Examples of org.apache.commons.math3.distribution.GammaDistribution.cumulativeProbability()

        throws DimensionMismatchException, NotPositiveException, ZeroException,
        MaxCountExceededException {


        ChiSquaredDistribution distribution;
        distribution = new ChiSquaredDistribution((double) observed1.length - 1);
        return 1 - distribution.cumulativeProbability(
                chiSquareDataSetsComparison(observed1, observed2));


    }


    /**

            throws NotPositiveException, NotStrictlyPositiveException,
            DimensionMismatchException, MaxCountExceededException {


        final ChiSquaredDistribution distribution =
                new ChiSquaredDistribution(expected.length - 1.0);
        return 1.0 - distribution.cumulativeProbability(
                g(expected, observed));
    }


    /**
     * Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described

            throws NotPositiveException, NotStrictlyPositiveException,
            DimensionMismatchException, MaxCountExceededException {


        final ChiSquaredDistribution distribution =
                new ChiSquaredDistribution(expected.length - 2.0);
        return 1.0 - distribution.cumulativeProbability(
                g(expected, observed));
    }


    /**
     * Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit

            final long[] observed2)
            throws DimensionMismatchException, NotPositiveException, ZeroException,
            MaxCountExceededException {
        final ChiSquaredDistribution distribution = new ChiSquaredDistribution(
                (double) observed1.length - 1);
        return 1 - distribution.cumulativeProbability(
                gDataSetsComparison(observed1, observed2));
    }


    /**
     * <p>Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned


        final AnovaStats a = anovaStats(categoryData);
        // No try-catch or advertised exception because args are valid
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final FDistribution fdist = new FDistribution(null, a.dfbg, a.dfwg);
        return 1.0 - fdist.cumulativeProbability(a.F);


    }


    /**
     * Computes the ANOVA P-value for a collection of {@link SummaryStatistics}.

               ConvergenceException, MaxCountExceededException {


        final AnovaStats a = anovaStats(categoryData, allowOneElementData);
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final FDistribution fdist = new FDistribution(null, a.dfbg, a.dfwg);
        return 1.0 - fdist.cumulativeProbability(a.F);


    }


    /**
     * This method calls the method that actually does the calculations (except

        throws NullArgumentException, DimensionMismatchException,
        ConvergenceException, MaxCountExceededException {


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


    }


    /**
     * Performs an ANOVA test, evaluating the null hypothesis that there

        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);


    }


    /**
     * Computes the ANOVA P-value for a collection of {@link SummaryStatistics}.

        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);


    }


    /**
     * This method calls the method that actually does the calculations (except

        TDistribution tDistribution = new TDistribution(2);
        for (double q : new double[]{0.001, 0.01, 0.1, 0.2, 0.5, 0.8, 0.9, 0.99, 0.99}) {
            double uG1 = gd1.cumulativeProbability(tdG1.quantile(q));
            assertEquals(q, uG1, (1 - q) * q * 10e-2);


            double uG2 = gd2.cumulativeProbability(tdG2.quantile(q));
            assertEquals(q, uG2, (1 - q) * q * 10e-2);


            double u1 = normalDistribution.cumulativeProbability(td1.quantile(q));
            assertEquals(q, u1, (1 - q) * q * 10e-2);

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