Package org.apache.commons.math3.distribution

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

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            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
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        throws NotPositiveException, NotStrictlyPositiveException,
        DimensionMismatchException, MaxCountExceededException {

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

    /**
     * Performs a <a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm">
     * Chi-square goodness of fit test</a> evaluating the null hypothesis that the
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        checkArray(counts);
        double df = ((double) counts.length -1) * ((double) counts[0].length - 1);
        ChiSquaredDistribution distribution;
        distribution = new ChiSquaredDistribution(df);
        return 1 - distribution.cumulativeProbability(chiSquare(counts));

    }

    /**
     * Performs a <a href="http://www.itl.nist.gov/div898/handbook/prc/section4/prc45.htm">
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        throws DimensionMismatchException, NotPositiveException, ZeroException,
        MaxCountExceededException {

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

    }

    /**
 
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            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
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            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
View Full Code Here
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            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
View Full Code Here
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        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}.
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               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
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        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
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