Examples of org.apache.commons.math.stat.inference.ChiSquareTest.chiSquare()

Package org.apache.commons.math.stat.inference

Class org.apache.commons.math.stat.inference.ChiSquareTest

Examples of org.apache.commons.math.stat.inference.ChiSquareTest.chiSquare()

org.apache.commons.math.stat.inference.ChiSquareTest.chiSquare()
tl.nist.gov/div898/handbook/eda/section3/eda35f.htm"> Chi-Square statistic comparing observed and expected frequency counts.
This statistic can be used to perform a Chi-Square test evaluating the null hypothesis that the observed counts follow the expected distribution.

Preconditions:
- Expected counts must all be positive.
- Observed counts must all be >= 0.
- The observed and expected arrays must have the same length and their common length must be at least 2.
If any of the preconditions are not met, an IllegalArgumentException is thrown.
@param observed array of observed frequency counts @param expected array of expected frequency counts @return chiSquare statistic @throws IllegalArgumentException if preconditions are not met

            msgBuffer.append("Chisquare test failed for mean = ");
            msgBuffer.append(mean);
            msgBuffer.append(" p-value = ");
            msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
            msgBuffer.append(" chisquare statistic = ");
            msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
            msgBuffer.append(". \n");
            msgBuffer.append("bin\t\texpected\tobserved\n");
            for (int i = 0; i < expected.length; i++) {
                msgBuffer.append("[");
                msgBuffer.append(i == 0 ? 1: binBounds.get(i - 1));

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        //score = cs;
        if(Double.isNaN(score)) score = 0.0;
        mcNemarScores.put(s, score);
      }

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        if(beforeCutOff && ((1.0 - csd.cumulativeProbability(mcNemarScores.get(s))) / foo) > 0.05) {
          System.out.println(count - 1);
          beforeCutOff = false;
          //break;
        }

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      if(observed > 0) g += 2 * observed * Math.log(observed/expected);
      if(observedElsewhere > 0) g += 2 * observedElsewhere * Math.log(observedElsewhere/expectedElsewhere); 
      
      long [] obsArray = new long[]{observed, observedElsewhere};
      double [] expectArray = new double[]{expected, expectedElsewhere};
      double cs = cst.chiSquare(expectArray, obsArray);
      
      double manualcs = 0.0;
      manualcs += Math.pow(observed - expected, 2.0) / expected;
      manualcs += Math.pow(observedElsewhere - expectedElsewhere, 2.0) / expectedElsewhere;

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        //score = cs;
        if(Double.isNaN(score)) score = 0.0;
        mcNemarScores.put(s, score);
      }

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        if(beforeCutOff && ((1.0 - csd.cumulativeProbability(mcNemarScores.get(s))) / foo) > 0.05) {
          System.out.println(count - 1);
          beforeCutOff = false;
          //break;
        }

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            DecimalFormat df = new DecimalFormat("#.##");
            msgBuffer.append("Chisquare test failed");
            msgBuffer.append(" p-value = ");
            msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
            msgBuffer.append(" chisquare statistic = ");
            msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
            msgBuffer.append(". \n");
            msgBuffer.append("value\texpected\tobserved\n");
            for (int i = 0; i < expected.length; i++) {
                msgBuffer.append(valueLabels[i]);
                msgBuffer.append("\t");

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            msgBuffer.append("Chisquare test failed for mean = ");
            msgBuffer.append(mean);
            msgBuffer.append(" p-value = ");
            msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
            msgBuffer.append(" chisquare statistic = ");
            msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
            msgBuffer.append(". \n");
            msgBuffer.append("bin\t\texpected\tobserved\n");
            for (int i = 0; i < expected.length; i++) {
                msgBuffer.append("[");
                msgBuffer.append(i == 0 ? 1: binBounds.get(i - 1));

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