Examples of cumulativeProbability()

@param x the value at which the CDF is evaluated. @return CDF for this distribution. @throws MathException if the cumulative probability can not becomputed due to convergence or other numerical errors.
  • org.apache.commons.math.distribution.NormalDistributionImpl.cumulativeProbability()
    For this distribution, X, this method returns P(X < x). If xis more than 40 standard deviations from the mean, 0 or 1 is returned, as in these cases the actual value is within Double.MIN_VALUE of 0 or 1. @param x the value at which the CDF is evaluated. @return CDF evaluated at x. @throws MathException if the algorithm fails to converge
  • org.apache.commons.math.distribution.PoissonDistribution.cumulativeProbability()
  • org.apache.commons.math.distribution.PoissonDistributionImpl.cumulativeProbability()
    The probability distribution function P(X <= x) for a Poisson distribution. @param x the value at which the PDF is evaluated. @return Poisson distribution function evaluated at x @throws MathException if the cumulative probability can not be computeddue to convergence or other numerical errors.
  • org.apache.commons.math.distribution.TDistribution.cumulativeProbability()
  • org.apache.commons.math.distribution.TDistributionImpl.cumulativeProbability()
    For this distribution, X, this method returns P(X < x). @param x the value at which the CDF is evaluated. @return CDF evaluated at x. @throws MathException if the cumulative probability can not becomputed due to convergence or other numerical errors.
  • org.apache.commons.math.distribution.WeibullDistribution.cumulativeProbability()
  • org.apache.commons.math3.distribution.BetaDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.BinomialDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.ChiSquaredDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.FDistribution.cumulativeProbability()
    orld.wolfram.com/F-Distribution.html"> F-Distribution, equation (4).
  • org.apache.commons.math3.distribution.GammaDistribution.cumulativeProbability()
    orld.wolfram.com/Chi-SquaredDistribution.html"> Chi-Squared Distribution, equation (9).
  • Casella, G., & Berger, R. (1990). Statistical Inference. Belmont, CA: Duxbury Press.
  • org.apache.commons.math3.distribution.IntegerDistribution.cumulativeProbability()
    For a random variable {@code X} whose values are distributed accordingto this distribution, this method returns {@code P(X <= x)}. In other words, this method represents the (cumulative) distribution function (CDF) for this distribution. @param x the point at which the CDF is evaluated @return the probability that a random variable with thisdistribution takes a value less than or equal to {@code x}
  • 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.
  • org.apache.commons.math3.distribution.PoissonDistribution.cumulativeProbability()
    {@inheritDoc}
  • org.apache.commons.math3.distribution.RealDistribution.cumulativeProbability()
    For a random variable {@code X} whose values are distributed accordingto this distribution, this method returns {@code P(x0 < X <= x1)}. @param x0 the exclusive lower bound @param x1 the inclusive upper bound @return the probability that a random variable with this distributiontakes a value between {@code x0} and {@code x1}, excluding the lower and including the upper endpoint @throws NumberIsTooLargeException if {@code x0> x1} @deprecated As of 3.1. In 4.0, this method will be renamed{@code probability(double x0, double x1)}.
  • org.apache.commons.math3.distribution.TDistribution.cumulativeProbability()
    {@inheritDoc}

    • Examples of org.apache.commons.math3.distribution.TDistribution.cumulativeProbability()

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            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
    View Full Code Here

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

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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.
    View Full Code Here

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

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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-----------------------------------

    View Full Code Here

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

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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);
        }
    View Full Code Here

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

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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.
    View Full Code Here

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

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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
    View Full Code Here

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

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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.
    View Full Code Here
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