Package org.apache.commons.math3.distribution

Examples of org.apache.commons.math3.distribution.FDistribution

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        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextF() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new FDistribution(12, 5));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextF(12, 5);
            TestUtils.updateCounts(value, counts, quartiles);
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            container.add(comp, c);

            c.gridx++;
            comp = createComponent("Fisher-Snedecor", 0, 5,
                                   new String[] { "d1=1,d2=1", "d1=2,d2=1", "d1=5,d2=2", "d1=100,d2=1", "d1=100,d2=100" },
                                   new FDistribution(1, 1),
                                   new FDistribution(2, 1),
                                   new FDistribution(5, 2),
                                   new FDistribution(100, 1),
                                   new FDistribution(100, 100));
            container.add(comp, c);

            c.gridx++;
            comp = createComponent("Gamma", 0, 20,
                                   new String[] { "k=1,θ=2", "k=2,θ=2", "k=3,θ=2", "k=5,θ=1", "k=9,θ=0.5" },
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        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        double lowerBound = 0;
        double upperBound = 0;
        final double alpha = (1.0 - confidenceLevel) / 2.0;

        final FDistribution distributionLowerBound = new FDistribution(2 * (numberOfTrials - numberOfSuccesses + 1),
                                                                       2 * numberOfSuccesses);
        final double fValueLowerBound = distributionLowerBound.inverseCumulativeProbability(1 - alpha);
        if (numberOfSuccesses > 0) {
            lowerBound = numberOfSuccesses /
                         (numberOfSuccesses + (numberOfTrials - numberOfSuccesses + 1) * fValueLowerBound);
        }

        final FDistribution distributionUpperBound = new FDistribution(2 * (numberOfSuccesses + 1),
                                                                       2 * (numberOfTrials - numberOfSuccesses));
        final double fValueUpperBound = distributionUpperBound.inverseCumulativeProbability(1 - alpha);
        if (numberOfSuccesses > 0) {
            upperBound = (numberOfSuccesses + 1) * fValueUpperBound /
                         (numberOfTrials - numberOfSuccesses + (numberOfSuccesses + 1) * fValueUpperBound);
        }

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        throws NullArgumentException, DimensionMismatchException,
        ConvergenceException, MaxCountExceededException {

        AnovaStats a = anovaStats(categoryData);
        // No try-catch or advertised exception because args are valid
        FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
        return 1.0 - fdist.cumulativeProbability(a.F);

    }
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                              final boolean allowOneElementData)
        throws NullArgumentException, DimensionMismatchException,
               ConvergenceException, MaxCountExceededException {

        final AnovaStats a = anovaStats(categoryData, allowOneElementData);
        final FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
        return 1.0 - fdist.cumulativeProbability(a.F);

    }
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        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextF() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new FDistribution(12, 5));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextF(12, 5);
            TestUtils.updateCounts(value, counts, quartiles);
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     * @return random value sampled from the F(numeratorDf, denominatorDf) distribution
     * @throws NotStrictlyPositiveException if
     * {@code numeratorDf <= 0} or {@code denominatorDf <= 0}.
     */
    public double nextF(double numeratorDf, double denominatorDf) throws NotStrictlyPositiveException {
        return new FDistribution(getRandomGenerator(), numeratorDf, denominatorDf,
                FDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample();
    }
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        throws NullArgumentException, DimensionMismatchException,
        ConvergenceException, MaxCountExceededException {

        AnovaStats a = anovaStats(categoryData);
        // No try-catch or advertised exception because args are valid
        FDistribution fdist = new FDistribution(a.dfbg, a.dfwg);
        return 1.0 - fdist.cumulativeProbability(a.F);

    }
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     * @return random value sampled from the F(numeratorDf, denominatorDf) distribution
     * @throws NotStrictlyPositiveException if
     * {@code numeratorDf <= 0} or {@code denominatorDf <= 0}.
     */
    public double nextF(double numeratorDf, double denominatorDf) throws NotStrictlyPositiveException {
        return new FDistribution(getRan(), numeratorDf, denominatorDf,
                FDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY).sample();
    }
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        TestUtils.assertChiSquareAccept(expected, counts, 0.001);
    }

    @Test
    public void testNextF() {
        double[] quartiles = TestUtils.getDistributionQuartiles(new FDistribution(12, 5));
        long[] counts = new long[4];
        randomData.reSeed(1000);
        for (int i = 0; i < 1000; i++) {
            double value = randomData.nextF(12, 5);
            TestUtils.updateCounts(value, counts, quartiles);
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Related Classes of org.apache.commons.math3.distribution.FDistribution

  • org.apache.commons.math3.analysis.differentiation.DerivativeStructure
  • org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction
  • org.apache.commons.math3.analysis.function.HarmonicOscillator
  • org.apache.commons.math3.analysis.function.Sin
  • org.apache.commons.math3.analysis.function.Sinc
  • org.apache.commons.math3.analysis.MultivariateFunction
  • org.apache.commons.math3.analysis.QuinticFunction
  • org.apache.commons.math3.analysis.solvers.BrentSolver
  • org.apache.commons.math3.analysis.UnivariateFunction
  • org.apache.commons.math3.complex.Complex
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