@bdelab/jscat - npm Package Compare versions

Comparing version 1.0.3 to 1.0.4

lib/index.d.ts

@@ -19,2 +19,9 @@ export declare type Zeta = {
 /**
+ * a 3PL Fisher information function
+ * @param theta - ability estimate
+ * @param zeta - item params
+ * @returns {number} - the expected value of the observed information
+ */
+export declare const fisherInformation: (theta: number, zeta: Zeta) => number;
+/**
  * return a Gaussian distribution within a given range
@@ -54,1 +61,7 @@ * @param mean
 } | undefined;
+/**
+ * calculate the standard error of mean of ability estimation
+ * @param theta
+ * @param zetas
+ */
+export declare const SEM: (theta: number, zetas: Array<Zeta>) => number;

lib/index.js

 "use strict";
 Object.defineProperty(exports, "__esModule", { value: true });
-exports.findNextItem = exports.estimateAbility = exports.abilityPrior = exports.normal = exports.itemResponseFunction = void 0;
+exports.SEM = exports.findNextItem = exports.estimateAbility = exports.abilityPrior = exports.normal = exports.fisherInformation = exports.itemResponseFunction = void 0;
 const optimization_js_1 = require("optimization-js");
@@ -17,2 +17,14 @@ const lodash_1 = require("lodash");
 /**
+ * a 3PL Fisher information function
+ * @param theta - ability estimate
+ * @param zeta - item params
+ * @returns {number} - the expected value of the observed information
+ */
+const fisherInformation = (theta, zeta) => {
+    const p = (0, exports.itemResponseFunction)(theta, zeta);
+    const q = 1 - p;
+    return Math.pow(zeta.a, 2) * (q / p) * (Math.pow(p - zeta.c, 2) / Math.pow(1 - zeta.c, 2));
+};
+exports.fisherInformation = fisherInformation;
+/**
  * return a Gaussian distribution within a given range
@@ -63,9 +75,7 @@ * @param mean
         let nf = 0;
-        for (let i = 0; i < prior.length; i++) {
-            const theta = prior[i][0];
-            const probability = prior[i][1];
+        prior.forEach(([theta, probability]) => {
             const like = likelihood(theta);
             num += theta * like * probability;
             nf += like * probability;
-        }
+        });
         return num / nf;
@@ -121,3 +131,3 @@ }
     if (method === 'mfi') {
-        const stimuliAddFisher = arr.map((element) => (Object.assign({ fisherInformation: fisherInformation(theta, { a: 1, b: element.difficulty, c: 0.5, d: 1 }) }, element)));
+        const stimuliAddFisher = arr.map((element) => (Object.assign({ fisherInformation: (0, exports.fisherInformation)(theta, { a: 1, b: element.difficulty, c: 0.5, d: 1 }) }, element)));
         stimuliAddFisher.sort((a, b) => b.fisherInformation - a.fisherInformation);
@@ -211,14 +221,16 @@ stimuliAddFisher.forEach((stimulus) => {
     }
-    /**
-     * a 3PL Fisher information function
-     * @param theta - ability estimate
-     * @param zeta - item params
-     * @returns {number} - the expected value of the observed information
-     */
-    function fisherInformation(theta, zeta) {
-        const p = (0, exports.itemResponseFunction)(theta, zeta);
-        const q = 1 - p;
-        return Math.pow(zeta.a, 2) * (q / p) * ((p - zeta.c) / Math.pow(1 - zeta.c, 2));
-    }
 };
 exports.findNextItem = findNextItem;
+/**
+ * calculate the standard error of mean of ability estimation
+ * @param theta
+ * @param zetas
+ */
+const SEM = (theta, zetas) => {
+    let sum = 0;
+    zetas.forEach(function (zeta) {
+        sum += (0, exports.fisherInformation)(theta, zeta);
+    });
+    return 1 / Math.sqrt(sum);
+};
+exports.SEM = SEM;

package.json

 {
   "name": "@bdelab/jscat",
-  "version": "1.0.3",
+  "version": "1.0.4",
   "description": "A library to support IRT-based computer adaptive testing in JavaScript",
@@ -5,0 +5,0 @@ "main": "lib/index.js",

