Examples of inverseCumulativeProbability()

Examples of org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()

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                                             double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double difference = normalDistribution.inverseCumulativeProbability(1 - alpha) *
                                  FastMath.sqrt(1.0 / numberOfTrials * mean * (1 - mean));
        return new ConfidenceInterval(mean - difference, mean + difference, confidenceLevel);
    }

}
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Examples of org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()

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    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double mean = (double) numberOfSuccesses / (double) numberOfTrials;

        final double factor = 1.0 / (1 + (1.0 / numberOfTrials) * zSquared);
        final double modifiedSuccessRatio = mean + (1.0 / (2 * numberOfTrials)) * zSquared;
 
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Examples of org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()

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    /** {@inheritDoc} */
    public ConfidenceInterval createInterval(int numberOfTrials, int numberOfSuccesses, double confidenceLevel) {
        IntervalUtils.checkParameters(numberOfTrials, numberOfSuccesses, confidenceLevel);
        final double alpha = (1.0 - confidenceLevel) / 2;
        final NormalDistribution normalDistribution = new NormalDistribution();
        final double z = normalDistribution.inverseCumulativeProbability(1 - alpha);
        final double zSquared = FastMath.pow(z, 2);
        final double modifiedNumberOfTrials = numberOfTrials + zSquared;
        final double modifiedSuccessesRatio = (1.0 / modifiedNumberOfTrials) * (numberOfSuccesses + 0.5 * zSquared);
        final double difference = z *
                                  FastMath.sqrt(1.0 / modifiedNumberOfTrials * modifiedSuccessesRatio *
 
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Examples of org.apache.commons.math3.distribution.NormalDistribution.inverseCumulativeProbability()

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    Arrays.sort(data);

    NormalDistribution reference = new NormalDistribution(RandomUtils.getRandom().getRandomGenerator(),
                                                          0, 1,
                                                          NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
    assertEquals("Median", reference.inverseCumulativeProbability(0.5), data[5000], 0.04);
  }
}
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Examples of org.apache.commons.math3.distribution.RealDistribution.inverseCumulativeProbability()

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        final double pBminus = pBminus(i);
        final double pCrit = p - pBminus;
        if (pCrit <= 0) {
            return lower;
        }
        return kernel.inverseCumulativeProbability(kBminus + pCrit * kB / pB);
    }

    /**
     * {@inheritDoc}
     * @since 3.1
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Examples of org.apache.commons.math3.distribution.RealDistribution.inverseCumulativeProbability()

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        final double pBminus = pBminus(i);
        final double pCrit = p - pBminus;
        if (pCrit <= 0) {
            return lower;
        }
        return kernel.inverseCumulativeProbability(kBminus + pCrit * kB / pB);
    }

    /**
     * {@inheritDoc}
     * @since 3.1
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Examples of org.apache.commons.math3.distribution.RealDistribution.inverseCumulativeProbability()

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        final double pBminus = pBminus(i);
        final double pCrit = p - pBminus;
        if (pCrit <= 0) {
            return lower;
        }
        return kernel.inverseCumulativeProbability(kBminus + pCrit * kB / pB);
    }

    /**
     * {@inheritDoc}
     * @since 3.1
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Examples of org.apache.commons.math3.distribution.TDistribution.inverseCumulativeProbability()

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                                          alpha, 0, 1);
        }
        // No advertised NotStrictlyPositiveException here - will return NaN above
        TDistribution distribution = new TDistribution(n - 2);
        return getSlopeStdErr() *
            distribution.inverseCumulativeProbability(1d - alpha / 2d);
    }

    /**
     * Returns the significance level of the slope (equiv) correlation.
     * <p>
 
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Examples of org.apache.commons.math3.distribution.TDistribution.inverseCumulativeProbability()

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            throw new OutOfRangeException(LocalizedFormats.SIGNIFICANCE_LEVEL,
                                          alpha, 0, 1);
        }
        TDistribution distribution = new TDistribution(n - 2);
        return getSlopeStdErr() *
            distribution.inverseCumulativeProbability(1d - alpha / 2d);
    }

    /**
     * Returns the significance level of the slope (equiv) correlation.
     * <p>
 
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Examples of org.apache.commons.math3.distribution.TDistribution.inverseCumulativeProbability()

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                                          alpha, 0, 1);
        }
        // No advertised NotStrictlyPositiveException here - will return NaN above
        TDistribution distribution = new TDistribution(n - 2);
        return getSlopeStdErr() *
            distribution.inverseCumulativeProbability(1d - alpha / 2d);
    }

    /**
     * Returns the significance level of the slope (equiv) correlation.
     * <p>
 
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