List of chiSquare() Examples

Examples of 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
org.apache.commons.math.stat.inference.ChiSquareTestImpl.chiSquare()
{@inheritDoc}
Note: This implementation rescales the expected array if necessary to ensure that the sum of the expected and observed counts are equal.
@param observed array of observed frequency counts @param expected array of expected frequency counts @return chi-square test statistic @throws IllegalArgumentException if preconditions are not metor length is less than 2
org.apache.commons.math3.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.

Note: This implementation rescales the expected array if necessary to ensure that the sum of the expected and observed counts are equal.
@param observed array of observed frequency counts @param expected array of expected frequency counts @return chiSquare test statistic @throws NotPositiveException if observed has negative entries @throws NotStrictlyPositiveException if expected has entries that arenot strictly positive @throws DimensionMismatchException if the arrays length is less than 2

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

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

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

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

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

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

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

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

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

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

        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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