[9353] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[9706] | 3 | * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[9353] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Linq;
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| 24 | using HeuristicLab.Common;
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| 25 |
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| 26 | namespace HeuristicLab.Analysis.Statistics {
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| 27 | public class SampleSizeDetermination {
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| 28 | /// <summary>
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| 29 | /// Determines for a given sample the sample size by estimating the means.
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| 30 | /// </summary>
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| 31 | /// <param name="samples">The pilot sample.</param>
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| 32 | /// <param name="e">Precision. </param>
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| 33 | /// <param name="conf">Confidence Interval.</param>
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| 34 | /// <returns>Number of required samples for the given confidence interval and precision. </returns>
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| 35 | public static int DetermineSampleSizeByEstimatingMean(double[] samples, double e, double conf = 0.95) {
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| 36 | if (e < 0) throw new ArgumentException("e needs to be a positive number.");
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| 37 | if (conf < 0 || conf > 1) throw new ArgumentException("The confidence Interval must be between zero and one.");
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| 38 | double result = 0;
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| 39 |
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| 40 | double var = samples.StandardDeviation();
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| 41 | double n = alglib.invnormaldistribution((conf + 1) / 2);
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| 42 | result = Math.Pow(n, 2) * Math.Pow(var, 2) / Math.Pow(e, 2);
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| 43 | result = Math.Ceiling(result);
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| 44 | if (result > int.MaxValue)
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| 45 | return int.MaxValue;
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| 46 | else
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| 47 | return (int)result;
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| 48 | }
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| 49 |
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| 50 | /// <summary>
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| 51 | /// Calculates Cohen's d.
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| 52 | /// </summary>
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| 53 | /// <returns>Cohen's d.
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| 54 | /// d = 0.2 means small effect
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| 55 | /// d = 0.5 means medium effect
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| 56 | /// d = 0.8 means big effect
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| 57 | /// According to Wikipedia this means: "A lower Cohen's d indicates a necessity of larger sample sizes, and vice versa."
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| 58 | /// </returns>
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| 59 | public static double CalculateCohensD(double[] d1, double[] d2) {
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| 60 | double x1, x2, s1, s2;
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| 61 |
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| 62 | x1 = d1.Average();
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| 63 | x2 = d2.Average();
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| 64 | s1 = d1.Variance();
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| 65 | s2 = d2.Variance();
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| 66 |
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| 67 | return (x1 - x2) / Math.Sqrt((s1 + s2) / 2);
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| 68 | }
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| 69 |
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| 70 | /// <summary>
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| 71 | /// Calculates Hedges' g.
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| 72 | /// Hedges' g works like Cohen's d but corrects for bias.
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| 73 | /// </summary>
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| 74 | /// <returns>Hedges' g</returns>
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| 75 | public static double CalculateHedgesG(double[] d1, double[] d2) {
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| 76 | double x1, x2, s1, s2, n1, n2, s, g, c;
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| 77 |
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| 78 | x1 = d1.Average();
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| 79 | x2 = d2.Average();
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| 80 | s1 = d1.Variance();
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| 81 | s2 = d2.Variance();
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| 82 | n1 = d1.Count();
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| 83 | n2 = d2.Count();
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| 84 |
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| 85 | s = Math.Sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2));
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| 86 | g = (x1 - x2) / s;
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| 87 | c = (1 - (3 / (4 * (n1 + n2) - 9))) * g;
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| 88 |
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| 89 | return c;
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| 90 | }
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| 91 | }
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| 92 | }
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