A_DTest
javanpst.tests.goodness.A_DTest

Class A_DTest



  • public class A_DTestextends StatisticalTest
    The A_D test. The Anderson-Darling test can be used to adjust a given sample to either a Normal or a Exponential distribution. It is possible to select which parameters are defined for each distribution: Mean, sigma, both, or none. Additionally, adjust to other continuous distributions is supported, (Uniform, Chi-Square, Laplace, Logistic, Gamma and Weibull) but they have to be completely defined
    • Constructor Detail

      • A_DTest

        public A_DTest()
        Default builder
      • A_DTest

        public A_DTest(NumericSequence newSequence)
        Builder. Load data to test.
        Parameters:
        newSequence - data to test.
    • Method Detail

      • setData

        public void setData(NumericSequence newSequence)
        Load data to test.
        Parameters:
        newSequence - data to test.
      • adjustNormal

        public void adjustNormal()
        Sets adjustment to a Normal distribution, without specifying mean or sigma
      • adjustNormalMean

        public void adjustNormalMean(double m)
        Sets adjustment to a Normal distribution, specifying its mean.
        Parameters:
        m - mean of the distribution
      • adjustNormalVariance

        public void adjustNormalVariance(double s)
        Sets adjustment to a Normal distribution, specifying its mean.
        Parameters:
        s - sigma parameter of the distribution
      • adjustNormal

        public void adjustNormal(double m,                double s)
        Sets adjustment to a Normal distribution, specifying mean and sigma.
        Parameters:
        m - mean of the distribution
        s - sigma parameter of the distribution
      • adjustExponential

        public void adjustExponential()
        Sets adjustment to a Exponential distribution, without specifying mean.
      • adjustExponential

        public void adjustExponential(double m)
        Sets adjustment to a Exponential distribution fully defined
        Parameters:
        m - mean of the distribution
      • adjustUniform

        public void adjustUniform(double start,                 double end)
        Sets adjustment to a Uniform distribution fully defined
        Parameters:
        start - lower limit of the distribution
        end - upper limit of the distribution
      • adjustChiSquare

        public void adjustChiSquare(int freedom)
        Sets adjustment to a Chi-square distribution fully defined
        Parameters:
        freedom - number of degrees of freedom
      • adjustGamma

        public void adjustGamma(double K,               double lambda)
        Sets adjustment to a Gamma distribution fully defined
        Parameters:
        K - K parameter of the distribution
        lambda - lambda parameter of the distribution
      • adjustLaplace

        public void adjustLaplace(double mean,                 double scale)
        Sets adjustment to a Laplace distribution fully defined
        Parameters:
        mean - mean of the distribution
        scale - scale parameter of the distribution
      • adjustLogistic

        public void adjustLogistic(double mean,                  double S)
        Sets adjustment to a Logistic distribution fully defined
        Parameters:
        mean - mean of the distribution
        S - S parameter of the distribution
      • adjustWeibull

        public void adjustWeibull(double K,                 double lambda)
        Sets adjustment to a Weibull distribution fully defined
        Parameters:
        K - K parameter of the distribution
        lambda - lambda parameter of the distribution
      • W2

        public double W2()
        Get W2 statistic
        Returns:
        W2 Statistic
      • getA

        public double getA()
        Get A statistic
        Returns:
        A Statistic
      • getPValue

        public double getPValue()
        Get p-value of the test
        Returns:
        p-value computed

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