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

Package org.apache.commons.math3.distribution

Class org.apache.commons.math3.distribution.NormalDistribution

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

org.apache.commons.math3.distribution.NormalDistribution.cumulativeProbability()
{@inheritDoc}If {@code x} is more than 40 standard deviations from the mean, 0 or 1is returned, as in these cases the actual value is within {@code Double.MIN_VALUE} of 0 or 1.


        final double t = FastMath.abs(t(m1, m2, v1, v2, n1, n2));
        final double degreesOfFreedom = df(v1, v2, n1, n2);
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final TDistribution distribution = new TDistribution(null, degreesOfFreedom);
        return 2.0 * distribution.cumulativeProbability(-t);


    }


    /**
     * Computes p-value for 2-sided, 2-sample t-test, under the assumption

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        final double t = FastMath.abs(homoscedasticT(m1, m2, v1, v2, n1, n2));
        final double degreesOfFreedom = n1 + n2 - 2;
        // pass a null rng to avoid unneeded overhead as we will not sample from this distribution
        final TDistribution distribution = new TDistribution(null, degreesOfFreedom);
        return 2.0 * distribution.cumulativeProbability(-t);


    }


    /**
     * Check significance level.

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     * @throws org.apache.commons.math3.exception.MaxCountExceededException
     * if the significance level can not be computed.
     */
    public double getSignificance() {
        TDistribution distribution = new TDistribution(n - 2);
        return 2d * (1.0 - distribution.cumulativeProbability(
                    FastMath.abs(getSlope()) / getSlopeStdErr()));
    }


    // ---------------------Private methods-----------------------------------

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                if (i == j) {
                    out[i][j] = 0d;
                } else {
                    double r = correlationMatrix.getEntry(i, j);
                    double t = FastMath.abs(r * FastMath.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * tDistribution.cumulativeProbability(-t);
                }
            }
        }
        return new BlockRealMatrix(out);
    }

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                           final double v, final double n)
        throws MaxCountExceededException {


        double t = FastMath.abs(t(m, mu, v, n));
        TDistribution distribution = new TDistribution(n - 1);
        return 2.0 * distribution.cumulativeProbability(-t);


    }


    /**
     * Computes p-value for 2-sided, 2-sample t-test.

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        throws MaxCountExceededException {


        final double t = FastMath.abs(t(m1, m2, v1, v2, n1, n2));
        final double degreesOfFreedom = df(v1, v2, n1, n2);
        TDistribution distribution = new TDistribution(degreesOfFreedom);
        return 2.0 * distribution.cumulativeProbability(-t);


    }


    /**
     * Computes p-value for 2-sided, 2-sample t-test, under the assumption

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        throws MaxCountExceededException {


        final double t = FastMath.abs(homoscedasticT(m1, m2, v1, v2, n1, n2));
        final double degreesOfFreedom = n1 + n2 - 2;
        TDistribution distribution = new TDistribution(degreesOfFreedom);
        return 2.0 * distribution.cumulativeProbability(-t);


    }


    /**
     * Check significance level.

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        if (n < 3) {
            return Double.NaN;
        }
        // No advertised NotStrictlyPositiveException here - will return NaN above
        TDistribution distribution = new TDistribution(n - 2);
        return 2d * (1.0 - distribution.cumulativeProbability(
                    FastMath.abs(getSlope()) / getSlopeStdErr()));
    }


    // ---------------------Private methods-----------------------------------

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        RealMatrix pValues = corrInstance.getCorrelationPValues();
        RealMatrix stdErrors = corrInstance.getCorrelationStandardErrors();
        for (int i = 0; i < 5; i++) {
            for (int j = 0; j < i; j++) {
                double t = FastMath.abs(rValues.getEntry(i, j)) / stdErrors.getEntry(i, j);
                double p = 2 * (1 - tDistribution.cumulativeProbability(t));
                Assert.assertEquals(p, pValues.getEntry(i, j), 10E-15);
            }
        }
    }

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                if (i == j) {
                    out[i][j] = 0d;
                } else {
                    double r = correlationMatrix.getEntry(i, j);
                    double t = FastMath.abs(r * FastMath.sqrt((nObs - 2)/(1 - r * r)));
                    out[i][j] = 2 * tDistribution.cumulativeProbability(-t);
                }
            }
        }
        return new BlockRealMatrix(out);
    }

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