Package org.apache.commons.math.distribution

Class TDistributionImpl

java.lang.Object
org.apache.commons.math.distribution.AbstractDistribution
org.apache.commons.math.distribution.AbstractContinuousDistribution
org.apache.commons.math.distribution.TDistributionImpl
All Implemented Interfaces:
Serializable, ContinuousDistribution, Distribution, TDistribution
public class TDistributionImpl extends AbstractContinuousDistribution implements TDistribution, Serializable
Default implementation of TDistribution.
See Also:

  • Field Details

    • DEFAULT_INVERSE_ABSOLUTE_ACCURACY

      public static final double DEFAULT_INVERSE_ABSOLUTE_ACCURACY
      Default inverse cumulative probability accuracy
      Since:
      2.1
      See Also:

  • Constructor Details

    • TDistributionImpl

      public TDistributionImpl(double degreesOfFreedom, double inverseCumAccuracy)
      Create a t distribution using the given degrees of freedom and the specified inverse cumulative probability absolute accuracy.
      Parameters:
      degreesOfFreedom - the degrees of freedom.
      inverseCumAccuracy - the maximum absolute error in inverse cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY)
      Since:
      2.1

    • TDistributionImpl

      public TDistributionImpl(double degreesOfFreedom)
      Create a t distribution using the given degrees of freedom.
      Parameters:
      degreesOfFreedom - the degrees of freedom.

  • Method Details

    • setDegreesOfFreedom

      @Deprecated public void setDegreesOfFreedom(double degreesOfFreedom)
      Deprecated.
      as of 2.1 (class will become immutable in 3.0)
      Modify the degrees of freedom.
      Specified by:
      setDegreesOfFreedom in interface TDistribution
      Parameters:
      degreesOfFreedom - the new degrees of freedom.

    • getDegreesOfFreedom

      public double getDegreesOfFreedom()
      Access the degrees of freedom.
      Specified by:
      getDegreesOfFreedom in interface TDistribution
      Returns:
      the degrees of freedom.

    • density

      public double density(double x)
      Returns the probability density for a particular point.
      Overrides:
      density in class AbstractContinuousDistribution
      Parameters:
      x - The point at which the density should be computed.
      Returns:
      The pdf at point x.
      Since:
      2.1

    • cumulativeProbability

      public double cumulativeProbability(double x) throws MathException
      For this distribution, X, this method returns P(X < x).
      Specified by:
      cumulativeProbability in interface Distribution
      Parameters:
      x - the value at which the CDF is evaluated.
      Returns:
      CDF evaluated at x.
      Throws:
      MathException - if the cumulative probability can not be computed due to convergence or other numerical errors.

    • inverseCumulativeProbability

      public double inverseCumulativeProbability(double p) throws MathException
      For this distribution, X, this method returns the critical point x, such that P(X < x) = p.

      Returns Double.NEGATIVE_INFINITY for p=0 and Double.POSITIVE_INFINITY for p=1.

      Specified by:
      inverseCumulativeProbability in interface ContinuousDistribution
      Overrides:
      inverseCumulativeProbability in class AbstractContinuousDistribution
      Parameters:
      p - the desired probability
      Returns:
      x, such that P(X < x) = p
      Throws:
      MathException - if the inverse cumulative probability can not be computed due to convergence or other numerical errors.
      IllegalArgumentException - if p is not a valid probability.

    • getSupportLowerBound

      public double getSupportLowerBound()
      Returns the lower bound of the support for the distribution. The lower bound of the support is always negative infinity no matter the parameters.
      Returns:
      lower bound of the support (always Double.NEGATIVE_INFINITY)
      Since:
      2.2

    • getSupportUpperBound

      public double getSupportUpperBound()
      Returns the upper bound of the support for the distribution. The upper bound of the support is always positive infinity no matter the parameters.
      Returns:
      upper bound of the support (always Double.POSITIVE_INFINITY)
      Since:
      2.2

    • getNumericalMean

      public double getNumericalMean()
      Returns the mean. For degrees of freedom parameter df, the mean is
      • if df > 1 then 0
      • else undefined
      Returns:
      the mean
      Since:
      2.2

    • getNumericalVariance

      public double getNumericalVariance()
      Returns the variance. For degrees of freedom parameter df, the variance is
      • if df > 2 then df / (df - 2)
      • if 1 < df <= 2 then positive infinity
      • else undefined
      Returns:
      the variance
      Since:
      2.2