math.round(Math.ceil(ci.lower * 100) / 100),
+            max: Math.round(Math.ceil(ci.upper * 100) / 100),
             isSig
@@ -96,0 +96,0 @@ };

package.json

 {
   "name": "@tracerbench/stats",
-  "version": "4.5.3",
+  "version": "4.5.5",
   "description": "Stats class written in TS-Node",
@@ -31,3 +31,3 @@ "keywords": [
   "dependencies": {
-    "d3-array": "^2.8.0",
+    "d3-array": "^2.9.1",
     "d3-scale": "^3.2.3",
@@ -43,27 +43,27 @@ "fs-extra": "^9.0.1",
     "@types/d3-array": "^2.8.0",
-    "@types/d3-scale": "^3.2.1",
-    "@types/fs-extra": "^9.0.4",
-    "@types/node": "^14.14.7",
+    "@types/d3-scale": "^3.2.2",
+    "@types/fs-extra": "^9.0.6",
+    "@types/node": "^14.14.19",
     "@types/tmp": "^0.2.0",
-    "@typescript-eslint/eslint-plugin": "^4.8.1",
-    "@typescript-eslint/parser": "^4.8.1",
+    "@typescript-eslint/eslint-plugin": "^4.11.1",
+    "@typescript-eslint/parser": "^4.11.1",
     "chai": "^4.2.0",
     "chai-files": "^1.4.0",
-    "eslint": "^7.13.0",
-    "eslint-config-prettier": "^6.15.0",
+    "eslint": "^7.17.0",
+    "eslint-config-prettier": "^7.1.0",
     "eslint-plugin-filenames": "^1.3.2",
     "eslint-plugin-import": "^2.22.1",
     "eslint-plugin-oclif": "^0.1.0",
-    "eslint-plugin-prettier": "^3.1.4",
-    "eslint-plugin-simple-import-sort": "^5.0.3",
+    "eslint-plugin-prettier": "^3.3.0",
+    "eslint-plugin-simple-import-sort": "5.0.3",
     "mocha": "^8.2.1",
     "mock-fs": "^4.13.0",
     "nyc": "^15.1.0",
-    "prettier": "^2.1.2",
-    "ts-node": "^9.0.0",
-    "typescript": "^4.0.5",
-    "typescript-json-schema": "^0.43.0"
+    "prettier": "^2.2.1",
+    "ts-node": "^9.1.1",
+    "typescript": "^4.1.3",
+    "typescript-json-schema": "^0.47.0"
   },
   "engine": "node >= 10",
-  "gitHead": "21395e4b64a55bf92aeb6cdef441e48f2ee4b854"
+  "gitHead": "fc452a997ad2db56e451bcf4b98650336e975c80"
 }

src/confidence-interval.ts

@@ -0,1 +1,2 @@
+import { median } from 'd3-array';
 import * as jStat from 'jstat';
@@ -13,3 +14,3 @@
 /**
- * Apply the passed "_func" to the permutations of the items in listOne and listTwo
+ * Apply the passed "func" to the permutations of the items in listOne and listTwo
  *
@@ -43,24 +44,59 @@ * @param listOne - Array of numbers
 export function confidenceInterval(
-  distributionOne: number[],
-  distributionTwo: number[],
-  interval: number
-): [number, number] {
-  const distributionOneLength = distributionOne.length;
-  const distributionTwoLength = distributionTwo.length;
+  a: number[],
+  b: number[],
+  confidence: number
+): {
+  lower: number;
+  median: number;
+  upper: number;
+  U: number;
+  zScore: number;
+  pValue: number;
+} {
+  const aLength = a.length;
+  const bLength = b.length;
+  const maxU = aLength * bLength;
+  const meanU = maxU / 2;
 
-  const lengthsMultiplied = distributionOneLength * distributionTwoLength;
-  const sqrtOfSomething = Math.sqrt(
-    (lengthsMultiplied * (distributionOneLength + distributionTwoLength + 1)) /
-      12
+  const deltas = a
+    .map((a) => b.map((b) => a - b))
+    .flat()
+    .sort((a, b) => a - b);
+
+  const U = deltas.reduce(
+    (accum, value) => accum + (value < 0 ? 1 : value == 0 ? 0.5 : 0),
+    0
   );
-  const other =
-    jStat.normal.inv(1 - (1 - interval) / 2, 0, 1) * sqrtOfSomething;
-  const ca = Math.floor(
-    (distributionOneLength * distributionTwoLength) / 2 - other
+
+  const lowerTail = U <= meanU;
+
+  const standadDeviationU = Math.sqrt((maxU * (aLength + bLength + 1)) / 12);
+
+  // we are estimating a discrete distribution so bias the mean depending on which tail
+  // we are computing the pValue for
+  const continuityCorrection = lowerTail ? 0.5 : -0.5;
+
+  const zScore = (U - meanU + continuityCorrection) / standadDeviationU;
+
+  // z is symmetrical, so use lower tail and double the cumulative for each tail
+  // since this is a two tailed test
+  const pValue = jStat.normal.cdf(-Math.abs(zScore), 0, 1) * 2;
+
+  const alpha = 1 - confidence;
+
+  const lowerU = Math.round(
+    jStat.normal.inv(alpha / 2, meanU + 0.5, standadDeviationU)
   );
-  const diffs = cartesianProduct(distributionOne, distributionTwo);
-  return [
-    diffs[ca - 1],
-    diffs[distributionOneLength * distributionTwoLength - ca]
-  ];
+  const upperU = Math.round(
+    jStat.normal.inv(1 - alpha / 2, meanU + 0.5, standadDeviationU)
+  );
+
+  return {
+    lower: deltas[lowerU],
+    median: median(deltas) ?? 0,
+    upper: deltas[upperU],
+    zScore,
+    pValue,
+    U
+  };
 }

src/stats.ts

@@ -218,10 +218,10 @@ import { cross, histogram, mean, quantile } from 'd3-array';
     const isSig =
-      (ci[0] < 0 && 0 < ci[1]) ||
-      (ci[0] > 0 && 0 > ci[1]) ||
-      (ci[0] === 0 && ci[1] === 0)
+      (ci.lower < 0 && 0 < ci.upper) ||
+      (ci.lower > 0 && 0 > ci.upper) ||
+      (ci.lower === 0 && ci.upper === 0)
         ? false
         : true;
     return {
-      min: Math.round(Math.ceil(ci[0] * 100) / 100),
-      max: Math.round(Math.ceil(ci[1] * 100) / 100),
+      min: Math.round(Math.ceil(ci.lower * 100) / 100),
+      max: Math.round(Math.ceil(ci.upper * 100) / 100),
       isSig
@@ -228,0 +228,0 @@ };

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Improved metrics

Total package byte prevSize

222351

increased by2.03%

Lines of code

1235

increased by4.48%

Dependency changes

• Updatedd3-array@^2.9.1