README.md

@@ -12,3 +12,3 @@ # jsCAT: Computer Adaptive Testing in JavaScript
 ```JavaScript
-import {normal, estimateAbility, findNextItem} from '@bdelab/jscat';
+import {normal, estimateAbility, findNextItem, SEM} from '@bdelab/jscat';
 
@@ -21,2 +21,4 @@ // declare prior if you choose to use EAP method
 
+const se = SEM(theta, zetas);
+
 const nextStimulus = findNextItem(stimuli, theta, method, deepCopy);
@@ -23,0 +25,0 @@ ```

src/__tests__/index.test.ts

@@ -1,4 +0,2 @@
-import { itemResponseFunction } from '../index';
-import { estimateAbility } from '../index';
-import { findNextItem } from '../index';
+import { itemResponseFunction, estimateAbility, findNextItem, SEM } from '../index';
 import { Stimulus } from '../index';
@@ -54,1 +52,15 @@
 });
+
+describe('SEM', () => {
+  it('correctly calculate the standard error of mean of ability estimate', () => {
+    const received = SEM(-1.551, [
+      { a: 1, b: -0.4473004, c: 0.5, d: 1 },
+      { a: 1, b: 2.8692, c: 0.5, d: 1 },
+      { a: 1, b: -0.46935, c: 0.5, d: 1 },
+      { a: 1, b: -0.5758, c: 0.5, d: 1 },
+      { a: 1, b: -1.43012, c: 0.5, d: 1 },
+      { a: 1, b: -1.60728, c: 0.5, d: 1 },
+    ]);
+    expect(1.88).toBeCloseTo(received, 2);
+  });
+});

src/index.ts

@@ -23,2 +23,14 @@ import { minimize_Powell } from 'optimization-js';
 /**
+ * a 3PL Fisher information function
+ * @param theta - ability estimate
+ * @param zeta - item params
+ * @returns {number} - the expected value of the observed information
+ */
+export const fisherInformation = (theta: number, zeta: Zeta) => {
+  const p = itemResponseFunction(theta, zeta);
+  const q = 1 - p;
+  return Math.pow(zeta.a, 2) * (q / p) * (Math.pow(p - zeta.c, 2) / Math.pow(1 - zeta.c, 2));
+};
+
+/**
  * return a Gaussian distribution within a given range
@@ -78,9 +90,7 @@ * @param mean
     let nf = 0;
-    for (let i = 0; i < prior.length; i++) {
-      const theta = prior[i][0];
-      const probability = prior[i][1];
+    prior.forEach(([theta, probability]) => {
       const like = likelihood(theta);
       num += theta * like * probability;
       nf += like * probability;
-    }
+    });
     return num / nf;
@@ -226,14 +236,15 @@ }
   }
+};
 
-  /**
-   * a 3PL Fisher information function
-   * @param theta - ability estimate
-   * @param zeta - item params
-   * @returns {number} - the expected value of the observed information
-   */
-  function fisherInformation(theta: number, zeta: Zeta) {
-    const p = itemResponseFunction(theta, zeta);
-    const q = 1 - p;
-    return Math.pow(zeta.a, 2) * (q / p) * ((p - zeta.c) / Math.pow(1 - zeta.c, 2));
-  }
+/**
+ * calculate the standard error of mean of ability estimation
+ * @param theta
+ * @param zetas
+ */
+export const SEM = (theta: number, zetas: Array<Zeta>) => {
+  let sum = 0;
+  zetas.forEach(function (zeta) {
+    sum += fisherInformation(theta, zeta);
+  });
+  return 1 / Math.sqrt(sum);
 };

No alert changes

Improved metrics

Total package byte prevSize

29334

increased by5.03%

Lines of code

640

increased by7.74%

Number of lines in readme file

29

increased by7.41%

No dependency changes