Changeset 9950 for branches/StatisticalTesting/HeuristicLab.Analysis.Statistics

Timestamp:

09/12/13 14:21:06 (11 years ago)

Author:

ascheibe

Message:

#2031 added Bonferroni-Holm adjusted p-values to the statistical testing view

Location:

branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3

Files:

• BonferroniHolm.cs (added)
• HeuristicLab.Analysis.Statistics-3.3.csproj (modified) (1 diff)
• StatisticalTestingView.cs (modified) (5 diffs)

branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3/HeuristicLab.Analysis.Statistics-3.3.csproj

@@ -119 +119 @@
   </ItemGroup>
   <ItemGroup>
+    <Compile Include="BonferroniHolm.cs" />
     <Compile Include="ExpFitting.cs" />
     <Compile Include="IFitting.cs" />

branches/StatisticalTesting/HeuristicLab.Analysis.Statistics/3.3/StatisticalTestingView.cs

@@ -36 +36 @@
   [Content(typeof(RunCollection), false)]
   public sealed partial class StatisticalTestingView : ItemView {
+    private const double significanceLevel = 0.05;
     private double[][] data;
 
@@ -297 +298 @@
       double pval = KruskalWallis.Test(data);
       pValTextBox.Text = pval.ToString();
-      if (pval < 0.05) {
+      if (pval < significanceLevel) {
         this.Invoke(new Action(() => { groupCompLabel.Image = HeuristicLab.Analysis.Statistics.Resources.Default; }));
       } else {
@@ -314 +315 @@
 
       // p-value is below significance level and thus the null hypothesis (data is normally distributed) is rejected. 
-      if (res.Any(x => x < 0.05)) {
+      if (res.Any(x => x < significanceLevel)) {
         this.Invoke(new Action(() => { normalityLabel.Image = HeuristicLab.Common.Resources.VSImageLibrary.Warning; }));
       } else {
@@ -355 +356 @@
         if (i != colIndex) {
           alglib.mannwhitneyutest(data[colIndex], data[colIndex].Length, data[i], data[i].Length, out mwuBothtails, out mwuLefttail, out mwuRighttail);
-          if (mwuBothtails > 0.05) {
+          if (mwuBothtails > significanceLevel) {
             cnt++;
           }
@@ -383 +384 @@
       }
 
-      DoubleMatrix pValsMatrix = new DoubleMatrix(5, stringConvertibleMatrixView.Content.Columns);
+      var rowNames = new string[] { "p-Value of Mann-Whitney U", "Adjusted p-Value of Mann-Whitney U", 
+            "p-Value of T-Test", "Adjusted p-Value of T-Test", "Necessary Sample Size for T-Test", "Cohen's d", "Hedges' g" };
+
+      DoubleMatrix pValsMatrix = new DoubleMatrix(rowNames.Length, stringConvertibleMatrixView.Content.Columns);
       pValsMatrix.ColumnNames = stringConvertibleMatrixView.Content.ColumnNames;
-      pValsMatrix.RowNames = new string[] { "p-Value of Mann-Whitney U", "p-Value of T-Test", "Necessary Sample Size for T-Test", "Cohen's d", "Hedges' g" };
+      pValsMatrix.RowNames = rowNames;
 
       double mwuBothtails;
       double mwuLefttail;
       double mwuRighttail;
-      double ttestLefttail;
+      double tTestLefttail;
+      double[] mwuPValues = new double[data.Length];
+      double[] tTestPValues = new double[data.Length];
+      bool[] decision = null;
+      double[] adjustedMwuPValues = null;
+      double[] adjustedTtestPValues = null;
+
       for (int i = 0; i < data.Length; i++) {
         alglib.mannwhitneyutest(data[colIndex], data[colIndex].Length, data[i], data[i].Length, out mwuBothtails, out mwuLefttail, out mwuRighttail);
-        ttestLefttail = TTest.Test(data[colIndex], data[i]);
-        pValsMatrix[0, i] = mwuBothtails;
-        pValsMatrix[1, i] = ttestLefttail;
-        pValsMatrix[2, i] = TTest.GetOptimalSampleSize(data[colIndex], data[i]);
-        pValsMatrix[3, i] = SampleSizeDetermination.CalculateCohensD(data[colIndex], data[i]);
-        pValsMatrix[4, i] = SampleSizeDetermination.CalculateHedgesG(data[colIndex], data[i]);
+        tTestLefttail = TTest.Test(data[colIndex], data[i]);
+        mwuPValues[i] = mwuBothtails;
+        tTestPValues[i] = tTestLefttail;
+      }
+
+      adjustedMwuPValues = BonferroniHolm.Calculate(significanceLevel, mwuPValues, out decision);
+      adjustedTtestPValues = BonferroniHolm.Calculate(significanceLevel, tTestPValues, out decision);
+
+      for (int i = 0; i < data.Length; i++) {
+        pValsMatrix[0, i] = mwuPValues[i];
+        pValsMatrix[1, i] = adjustedMwuPValues[i];
+        pValsMatrix[2, i] = tTestPValues[i];
+        pValsMatrix[3, i] = adjustedTtestPValues[i];
+        pValsMatrix[4, i] = TTest.GetOptimalSampleSize(data[colIndex], data[i]);
+        pValsMatrix[5, i] = SampleSizeDetermination.CalculateCohensD(data[colIndex], data[i]);
+        pValsMatrix[6, i] = SampleSizeDetermination.CalculateHedgesG(data[colIndex], data[i]);
       }